Sleep, Internalizing Problems, and Social Withdrawal: Unique Associations in Clinic-Referred Youth With Elevated Sluggish Cognitive Tempo Symptoms.

PubMed

Rondon, Ana T; Hilton, Dane C; Jarrett, Matthew A; Ollendick, Thomas H

2018-02-01

We compared clinic-referred youth with ADHD + sluggish cognitive tempo (SCT; n = 34), ADHD Only ( n = 108), and SCT Only ( n = 22) on demographics, co-occurring symptomatology, comorbid diagnoses, and social functioning. In total, 164 youth (age = 6-17 years, M = 9.97) and their parent(s) presented to an outpatient clinic for a psychoeducational assessment. Between-group analyses and regressions were used to examine study variables. SCT groups were older and exhibited more parent-reported internalizing problems, externalizing problems, sleep problems, and social withdrawal on the Child Behavior Checklist. No significant differences emerged between groups on the Teacher Report Form. Regression analyses involving multiple covariates revealed that SCT symptoms were uniquely related to social withdrawal but not general social problems. Based on parent report, SCT symptoms have a unique relationship with internalizing problems, sleep problems, and social withdrawal. Future research should explore correlates of SCT in youth using multiple informants.

Predicting MHC-II binding affinity using multiple instance regression

PubMed Central

EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2011-01-01

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir. PMID:20855923

Emotion dysregulation, problem-solving, and hopelessness.

PubMed

Vatan, Sevginar; Lester, David; Gunn, John F

2014-04-01

A sample of 87 Turkish undergraduate students was administered scales to measure hopelessness, problem-solving skills, emotion dysregulation, and psychiatric symptoms. All of the scores from these scales were strongly associated. In a multiple regression, hopelessness scores were predicted by poor problem-solving skills and emotion dysregulation.

The effects of multiple interpersonal traumas on psychological maladjustment of sexually abused children in Korea.

PubMed

Choi, Ji Young; Oh, Kyung Ja

2013-02-01

The purpose of the present study was to explore the effects of multiple interpersonal traumas on psychiatric diagnosis and behavior problems of sexually abused children in Korea. With 495 children (ages 4-13 years) referred to a public counseling center for sexual abuse in Korea, we found significant differences in the rate of psychiatric diagnoses (r = .23) and severity of behavioral problems (internalizing d = 0.49, externalizing d = 0.40, total d = 0.52) between children who were victims of sexual abuse only (n = 362) and youth who were victims of interpersonal trauma experiences in addition to sexual abuse (n = 133). The effects of multiple interpersonal trauma experiences on single versus multiple diagnoses remained significant in the logistic regression analysis where demographic variables, family environmental factors, sexual abuse characteristics, and postincident factors were considered together, odds ratio (OR) = 0.44, 95% confidence interval (CI) = [0.25, 0.77], p < .01. Similarly, multiple regression analyses revealed a significant effect of multiple interpersonal trauma experiences on severity of behavioral problems above and beyond all aforementioned variables (internalizing β =.12, p = .019, externalizing β = .11, p = .036, total β = .14, p =.008). The results suggested that children with multiple interpersonal traumas are clearly at a greater risk for negative consequences following sexual abuse. Copyright © 2013 International Society for Traumatic Stress Studies.

Exposure to child abuse and risk for mental health problems in women.

PubMed

Schneider, Renee; Baumrind, Nikki; Kimerling, Rachel

2007-01-01

Risk for adult mental health problems associated with child sexual, physical, or emotional abuse and multiple types of child abuse was examined. Logistic regression analyses were used to test study hypotheses in a population-based sample of women (N = 3,936). As expected, child sexual, physical, and emotional abuse were independently associated with increased risk for mental health problems. History of multiple types of child abuse was also associated with elevated risk for mental health problems. In particular, exposure to all three types of child abuse was linked to a 23-fold increase in risk for probable posttraumatic stress disorder (PTSD). Findings underscore relations between child emotional abuse and adult mental health problems and highlight the need for mental health services for survivors of multiple types of child abuse.

Ethnic Identity as a Predictor of Problem Behaviors among Korean American Adolescents

ERIC Educational Resources Information Center

Shrake, Eunai K.; Rhee, Siyon

2004-01-01

This study examined three dimensions of ethnic identity (level of ethnic identity, attitudes toward other groups, and perceived discrimination) as predictors of adolescent problem behaviors among Korean American adolescents. Multiple regression analyses were carried out, and the results indicated that level of ethnic identity, perceived…

Association of TV watching with sleep problems in a church-going population.

PubMed

Serrano, Salim; Lee, Jerry W; Dehom, Salem; Tonstad, Serena

2014-01-01

Sensory stimuli/inactivity may affect sleep. Sleep problems are associated with multiple health problems. We assessed TV habits in the Adventist Health Study-2 at baseline and sleep problems in the Biopsychosocial Religion and Health Study 1 to 4 years later. After exclusions, 3914 subjects split equally into TV watchers less than 2 hours per day or 2 or more hours per day. Watching TV 2 or more hours per day predicted problems falling asleep, middle of the night awakening, and waking early with inability to sleep again in multiple logistic regression. Excess TV watching disturbed sleep induction and quality, though the relationship may be bidirectional. TV habits should be considered in individuals with sleep problems.

Predicting flight delay based on multiple linear regression

NASA Astrophysics Data System (ADS)

Ding, Yi

2017-08-01

Delay of flight has been regarded as one of the toughest difficulties in aviation control. How to establish an effective model to handle the delay prediction problem is a significant work. To solve the problem that the flight delay is difficult to predict, this study proposes a method to model the arriving flights and a multiple linear regression algorithm to predict delay, comparing with Naive-Bayes and C4.5 approach. Experiments based on a realistic dataset of domestic airports show that the accuracy of the proposed model approximates 80%, which is further improved than the Naive-Bayes and C4.5 approach approaches. The result testing shows that this method is convenient for calculation, and also can predict the flight delays effectively. It can provide decision basis for airport authorities.

Normalization Ridge Regression in Practice II: The Estimation of Multiple Feedback Linkages.

ERIC Educational Resources Information Center

Bulcock, J. W.

The use of the two-stage least squares (2 SLS) procedure for estimating nonrecursive social science models is often impractical when multiple feedback linkages are required. This is because 2 SLS is extremely sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is a biased, variance reduced procedure…

Touch Processing and Social Behavior in ASD

ERIC Educational Resources Information Center

Miguel, Helga O.; Sampaio, Adriana; Martínez-Regueiro, Rocío; Gómez-Guerrero, Lorena; López-Dóriga, Cristina Gutiérrez; Gómez, Sonia; Carracedo, Ángel; Fernández-Prieto, Montse

2017-01-01

Abnormal patterns of touch processing have been linked to core symptoms in ASD. This study examined the relation between tactile processing patterns and social problems in 44 children and adolescents with ASD, aged 6-14 (M = 8.39 ± 2.35). Multiple linear regression indicated significant associations between touch processing and social problems. No…

The Impact of Problem Sets on Student Learning

ERIC Educational Resources Information Center

Kim, Myeong Hwan; Cho, Moon-Heum; Leonard, Karen Moustafa

2012-01-01

The authors examined the role of problem sets on student learning in university microeconomics. A total of 126 students participated in the study in consecutive years. independent samples t test showed that students who were not given answer keys outperformed students who were given answer keys. Multiple regression analysis showed that, along with…

Data Mining Methods Applied to Flight Operations Quality Assurance Data: A Comparison to Standard Statistical Methods

NASA Technical Reports Server (NTRS)

Stolzer, Alan J.; Halford, Carl

2007-01-01

In a previous study, multiple regression techniques were applied to Flight Operations Quality Assurance-derived data to develop parsimonious model(s) for fuel consumption on the Boeing 757 airplane. The present study examined several data mining algorithms, including neural networks, on the fuel consumption problem and compared them to the multiple regression results obtained earlier. Using regression methods, parsimonious models were obtained that explained approximately 85% of the variation in fuel flow. In general data mining methods were more effective in predicting fuel consumption. Classification and Regression Tree methods reported correlation coefficients of .91 to .92, and General Linear Models and Multilayer Perceptron neural networks reported correlation coefficients of about .99. These data mining models show great promise for use in further examining large FOQA databases for operational and safety improvements.

Drug use, mental health and problems related to crime and violence: cross-sectional study1

PubMed Central

Claro, Heloísa Garcia; de Oliveira, Márcia Aparecida Ferreira; Bourdreaux, Janet Titus; Fernandes, Ivan Filipe de Almeida Lopes; Pinho, Paula Hayasi; Tarifa, Rosana Ribeiro

2015-01-01

Objective: to investigate the correlation between disorders related to the use of alcohol and other drugs and symptoms of mental disorders, problems related to crime and violence and to age and gender. Methods: cross-sectional descriptive study carried out with 128 users of a Psychosocial Care Center for Alcohol and other Drugs, in the city of São Paulo, interviewed by means of the instrument entitled Global Appraisal of Individual Needs - Short Screener. Univariate and multiple linear regression models were used to verify the correlation between the variables. Results: using univariate regression models, internalizing and externalizing symptoms and problems related to crime/violence proved significant and were included in the multiple model, in which only the internalizing symptoms and problems related to crime and violence remained significant. Conclusions: there is a correlation between the severity of problems related to alcohol use and severity of mental health symptoms and crime and violence in the study sample. The results emphasize the need for an interdisciplinary and intersectional character of attention to users of alcohol and other drugs, since they live in a socially vulnerable environment. PMID:26626010

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

DYNA3D/ParaDyn Regression Test Suite Inventory

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

Lin, Jerry I.

2016-09-01

The following table constitutes an initial assessment of feature coverage across the regression test suite used for DYNA3D and ParaDyn. It documents the regression test suite at the time of preliminary release 16.1 in September 2016. The columns of the table represent groupings of functionalities, e.g., material models. Each problem in the test suite is represented by a row in the table. All features exercised by the problem are denoted by a check mark (√) in the corresponding column. The definition of “feature” has not been subdivided to its smallest unit of user input, e.g., algorithmic parameters specific to amore » particular type of contact surface. This represents a judgment to provide code developers and users a reasonable impression of feature coverage without expanding the width of the table by several multiples. All regression testing is run in parallel, typically with eight processors, except problems involving features only available in serial mode. Many are strictly regression tests acting as a check that the codes continue to produce adequately repeatable results as development unfolds; compilers change and platforms are replaced. A subset of the tests represents true verification problems that have been checked against analytical or other benchmark solutions. Users are welcomed to submit documented problems for inclusion in the test suite, especially if they are heavily exercising, and dependent upon, features that are currently underrepresented.« less

Monitoring heavy metal Cr in soil based on hyperspectral data using regression analysis

NASA Astrophysics Data System (ADS)

Zhang, Ningyu; Xu, Fuyun; Zhuang, Shidong; He, Changwei

2016-10-01

Heavy metal pollution in soils is one of the most critical problems in the global ecology and environment safety nowadays. Hyperspectral remote sensing and its application is capable of high speed, low cost, less risk and less damage, and provides a good method for detecting heavy metals in soil. This paper proposed a new idea of applying regression analysis of stepwise multiple regression between the spectral data and monitoring the amount of heavy metal Cr by sample points in soil for environmental protection. In the measurement, a FieldSpec HandHeld spectroradiometer is used to collect reflectance spectra of sample points over the wavelength range of 325-1075 nm. Then the spectral data measured by the spectroradiometer is preprocessed to reduced the influence of the external factors, and the preprocessed methods include first-order differential equation, second-order differential equation and continuum removal method. The algorithms of stepwise multiple regression are established accordingly, and the accuracy of each equation is tested. The results showed that the accuracy of first-order differential equation works best, which makes it feasible to predict the content of heavy metal Cr by using stepwise multiple regression.

Associations between overweight, peer problems, and mental health in 12-13-year-old Norwegian children.

PubMed

Hestetun, Ingebjørg; Svendsen, Martin Veel; Oellingrath, Inger Margaret

2015-03-01

Overweight and mental health problems represent two major challenges related to child and adolescent health. More knowledge of a possible relationship between the two problems and the influence of peer problems on the mental health of overweight children is needed. It has previously been hypothesized that peer problems may be an underlying factor in the association between overweight and mental health problems. The purpose of the present study was to investigate the associations between overweight, peer problems, and indications of mental health problems in a sample of 12-13-year-old Norwegian schoolchildren. Children aged 12-13 years were recruited from the seventh grade of primary schools in Telemark County, Norway. Parents gave information about mental health and peer problems by completing the extended version of the Strength and Difficulties Questionnaire (SDQ). Height and weight were objectively measured. Complete data were obtained for 744 children. Fisher's exact probability test and multiple logistic regressions were used. Most children had normal good mental health. Multiple logistic regression analysis showed that overweight children were more likely to have indications of psychiatric disorders (adjusted OR: 1.8, CI: 1.0-3.2) and peer problems (adjusted OR: 2.6, CI: 1.6-4.2) than normal-weight children, when adjusted for relevant background variables. When adjusted for peer problems, the association between overweight and indications of any psychiatric disorder was no longer significant. The results support the hypothesis that peer problems may be an important underlying factor for mental health problems in overweight children.

Maximum margin multiple instance clustering with applications to image and text clustering.

PubMed

Zhang, Dan; Wang, Fei; Si, Luo; Li, Tao

2011-05-01

In multiple instance learning problems, patterns are often given as bags and each bag consists of some instances. Most of existing research in the area focuses on multiple instance classification and multiple instance regression, while very limited work has been conducted for multiple instance clustering (MIC). This paper formulates a novel framework, maximum margin multiple instance clustering (M(3)IC), for MIC. However, it is impractical to directly solve the optimization problem of M(3)IC. Therefore, M(3)IC is relaxed in this paper to enable an efficient optimization solution with a combination of the constrained concave-convex procedure and the cutting plane method. Furthermore, this paper presents some important properties of the proposed method and discusses the relationship between the proposed method and some other related ones. An extensive set of empirical results are shown to demonstrate the advantages of the proposed method against existing research for both effectiveness and efficiency.

Influence of Child Behavioral Problems and Parenting Stress on Parent-Child Conflict among Low-Income Families: The Moderating Role of Maternal Nativity

ERIC Educational Resources Information Center

Garcia, Aileen S.; Ren, Lixin; Esteraich, Jan M.; Raikes, Helen H.

2017-01-01

This study was designed to examine whether parenting stress and child behavioral problems are significant predictors of parent-child conflict in the context of low-income families and how these relations are moderated by maternal nativity. The authors conducted multiple regression analyses to examine relations between teachers' report of…

Model selection with multiple regression on distance matrices leads to incorrect inferences.

PubMed

Franckowiak, Ryan P; Panasci, Michael; Jarvis, Karl J; Acuña-Rodriguez, Ian S; Landguth, Erin L; Fortin, Marie-Josée; Wagner, Helene H

2017-01-01

In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.

Confounding Problems in Multifactor AOV When Using Several Organismic Variables of Limited Reliability

ERIC Educational Resources Information Center

Games, Paul A.

1975-01-01

A brief introduction is presented on how multiple regression and linear model techniques can handle data analysis situations that most educators and psychologists think of as appropriate for analysis of variance. (Author/BJG)

Construction of mathematical model for measuring material concentration by colorimetric method

NASA Astrophysics Data System (ADS)

Liu, Bing; Gao, Lingceng; Yu, Kairong; Tan, Xianghua

2018-06-01

This paper use the method of multiple linear regression to discuss the data of C problem of mathematical modeling in 2017. First, we have established a regression model for the concentration of 5 substances. But only the regression model of the substance concentration of urea in milk can pass through the significance test. The regression model established by the second sets of data can pass the significance test. But this model exists serious multicollinearity. We have improved the model by principal component analysis. The improved model is used to control the system so that it is possible to measure the concentration of material by direct colorimetric method.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION.

PubMed

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-06-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression.

STRONG ORACLE OPTIMALITY OF FOLDED CONCAVE PENALIZED ESTIMATION

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2014-01-01

Folded concave penalization methods have been shown to enjoy the strong oracle property for high-dimensional sparse estimation. However, a folded concave penalization problem usually has multiple local solutions and the oracle property is established only for one of the unknown local solutions. A challenging fundamental issue still remains that it is not clear whether the local optimum computed by a given optimization algorithm possesses those nice theoretical properties. To close this important theoretical gap in over a decade, we provide a unified theory to show explicitly how to obtain the oracle solution via the local linear approximation algorithm. For a folded concave penalized estimation problem, we show that as long as the problem is localizable and the oracle estimator is well behaved, we can obtain the oracle estimator by using the one-step local linear approximation. In addition, once the oracle estimator is obtained, the local linear approximation algorithm converges, namely it produces the same estimator in the next iteration. The general theory is demonstrated by using four classical sparse estimation problems, i.e., sparse linear regression, sparse logistic regression, sparse precision matrix estimation and sparse quantile regression. PMID:25598560

Intersections of discrimination due to unemployment and mental health problems: the role of double stigma for job- and help-seeking behaviors.

PubMed

Staiger, Tobias; Waldmann, Tamara; Oexle, Nathalie; Wigand, Moritz; Rüsch, Nicolas

2018-05-21

The everyday lives of unemployed people with mental health problems can be affected by multiple discrimination, but studies about double stigma-an overlap of identities and experiences of discrimination-in this group are lacking. We therefore studied multiple discrimination among unemployed people with mental health problems and its consequences for job- and help-seeking behaviors. Everyday discrimination and attributions of discrimination to unemployment and/or to mental health problems were examined among 301 unemployed individuals with mental health problems. Job search self-efficacy, barriers to care, and perceived need for treatment were compared among four subgroups, depending on attributions of experienced discrimination to unemployment and to mental health problems (group i); neither to unemployment nor to mental health problems (group ii); mainly to unemployment (group iii); or mainly to mental health problems (group iv). In multiple regressions among all participants, higher levels of discrimination predicted reduced job search self-efficacy and higher barriers to care; and attributions of discrimination to unemployment were associated with increased barriers to care. In ANOVAs for subgroup comparisons, group i participants, who attributed discrimination to both unemployment and mental health problems, reported lower job search self-efficacy, more perceived stigma-related barriers to care and more need for treatment than group iii participants, as well as more stigma-related barriers to care than group iv. Multiple discrimination may affect job search and help-seeking among unemployed individuals with mental health problems. Interventions to reduce public stigma and to improve coping with multiple discrimination for this group should be developed.

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

Relationship of early-life stress and resilience to military adjustment in a young adulthood population.

PubMed

Choi, Kang; Im, Hyoungjune; Kim, Joohan; Choi, Kwang H; Jon, Duk-In; Hong, Hyunju; Hong, Narei; Lee, Eunjung; Seok, Jeong-Ho

2013-11-01

Early-life stress (ELS) may mediate adjustment problems while resilience may protect individuals against adjustment problems during military service. We investigated the relationship of ELS and resilience with adjustment problem factor scores in the Korea Military Personality Test (KMPT) in candidates for the military service. Four hundred and sixty-one candidates participated in this study. Vulnerability traits for military adjustment, ELS, and resilience were assessed using the KMPT, the Korean Early-Life Abuse Experience Questionnaire, and the Resilience Quotient Test, respectively. Data were analyzed using multiple linear regression analyses. The final model of the multiple linear regression analyses explained 30.2 % of the total variances of the sum of the adjustment problem factor scores of the KMPT. Neglect and exposure to domestic violence had a positive association with the total adjustment problem factor scores of the KMPT, but emotion control, impulse control, and optimism factor scores as well as education and occupational status were inversely associated with the total military adjustment problem score. ELS and resilience are important modulating factors in adjusting to military service. We suggest that neglect and exposure to domestic violence during early life may increase problem with adjustment, but capacity to control emotion and impulse as well as optimistic attitude may play protective roles in adjustment to military life. The screening procedures for ELS and the development of psychological interventions may be helpful for young adults to adjust to military service.

Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants.

PubMed

Pierce, Brandon L; Ahsan, Habibul; Vanderweele, Tyler J

2011-06-01

Mendelian Randomization (MR) studies assess the causality of an exposure-disease association using genetic determinants [i.e. instrumental variables (IVs)] of the exposure. Power and IV strength requirements for MR studies using multiple genetic variants have not been explored. We simulated cohort data sets consisting of a normally distributed disease trait, a normally distributed exposure, which affects this trait and a biallelic genetic variant that affects the exposure. We estimated power to detect an effect of exposure on disease for varying allele frequencies, effect sizes and samples sizes (using two-stage least squares regression on 10,000 data sets-Stage 1 is a regression of exposure on the variant. Stage 2 is a regression of disease on the fitted exposure). Similar analyses were conducted using multiple genetic variants (5, 10, 20) as independent or combined IVs. We assessed IV strength using the first-stage F statistic. Simulations of realistic scenarios indicate that MR studies will require large (n > 1000), often very large (n > 10,000), sample sizes. In many cases, so-called 'weak IV' problems arise when using multiple variants as independent IVs (even with as few as five), resulting in biased effect estimates. Combining genetic factors into fewer IVs results in modest power decreases, but alleviates weak IV problems. Ideal methods for combining genetic factors depend upon knowledge of the genetic architecture underlying the exposure. The feasibility of well-powered, unbiased MR studies will depend upon the amount of variance in the exposure that can be explained by known genetic factors and the 'strength' of the IV set derived from these genetic factors.

Attitude and practice of physical activity and social problem-solving ability among university students.

PubMed

Sone, Toshimasa; Kawachi, Yousuke; Abe, Chihiro; Otomo, Yuki; Sung, Yul-Wan; Ogawa, Seiji

2017-04-04

Effective social problem-solving abilities can contribute to decreased risk of poor mental health. In addition, physical activity has a favorable effect on mental health. These previous studies suggest that physical activity and social problem-solving ability can interact by helping to sustain mental health. The present study aimed to determine the association between attitude and practice of physical activity and social problem-solving ability among university students. Information on physical activity and social problem-solving was collected using a self-administered questionnaire. We analyzed data from 185 students who participated in the questionnaire surveys and psychological tests. Social problem-solving as measured by the Social Problem-Solving Inventory-Revised (SPSI-R) (median score 10.85) was the dependent variable. Multiple logistic regression analysis was employed to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for higher SPSI-R according to physical activity categories. The multiple logistic regression analysis indicated that the ORs (95% CI) in reference to participants who said they never considered exercising were 2.08 (0.69-6.93), 1.62 (0.55-5.26), 2.78 (0.86-9.77), and 6.23 (1.81-23.97) for participants who did not exercise but intended to start, tried to exercise but did not, exercised but not regularly, and exercised regularly, respectively. This finding suggested that positive linear association between physical activity and social problem-solving ability (p value for linear trend < 0.01). The present findings suggest that regular physical activity or intention to start physical activity may be an effective strategy to improve social problem-solving ability.

Using an innovative multiple regression procedure in a cancer population (Part II): fever, depressive affect, and mobility problems clarify an influential symptom pair (pain-fatigue/weakness) and cluster (pain-fatigue/weakness-sleep problems).

PubMed

Francoeur, Richard B

2015-01-01

Most patients with advanced cancer experience symptom pairs or clusters among pain, fatigue, and insomnia. However, only combinations where symptoms are mutually influential hold potential for identifying patient subgroups at greater risk, and in some contexts, interventions with "cross-over" (multisymptom) effects. Improved methods to detect and interpret interactions among symptoms, signs, or biomarkers are needed to reveal these influential pairs and clusters. I recently created sequential residual centering (SRC) to reduce multicollinearity in moderated regression, which enhances sensitivity to detect these interactions. I applied SRC to moderated regressions of single-item symptoms that interact to predict outcomes from 268 palliative radiation outpatients. I investigated: 1) the hypothesis that the interaction, pain × fatigue/weakness × sleep problems, predicts depressive affect only when fever presents, and 2) an exploratory analysis, when fever is absent, that the interaction, pain × fatigue/weakness × sleep problems × depressive affect, predicts mobility problems. In the fever context, three-way interactions (and derivative terms) of the four symptoms (pain, fatigue/weakness, fever, sleep problems) are tested individually and simultaneously; in the non-fever context, a single four-way interaction (and derivative terms) is tested. Fever interacts separately with fatigue/weakness and sleep problems; these comoderators each magnify the pain-depressive affect relationship along the upper or full range of pain values. In non-fever contexts, fatigue/weakness, sleep problems, and depressive affect comagnify the relationship between pain and mobility problems. Different mechanisms contribute to the pain × fatigue/weakness × sleep problems interaction, but all depend on the presence of fever, a sign/biomarker/symptom of proinflammatory sickness behavior. In non-fever contexts, depressive affect is no longer an outcome representing malaise from the physical symptoms of sickness, but becomes a fourth symptom of the interaction. In outpatient subgroups at heightened risk, single interventions could potentially relieve multiple symptoms when fever accompanies sickness malaise and in non-fever contexts with mobility problems. SRC strengthens insights into symptom pairs/clusters.

Regression techniques for oceanographic parameter retrieval using space-borne microwave radiometry

NASA Technical Reports Server (NTRS)

Hofer, R.; Njoku, E. G.

1981-01-01

Variations of conventional multiple regression techniques are applied to the problem of remote sensing of oceanographic parameters from space. The techniques are specifically adapted to the scanning multichannel microwave radiometer (SMRR) launched on the Seasat and Nimbus 7 satellites to determine ocean surface temperature, wind speed, and atmospheric water content. The retrievals are studied primarily from a theoretical viewpoint, to illustrate the retrieval error structure, the relative importances of different radiometer channels, and the tradeoffs between spatial resolution and retrieval accuracy. Comparisons between regressions using simulated and actual SMMR data are discussed; they show similar behavior.

Simple models for estimating local removals of timber in the northeast

Treesearch

David N. Larsen; David A. Gansner

1975-01-01

Provides a practical method of estimating subregional removals of timber and demonstrates its application to a typical problem. Stepwise multiple regression analysis is used to develop equations for estimating removals of softwood, hardwood, and all timber from selected characteristics of socioeconomic structure.

Overcoming multicollinearity in multiple regression using correlation coefficient

NASA Astrophysics Data System (ADS)

Zainodin, H. J.; Yap, S. J.

2013-09-01

Multicollinearity happens when there are high correlations among independent variables. In this case, it would be difficult to distinguish between the contributions of these independent variables to that of the dependent variable as they may compete to explain much of the similar variance. Besides, the problem of multicollinearity also violates the assumption of multiple regression: that there is no collinearity among the possible independent variables. Thus, an alternative approach is introduced in overcoming the multicollinearity problem in achieving a well represented model eventually. This approach is accomplished by removing the multicollinearity source variables on the basis of the correlation coefficient values based on full correlation matrix. Using the full correlation matrix can facilitate the implementation of Excel function in removing the multicollinearity source variables. It is found that this procedure is easier and time-saving especially when dealing with greater number of independent variables in a model and a large number of all possible models. Hence, in this paper detailed insight of the procedure is shown, compared and implemented.

Partial Least Squares with Structured Output for Modelling the Metabolomics Data Obtained from Complex Experimental Designs: A Study into the Y-Block Coding.

PubMed

Xu, Yun; Muhamadali, Howbeer; Sayqal, Ali; Dixon, Neil; Goodacre, Royston

2016-10-28

Partial least squares (PLS) is one of the most commonly used supervised modelling approaches for analysing multivariate metabolomics data. PLS is typically employed as either a regression model (PLS-R) or a classification model (PLS-DA). However, in metabolomics studies it is common to investigate multiple, potentially interacting, factors simultaneously following a specific experimental design. Such data often cannot be considered as a "pure" regression or a classification problem. Nevertheless, these data have often still been treated as a regression or classification problem and this could lead to ambiguous results. In this study, we investigated the feasibility of designing a hybrid target matrix Y that better reflects the experimental design than simple regression or binary class membership coding commonly used in PLS modelling. The new design of Y coding was based on the same principle used by structural modelling in machine learning techniques. Two real metabolomics datasets were used as examples to illustrate how the new Y coding can improve the interpretability of the PLS model compared to classic regression/classification coding.

Life stressors as mediators of the relation between socioeconomic position and mental health problems in early adolescence: the TRAILS study.

PubMed

Amone-P'Olak, Kennedy; Ormel, Johan; Huisman, Martijn; Verhulst, Frank C; Oldehinkel, Albertine J; Burger, Huibert

2009-10-01

Life stressors and family socioeconomic position have often been associated with mental health status. The aim of the present study is to contribute to the understanding of the pathways from low socioeconomic position and life stressors to mental problems. In a cross-sectional analysis using data from a longitudinal study of early adolescents (N = 2,149, 51% girls; mean age 13.6 years, SD 0.53, range 12-15), we assessed the extent of mediation of the association between family socioeconomic position and mental health problems by different types of life stressors in multiple regression models. Stressors were rated as environment related or person related. Information on socioeconomic position was obtained directly from parents, and internalizing and externalizing problem behaviors were assessed by reports from multiple informants (parents, self, and teachers). Low socioeconomic position was associated with more mental health problems and more life stressors. Both environment-related and person-related stressors predicted mental health problems independently of socioeconomic position. The associations between socioeconomic position and all mental health outcomes were partly mediated by environment-related life stressors. Mediation by environment-related and person-related stressors as assessed by linear regression amounted to 56% (95% confidence interval [CI] 35%-78%) and 7% (95% CI -25% to 38%) for internalizing problems and 13% (95% CI 7%-19%) and 5% (95% CI -2% to 13%) for externalizing problems, respectively. Environment-related, but not person-related, stressors partly mediated the association between socio economic position and adolescent mental problems. The extent of mediation was larger for internalizing than for externalizing problems. Because the effect sizes of the associations were relatively small, targeted interventions to prevent impaired mental health may have only modest benefits to adolescents from low socioeconomic background.

Solving a mixture of many random linear equations by tensor decomposition and alternating minimization.

DOT National Transportation Integrated Search

2016-09-01

We consider the problem of solving mixed random linear equations with k components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple linear models from mixed samples in the case where the labels (which sample...

Using Multilevel Modeling in Language Assessment Research: A Conceptual Introduction

ERIC Educational Resources Information Center

Barkaoui, Khaled

2013-01-01

This article critiques traditional single-level statistical approaches (e.g., multiple regression analysis) to examining relationships between language test scores and variables in the assessment setting. It highlights the conceptual, methodological, and statistical problems associated with these techniques in dealing with multilevel or nested…

Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin

EPA Science Inventory

Ecologists are often faced with problem of small sample size, correlated and large number of predictors, and high noise-to-signal relationships. This necessitates excluding important variables from the model when applying standard multiple or multivariate regression analyses. In ...

Maternal Risk Factors for Fetal Alcohol Spectrum Disorders in a Province in Italy*

PubMed Central

Ceccanti, Mauro; Fiorentino, Daniela; Coriale, Giovanna; Kalberg, Wendy O.; Buckley, David; Hoyme, H. Eugene; Gossage, J. Phillip; Robinson, Luther K.; Manning, Melanie; Romeo, Marina; Hasken, Julie M.; Tabachnick, Barbara; Blankenship, Jason

2016-01-01

Background Maternal risk factors for fetal alcohol spectrum disorders (FASD) in Italy and Mediterranean cultures need clarification, as there are few studies and most are plagued by inaccurate reporting of antenatal alcohol use. Methods Maternal interviews (n=905) were carried out in a population-based study of the prevalence and characteristics of FASD in the Lazio region of Italy which provided data for multivariate case control comparisons and multiple correlation models. Results Case control findings from interviews seven years post-partum indicate that mothers of children with FASD are significantly more likely than randomly-selected controls or community mothers to: be shorter; have higher body mass indexes (BMI); be married to a man with legal problems; report more drinking three months pre-pregnancy; engage in more current drinking and drinking alone; and have alcohol problems in her family. Logistic regression analysis of multiple candidate predictors of a FASD diagnosis indicates that alcohol problems in the child’s family is the most significant risk factor, making a diagnosis within the continuum of FASD 9 times more likely (95% C.I. = 1.6 to 50.7). Sequential multiple regression analysis of the child’s neuropsychological performance also identifies alcohol problems in the child’s family as the only significant maternal risk variable (p<.001) when controlling for other potential risk factors. Conclusions Underreporting of prenatal alcohol use has been demonstrated among Italian and other Mediterranean antenatal samples, and it was suspected in this sample. Nevertheless, several significant maternal risk factors for FASD have been identified. PMID:25456331

Maternal risk factors for fetal alcohol spectrum disorders in a province in Italy.

PubMed

Ceccanti, Mauro; Fiorentino, Daniela; Coriale, Giovanna; Kalberg, Wendy O; Buckley, David; Hoyme, H Eugene; Gossage, J Phillip; Robinson, Luther K; Manning, Melanie; Romeo, Marina; Hasken, Julie M; Tabachnick, Barbara; Blankenship, Jason; May, Philip A

2014-12-01

Maternal risk factors for fetal alcohol spectrum disorders (FASD) in Italy and Mediterranean cultures need clarification, as there are few studies and most are plagued by inaccurate reporting of antenatal alcohol use. Maternal interviews (n = 905) were carried out in a population-based study of the prevalence and characteristics of FASD in the Lazio region of Italy which provided data for multivariate case control comparisons and multiple correlation models. Case control findings from interviews seven years post-partum indicate that mothers of children with FASD are significantly more likely than randomly-selected controls or community mothers to: be shorter; have higher body mass indexes (BMI); be married to a man with legal problems; report more drinking three months pre-pregnancy; engage in more current drinking and drinking alone; and have alcohol problems in her family. Logistic regression analysis of multiple candidate predictors of a FASD diagnosis indicates that alcohol problems in the child's family is the most significant risk factor, making a diagnosis within the continuum of FASD 9 times more likely (95%C.I. = 1.6 to 50.7). Sequential multiple regression analysis of the child's neuropsychological performance also identifies alcohol problems in the child's family as the only significant maternal risk variable (p < .001) when controlling for other potential risk factors. Underreporting of prenatal alcohol use has been demonstrated among Italian and other Mediterranean antenatal samples, and it was suspected in this sample. Nevertheless, several significant maternal risk factors for FASD have been identified. Copyright © 2014. Published by Elsevier Ireland Ltd.

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

PubMed

Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

Acculturation Stress and Drinking Problems Among Urban Heavy Drinking Latinos in the Northeast

PubMed Central

Lee, Christina S.; Colby, Suzanne M.; Rohsenow, Damaris J.; López, Steven R.; Hernández, Lynn; Caetano, Raul

2014-01-01

This study investigates the relationship between level of acculturation and acculturation stress, and the extent to which each predicts problems related to drinking. Hispanics who met criteria for hazardous drinking completed measures of acculturation, acculturation stress, and drinking problems. Sequential multiple regression was used to determine whether levels of self-reported acculturation stress predicted concurrent alcohol problems after controlling for the predictive value of acculturation level. Acculturation stress accounted for significant variance in drinking problems while adjusting for acculturation, income, and education. Choosing to drink in response to acculturation stress should be an intervention target with Hispanic heavy drinkers. PMID:24215224

Acculturation stress and drinking problems among urban heavy drinking Latinos in the Northeast.

PubMed

Lee, Christina S; Colby, Suzanne M; Rohsenow, Damaris J; López, Steven R; Hernández, Lynn; Caetano, Raul

2013-01-01

This study investigates the relationship between the level of acculturation and acculturation stress and the extent to which each predicts problems related to drinking. Hispanics who met criteria for hazardous drinking completed measures of acculturation, acculturation stress, and drinking problems. Sequential multiple regression was used to determine whether the levels of self-reported acculturation stress predicted concurrent alcohol problems after controlling for the predictive value of the acculturation level. Acculturation stress accounted for a significant variance in drinking problems, while adjusting for acculturation, income, and education. Choosing to drink in response to acculturation stress should be an intervention target with Hispanic heavy drinkers.

The contribution of parenting practices and parent emotion factors in children at risk for disruptive behavior disorders.

PubMed

Duncombe, Melissa E; Havighurst, Sophie S; Holland, Kerry A; Frankling, Emma J

2012-10-01

The goal of this study was to examine the impact of different parenting characteristics on child disruptive behavior and emotional regulation among a sample of at-risk children. The sample consisted of 373 Australian 5- to 9-year-old children who were screened for serious behavior problems. Seven parenting variables based on self-report were evaluated, involving parenting practices, emotion beliefs and behaviors, emotion expressiveness, and mental health. Outcome variables based on parent/teacher report were child disruptive behavior problems and emotion regulatory ability. When entered simultaneously in a multiple regression analysis, inconsistent discipline, negative parental emotional expressiveness, and parent mental health demonstrated the strongest relationship to disruptive behavior problems and problems with emotion regulation. The data presented here elucidate multiple risk pathways to disruptive behavior disorders and can inform the design of prevention and early intervention programs.

Patterns of adverse childhood experiences and substance use among young adults: A latent class analysis.

PubMed

Shin, Sunny H; McDonald, Shelby Elaine; Conley, David

2018-03-01

Adverse childhood experiences (ACEs) have been strongly linked with subsequent substance use. The aim of this study was to investigate how different patterns of ACEs influence substance use in young adulthood. Using a community sample of young individuals (N=336; ages 18-25), we performed latent class analyses (LCA) to identify homogenous groups of young people with similar patterns of ACEs. Exposure to ACEs incorporates 13 childhood adversities including childhood maltreatment, household dysfunction, and community violence. Multiple linear and logistic regression models were used in an effort to examine the associations between ACEs classes and four young adult outcomes such as alcohol-related problems, current tobacco use, drug dependence symptoms, and psychological distress. LCA identified four heterogeneous classes of young people distinguished by different patterns of ACEs exposure: Low ACEs (56%), Household Dysfunction/Community Violence (14%), Emotional ACEs (14%), and High/Multiple ACEs (16%). Multiple regression analyses found that compared to those in the Low ACEs class, young adults in the High/Multiple ACEs class reported more alcohol-related problems, current tobacco use, and psychological symptoms, controlling for sociodemographic characteristics and common risk factors for substance use such as peer substance use. Our findings confirm that for many young people, ACEs occur as multiple rather than single experiences. The results of this research suggest that exposure to poly-victimization during childhood is particularly related to substance use during young adulthood. Copyright © 2017 Elsevier Ltd. All rights reserved.

Acculturation, Internalizing Mental Health Symptoms, and Self-Esteem: Cultural Experiences of Latino Adolescents in North Carolina

ERIC Educational Resources Information Center

Smokowski, Paul R.; Bacallao, Martica L.

2007-01-01

This investigation examined acculturation risk factors and cultural assets, internalizing behavioral problems, and self-esteem in 323 Latino adolescents living in North Carolina. Multiple regression analyses revealed two risk factors--perceived discrimination and parent-adolescent conflict--as highly significant predictors of adolescent…

An Examination of the Roles of Rationalization and Narcissism in Facilitating Academic Dishonesty

ERIC Educational Resources Information Center

Faulkner, Karen

2012-01-01

Academic dishonesty is a significant problem among college students. Numerous factors affect levels of cheating. This study utilized an original survey on cheating and rationalization along with the Narcissistic Personality Inventory and multiple regression analysis to examine the relationships between rationalization, narcissism, and academic…

On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms

DTIC Science & Technology

1976-08-01

a I (3.4) Hannan and Terrell (1972) consider problems of a similar nature. Efficient estimates A(1),... , A(p) , and i of A(1)... ,A(p) and...34Autoregressive model fitting for control, Ann . Inst. Statist. Math., 23, 163-180. Hannan, E. J. (1970), Multiple Time Series, New York, John Wiley...Hannan, E. J. and Terrell , R. D. (1972), "Time series regression with linear constraints, " International Economic Review, 13, 189-200. Masani, P

Investigation of marital satisfaction and its relationship with job stress and general health of nurses in Qazvin, Iran.

PubMed

Azimian, Jalil; Piran, Pegah; Jahanihashemi, Hassan; Dehghankar, Leila

2017-04-01

Pressures in nursing can affect family life and marital problems, disrupt common social problems, increase work-family conflicts and endanger people's general health. To determine marital satisfaction and its relationship with job stress and general health of nurses. This descriptive and cross-sectional study was done in 2015 in medical educational centers of Qazvin by using an ENRICH marital satisfaction scale and General Health and Job Stress questionnaires completed by 123 nurses. Analysis was done by SPSS version 19 using descriptive and analytical statistics (Pearson correlation, t-test, ANOVA, Chi-square, regression line, multiple regression analysis). The findings showed that 64.4% of nurses had marital satisfaction. There was significant relationship between age (p=0.03), job experience (p=0.01), age of spouse (p=0.01) and marital satisfaction. The results showed that there was a significant relationship between marital satisfaction and general health (p<0.0001). Multiple regression analysis showed that there was a significant relationship between depression (p=0.012) and anxiety (p=0.001) with marital satisfaction. Due to high levels of job stress and disorder in general health of nurses and low marital satisfaction by running health promotion programs and paying attention to its dimensions can help work and family health of nurses.

Bayesian LASSO, scale space and decision making in association genetics.

PubMed

Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J

2015-01-01

LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

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

PubMed

Zamboni, Brian D; Crawford, Isiaah

2007-08-01

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

Predictors of hopelessness among clinically depressed youth.

PubMed

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

2009-05-01

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

Exact Interval Estimation, Power Calculation, and Sample Size Determination in Normal Correlation Analysis

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…

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

ERIC Educational Resources Information Center

Shieh, Gwowen

2010-01-01

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

The Roles of Attitudinal and Personality Variables in the Prediction of Environmental Behavior and Knowledge

ERIC Educational Resources Information Center

Arbuthnot, Jack

1977-01-01

This study explored the relationships among selected attitudinal and personality characteristics, attitudes toward environmental problems, and environmental knowledge and behavioral commitment of two diverse samples: 85 users of a recycling center and 60 conservative church members. Multiple regression analysis was utilized to determine the best…

Emotional autonomy and problem behavior among Chinese adolescents.

PubMed

Chou, Kee-Lee

2003-12-01

The author examined the association between emotional autonomy and problem behavior among Chinese adolescents living in Hong Kong. The respondents were 512 adolescents, 16 to 18 years of age, who were interviewed for a cross-sectional study. Three dimensions of emotional autonomy including individuation, nondependency on parents, and de-idealization of parents were significantly and positively correlated with the amount of problem behavior the participants engaged in during the past 6 months. Using a simple linear multiple regression model, the author found that problem behavior was associated with only one aspect of emotional autonomy-individuation. Results indicated that the relationship between problem behavior and three aspects of emotional autonomy was similar in both individualistic and collectivistic societies.

Prospective relations between family conflict and adolescent maladjustment: security in the family system as a mediating process.

PubMed

Cummings, E Mark; Koss, Kalsea J; Davies, Patrick T

2015-04-01

Conflict in specific family systems (e.g., interparental, parent-child) has been implicated in the development of a host of adjustment problems in adolescence, but little is known about the impact of family conflict involving multiple family systems. Furthermore, questions remain about the effects of family conflict on symptoms of specific disorders and adjustment problems and the processes mediating these effects. The present study prospectively examines the impact of family conflict and emotional security about the family system on adolescent symptoms of specific disorders and adjustment problems, including the development of symptoms of anxiety, depression, conduct problems, and peer problems. Security in the family system was examined as a mediator of these relations. Participants included 295 mother-father-adolescent families (149 girls) participating across three annual time points (grades 7-9). Including auto-regressive controls for initial levels of emotional insecurity and multiple adjustment problems (T1), higher-order emotional insecurity about the family system (T2) mediated relations between T1 family conflict and T3 peer problems, anxiety, and depressive symptoms. Further analyses supported specific patterns of emotional security/insecurity (i.e., security, disengagement, preoccupation) as mediators between family conflict and specific domains of adolescent adjustment. Family conflict was thus found to prospectively predict the development of symptoms of multiple specific adjustment problems, including symptoms of depression, anxiety, conduct problems, and peer problems, by elevating in in adolescent's emotional insecurity about the family system. The clinical implications of these findings are considered.

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

PubMed

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

2012-03-01

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

Learning accurate and interpretable models based on regularized random forests regression

PubMed Central

2014-01-01

Background Many biology related research works combine data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. Methods In this study, we focus on regression problems for biological data where target outcomes are continuous. In general, models constructed from linear regression approaches are relatively easy to interpret. However, many practical biological applications are nonlinear in essence where we can hardly find a direct linear relationship between input and output. Nonlinear regression techniques can reveal nonlinear relationship of data, but are generally hard for human to interpret. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features. Results We tested the approach on some biological data sets. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of random forests regression. Conclusion It demonstrates high potential in aiding prediction and interpretation of nonlinear relationships of the subject being studied. PMID:25350120

Simple linear and multivariate regression models.

PubMed

Rodríguez del Águila, M M; Benítez-Parejo, N

2011-01-01

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

Investigation of marital satisfaction and its relationship with job stress and general health of nurses in Qazvin, Iran

PubMed Central

Azimian, Jalil; Piran, Pegah; Jahanihashemi, Hassan; Dehghankar, Leila

2017-01-01

Background Pressures in nursing can affect family life and marital problems, disrupt common social problems, increase work-family conflicts and endanger people’s general health. Aim To determine marital satisfaction and its relationship with job stress and general health of nurses. Methods This descriptive and cross-sectional study was done in 2015 in medical educational centers of Qazvin by using an ENRICH marital satisfaction scale and General Health and Job Stress questionnaires completed by 123 nurses. Analysis was done by SPSS version 19 using descriptive and analytical statistics (Pearson correlation, t-test, ANOVA, Chi-square, regression line, multiple regression analysis). Results The findings showed that 64.4% of nurses had marital satisfaction. There was significant relationship between age (p=0.03), job experience (p=0.01), age of spouse (p=0.01) and marital satisfaction. The results showed that there was a significant relationship between marital satisfaction and general health (p<0.0001). Multiple regression analysis showed that there was a significant relationship between depression (p=0.012) and anxiety (p=0.001) with marital satisfaction. Conclusions Due to high levels of job stress and disorder in general health of nurses and low marital satisfaction by running health promotion programs and paying attention to its dimensions can help work and family health of nurses. PMID:28607660

Adults' strategies for simple addition and multiplication: verbal self-reports and the operand recognition paradigm.

PubMed

Metcalfe, Arron W S; Campbell, Jamie I D

2011-05-01

Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.

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

PubMed

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

1999-09-01

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

A Powerful Test for Comparing Multiple Regression Functions.

PubMed

Maity, Arnab

2012-09-01

In this article, we address the important problem of comparison of two or more population regression functions. Recently, Pardo-Fernández, Van Keilegom and González-Manteiga (2007) developed test statistics for simple nonparametric regression models: Y(ij) = θ(j)(Z(ij)) + σ(j)(Z(ij))∊(ij), based on empirical distributions of the errors in each population j = 1, … , J. In this paper, we propose a test for equality of the θ(j)(·) based on the concept of generalized likelihood ratio type statistics. We also generalize our test for other nonparametric regression setups, e.g, nonparametric logistic regression, where the loglikelihood for population j is any general smooth function [Formula: see text]. We describe a resampling procedure to obtain the critical values of the test. In addition, we present a simulation study to evaluate the performance of the proposed test and compare our results to those in Pardo-Fernández et al. (2007).

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

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

Fundamental Analysis of the Linear Multiple Regression Technique for Quantification of Water Quality Parameters from Remote Sensing Data. Ph.D. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Whitlock, C. H., III

1977-01-01

Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

Touch Processing and Social Behavior in ASD.

PubMed

O Miguel, Helga; Sampaio, Adriana; Martínez-Regueiro, Rocío; Gómez-Guerrero, Lorena; López-Dóriga, Cristina Gutiérrez; Gómez, Sonia; Carracedo, Ángel; Fernández-Prieto, Montse

2017-08-01

Abnormal patterns of touch processing have been linked to core symptoms in ASD. This study examined the relation between tactile processing patterns and social problems in 44 children and adolescents with ASD, aged 6-14 (M = 8.39 ± 2.35). Multiple linear regression indicated significant associations between touch processing and social problems. No such relationships were found for social problems and autism severity. Within touch processing, patterns of hyper-responsiveness and hypo-responsiveness best predicted social problems, whereas sensory-seeking did not. These results support that atypical touch processing in individuals with ASD might be contributing to the social problems they present. Moreover, it the need to explore more in depth the contribution of sensory features to the ASD phenotype.

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

USGS Publications Warehouse

Ohlmacher, G.C.; Davis, J.C.

2003-01-01

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. ?? 2003 Elsevier Science B.V. All rights reserved.

Multiple regression technique for Pth degree polynominals with and without linear cross products

NASA Technical Reports Server (NTRS)

Davis, J. W.

1973-01-01

A multiple regression technique was developed by which the nonlinear behavior of specified independent variables can be related to a given dependent variable. The polynomial expression can be of Pth degree and can incorporate N independent variables. Two cases are treated such that mathematical models can be studied both with and without linear cross products. The resulting surface fits can be used to summarize trends for a given phenomenon and provide a mathematical relationship for subsequent analysis. To implement this technique, separate computer programs were developed for the case without linear cross products and for the case incorporating such cross products which evaluate the various constants in the model regression equation. In addition, the significance of the estimated regression equation is considered and the standard deviation, the F statistic, the maximum absolute percent error, and the average of the absolute values of the percent of error evaluated. The computer programs and their manner of utilization are described. Sample problems are included to illustrate the use and capability of the technique which show the output formats and typical plots comparing computer results to each set of input data.

Bias-motivated bullying and psychosocial problems: implications for HIV risk behaviors among young men who have sex with men.

PubMed

Li, Michael Jonathan; Distefano, Anthony; Mouttapa, Michele; Gill, Jasmeet K

2014-02-01

The present study aimed to determine whether the experience of bias-motivated bullying was associated with behaviors known to increase the risk of HIV infection among young men who have sex with men (YMSM) aged 18-29, and to assess whether the psychosocial problems moderated this relationship. Using an Internet-based direct marketing approach in sampling, we recruited 545 YMSM residing in the USA to complete an online questionnaire. Multiple linear regression analyses tested three regression models where we controlled for sociodemographics. The first model indicated that bullying during high school was associated with unprotected receptive anal intercourse within the past 12 months, while the second model indicated that bullying after high school was associated with engaging in anal intercourse while under the influence of drugs or alcohol in the past 12 months. In the final regression model, our composite measure of HIV risk behavior was found to be associated with lifetime verbal harassment. None of the psychosocial problems measured in this study - depression, low self-esteem, and internalized homonegativity - moderated any of the associations between bias-motivated bullying victimization and HIV risk behaviors in our regression models. Still, these findings provide novel evidence that bullying prevention programs in schools and communities should be included in comprehensive approaches to HIV prevention among YMSM.

The Utility of a Syndemic Framework in Understanding Chronic Disease Management Among HIV-Infected and Type 2 Diabetic Men Who Have Sex with Men.

PubMed

Byg, Blaire; Bazzi, Angela Robertson; Funk, Danielle; James, Bonface; Potter, Jennifer

2016-12-01

Syndemic theory posits that epidemics of multiple physical and psychosocial problems co-occur among disadvantaged groups due to adverse social conditions. Although sexual minority populations are often stigmatized and vulnerable to multiple health problems, the syndemic perspective has been underutilized in understanding chronic disease. To assess the potential utility of this perspective in understanding the management of co-occurring HIV and Type 2 diabetes, we used linear regression to examine glycemic control (A1c) among men who have sex with men (MSM) with both HIV and Type 2 diabetes (n = 88). Bivariable linear regression explored potential syndemic correlates of inadequate glycemic control. Compared to those with adequate glycemic control (A1c ≤ 7.5 %), more men with inadequate glycemic control (A1c > 7.5 %) had hypertension (70 vs. 46 %, p = 0.034), high triglycerides (93 vs. 61 %, p = 0.002), depression (67 vs. 39 %, p = 0.018), current substance abuse (15 vs. 2 %, p = 0.014), and detectable levels of HIV (i.e., viral load ≥75 copies per ml blood; 30 vs. 10 %, p = 0.019). In multivariable regression controlling for age, the factors that were independently associated with higher A1c were high triglycerides, substance use, and detectable HIV viral load, suggesting that chronic disease management among MSM is complex and challenging for patients and providers. Findings also suggest that syndemic theory can be a clarifying lens for understanding chronic disease management among sexual minority stigmatized populations. Interventions targeting single conditions may be inadequate when multiple conditions co-occur; thus, research using a syndemic framework may be helpful in identifying intervention strategies that target multiple co-occurring conditions.

Prospective Relations between Family Conflict and Adolescent Maladjustment: Security in the Family System as a Mediating Process

PubMed Central

Cummings, E. Mark; Koss, Kalsea J.; Davies, Patrick T.

2018-01-01

Conflict in specific family systems (e.g., interparental, parent-child) has been implicated in the development of a host of adjustment problems in adolescence, but little is known about the impact of family conflict involving multiple family systems. Furthermore, questions remain about the effects of family conflict on symptoms of specific disorders and adjustment problems and the processes mediating these effects. The present study prospectively examines the impact of family conflict and emotional security about the family system on adolescent symptoms of specific disorders and adjustment problems, including the development of symptoms of anxiety, depression, conduct problems, and peer problems. Security in the family system was examined as a mediator of these relations. Participants included 295 mother-father-adolescent families (149 girls) participating across three annual time points (grades 7–9). Including auto-regressive controls for initial levels of emotional insecurity and multiple adjustment problems (T1), higher-order emotional insecurity about the family system (T2) mediated relations between T1 family conflict and T3 peer problems, anxiety, and depressive symptoms. Further analyses supported specific patterns of emotional security/insecurity (i.e., security, disengagement, preoccupation) as mediators between family conflict and specific domains of adolescent adjustment. Family conflict was thus found to prospectively predict the development of symptoms of multiple specific adjustment problems, including symptoms of depression, anxiety, conduct problems, and peer problems, by elevating in in adolescent’s emotional insecurity about the family system. The clinical implications of these findings are considered. PMID:25131144

Penalized regression procedures for variable selection in the potential outcomes framework

PubMed Central

Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L.

2015-01-01

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple ‘impute, then select’ class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems, and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data and imputation are drawn. A difference LASSO algorithm is defined, along with its multiple imputation analogues. The procedures are illustrated using a well-known right heart catheterization dataset. PMID:25628185

Regression modeling of ground-water flow

USGS Publications Warehouse

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

[Life satisfaction and related socio-demographic factors during female midlife].

PubMed

Cuadros, José Luis; Pérez-Roncero, Gonzalo R; López-Baena, María Teresa; Cuadros-Celorrio, Angela M; Fernández-Alonso, Ana María

2014-01-01

To assess life satisfaction and related factors in middle-aged Spanish women. This was a cross-sectional study including 235 women aged 40 to 65, living in Granada (Spain), healthy companions of patients visiting the obstetrics and gynecology clinics. They completed the Diener Satisfaction with Life Scale, the Menopause Rating Scale, the Perceived Stress Scale, the Insomnia Severity Index and a sociodemographic questionnaire containing personal and partner data. Internal consistency of each tool was also calculated. Almost two-thirds (61.3%) of the women were postmenopausal, and 43.8% had abdominal obesity, 36.6% had insomnia, 18.7% had poor menopause-related quality of life, 31.9% performed regular exercise, and 5.1% had severe financial problems. Life satisfaction showed significant positive correlations (Spearman's test) with female and male age, and inverse correlations with menopause-related quality of life, perceived stress and insomnia. In the multiple linear regression analysis, high life satisfaction is positively correlated with having a partner who performed exercise, and inversely with having work problems, perceived stress and the suspicion of partner infidelity. These factors explained 40% of the variance of the multiple regression analysis for life satisfaction in middle-aged women. Life satisfaction is a construct related to perceived stress, work problems, and having a partner, while aspects of menopause and general health had no significant influence. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Deep ensemble learning of sparse regression models for brain disease diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Deep ensemble learning of sparse regression models for brain disease diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2018-01-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘ Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. PMID:28167394

Probabilistic Low-Rank Multitask Learning.

PubMed

Kong, Yu; Shao, Ming; Li, Kang; Fu, Yun

2018-03-01

In this paper, we consider the problem of learning multiple related tasks simultaneously with the goal of improving the generalization performance of individual tasks. The key challenge is to effectively exploit the shared information across multiple tasks as well as preserve the discriminative information for each individual task. To address this, we propose a novel probabilistic model for multitask learning (MTL) that can automatically balance between low-rank and sparsity constraints. The former assumes a low-rank structure of the underlying predictive hypothesis space to explicitly capture the relationship of different tasks and the latter learns the incoherent sparse patterns private to each task. We derive and perform inference via variational Bayesian methods. Experimental results on both regression and classification tasks on real-world applications demonstrate the effectiveness of the proposed method in dealing with the MTL problems.

Associations between Resilience and the Well-Being of Mothers of Children with Autism Spectrum Disorder and Other Developmental Disabilities

ERIC Educational Resources Information Center

Halstead, Elizabeth; Ekas, Naomi; Hastings, Richard P.; Griffith, Gemma M.

2018-01-01

There is variability in the extent to which mothers are affected by the behavior problems of their children with developmental disabilities (DD). We explore whether maternal resilience functions as a protective or compensatory factor. In Studies 1 and 2, using moderated multiple regression models, we found evidence that maternal resilience…

Linking the Components of a University Program to the Qualification Profile of Graduates: The Case of a Sustainability-Oriented Environmental Science Curriculum

ERIC Educational Resources Information Center

Hansmann, Ralf

2009-01-01

A university Environmental Sciences curriculum is described against the background of requirements for environmental problem solving for sustainability and then analyzed using data from regular surveys of graduates (N = 373). Three types of multiple regression models examine links between qualifications and curriculum components in order to derive…

Use of Case History Data for the Development of Equations in Predicting High Risk, Reading Disabled Students.

ERIC Educational Resources Information Center

Stratton, Beverly D.; And Others

Demographic data on 92 subjects identified as having reading problems were used to develop equations useful in identifying high risk, reading disabled students. Multiple linear regression analysis of the data indicated that reading disability (1) had a significant positive relationship with birth order and number of siblings; (2) had a positive…

Principal Covariates Clusterwise Regression (PCCR): Accounting for Multicollinearity and Population Heterogeneity in Hierarchically Organized Data.

PubMed

Wilderjans, Tom Frans; Vande Gaer, Eva; Kiers, Henk A L; Van Mechelen, Iven; Ceulemans, Eva

2017-03-01

In the behavioral sciences, many research questions pertain to a regression problem in that one wants to predict a criterion on the basis of a number of predictors. Although in many cases, ordinary least squares regression will suffice, sometimes the prediction problem is more challenging, for three reasons: first, multiple highly collinear predictors can be available, making it difficult to grasp their mutual relations as well as their relations to the criterion. In that case, it may be very useful to reduce the predictors to a few summary variables, on which one regresses the criterion and which at the same time yields insight into the predictor structure. Second, the population under study may consist of a few unknown subgroups that are characterized by different regression models. Third, the obtained data are often hierarchically structured, with for instance, observations being nested into persons or participants within groups or countries. Although some methods have been developed that partially meet these challenges (i.e., principal covariates regression (PCovR), clusterwise regression (CR), and structural equation models), none of these methods adequately deals with all of them simultaneously. To fill this gap, we propose the principal covariates clusterwise regression (PCCR) method, which combines the key idea's behind PCovR (de Jong & Kiers in Chemom Intell Lab Syst 14(1-3):155-164, 1992) and CR (Späth in Computing 22(4):367-373, 1979). The PCCR method is validated by means of a simulation study and by applying it to cross-cultural data regarding satisfaction with life.

Multiple concurrent recursive least squares identification with application to on-line spacecraft mass-property identification

NASA Technical Reports Server (NTRS)

Wilson, Edward (Inventor)

2006-01-01

The present invention is a method for identifying unknown parameters in a system having a set of governing equations describing its behavior that cannot be put into regression form with the unknown parameters linearly represented. In this method, the vector of unknown parameters is segmented into a plurality of groups where each individual group of unknown parameters may be isolated linearly by manipulation of said equations. Multiple concurrent and independent recursive least squares identification of each said group run, treating other unknown parameters appearing in their regression equation as if they were known perfectly, with said values provided by recursive least squares estimation from the other groups, thereby enabling the use of fast, compact, efficient linear algorithms to solve problems that would otherwise require nonlinear solution approaches. This invention is presented with application to identification of mass and thruster properties for a thruster-controlled spacecraft.

Cross reactions elicited by serum 17-OH progesterone and 11-desoxycortisol in cortisol assays.

PubMed

Brossaud, Julie; Barat, Pascal; Gualde, Dominique; Corcuff, Jean-Benoît

2009-09-01

Different pathophysiological situations such as congenital adrenal hyperplasia, adrenocortical carcinoma, metyrapone treatment, etc. elicit specificity problems with serum cortisol assay. We assayed cortisol using 2 kits and performed cross reaction studies as well as multiple regression analysis using 2 other steroids: 11-desoxycortisol and 17-OH progesterone. Analysis showed the existence of an analytical bias. Importantly, significantly different biases were demonstrated in newborns or patients taking metyrapone. Multiple regression analysis and cross reaction studies showed that 11-desoxycortisol level significantly influenced cortisol determination. Moreover, despite using the normal ranges provided by manufacturers discrepant results occurred such as 17% discordance in the diagnosis of hypocorticism in infants. We wish to raise awareness about the consequences of the (lack of) specificity of cortisol assays with regard to the evaluation of hypocorticism in infants or when "unusual" steroids may be increased.

Multiplicative Effects of Social and Psychological Risk Factors on College Students' Suicidal Behaviors.

PubMed

Assari, Shervin

2018-05-17

Less is known about the multiplicative effects of social and psychological risk and protective factors of suicidality on college campuses. The current study aimed to investigate the multiplicative effects of social (identifying oneself as gay/lesbian, financial difficulty, violence victimization, and religiosity) and psychological (anxiety, depression, problem alcohol use, drug use) and risk/protective factors on suicidal behaviors among college students in the United States. Using a cross-sectional design, the Healthy Mind Study (HMS; 2016⁻2017), is a national online survey of college students in the United States. Social (identifying oneself as gay/lesbian, violence victimization, financial difficulty, and religiosity) and psychological (anxiety, depression, problem alcohol use, and drug use) risk/protective factors were assessed among 27,961 individuals. Three aspects of suicidality, including ideation, plan, and attempt, were also assessed. Logistic regression models were used for data analysis. Financial difficulty, violence victimization, identifying oneself as gay/lesbian, anxiety, depression, and drug use increased, while religiosity reduced the odds of suicidal behaviors. Multiplicative effects were found between the following social and psychological risk factors: (1) financial difficulty and anxiety; (2) financial difficulty and depression; (3) depression and drug use; (4) problem alcohol use and drug use; and (5) depression and problem alcohol use. There is a considerable overlap in the social and psychological processes, such as financial stress, mood disorders, and substance use problems, on risk of suicide in college students. As social and psychological risk factors do not operate independently, comprehensive suicidal risk evaluations that simultaneously address multiple social and psychological risk factors may be superior to programs that only address a single risk factor.

Multiple Ordinal Regression by Maximizing the Sum of Margins

PubMed Central

Hamsici, Onur C.; Martinez, Aleix M.

2016-01-01

Human preferences are usually measured using ordinal variables. A system whose goal is to estimate the preferences of humans and their underlying decision mechanisms requires to learn the ordering of any given sample set. We consider the solution of this ordinal regression problem using a Support Vector Machine algorithm. Specifically, the goal is to learn a set of classifiers with common direction vectors and different biases correctly separating the ordered classes. Current algorithms are either required to solve a quadratic optimization problem, which is computationally expensive, or are based on maximizing the minimum margin (i.e., a fixed margin strategy) between a set of hyperplanes, which biases the solution to the closest margin. Another drawback of these strategies is that they are limited to order the classes using a single ranking variable (e.g., perceived length). In this paper, we define a multiple ordinal regression algorithm based on maximizing the sum of the margins between every consecutive class with respect to one or more rankings (e.g., perceived length and weight). We provide derivations of an efficient, easy-to-implement iterative solution using a Sequential Minimal Optimization procedure. We demonstrate the accuracy of our solutions in several datasets. In addition, we provide a key application of our algorithms in estimating human subjects’ ordinal classification of attribute associations to object categories. We show that these ordinal associations perform better than the binary one typically employed in the literature. PMID:26529784

On using summary statistics from an external calibration sample to correct for covariate measurement error.

PubMed

Guo, Ying; Little, Roderick J; McConnell, Daniel S

2012-01-01

Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed. Information about measurement error is provided in an external calibration sample where data on X and W (but not Y and Z) are recorded. We describe a method that uses summary statistics from the calibration sample to create multiple imputations of the missing values of X in the regression sample, so that the regression coefficients of Y on X and Z and associated standard errors can be estimated using simple multiple imputation combining rules, yielding valid statistical inferences under the assumption of a multivariate normal distribution. The proposed method is shown by simulation to provide better inferences than existing methods, namely the naive method, classical calibration, and regression calibration, particularly for correction for bias and achieving nominal confidence levels. We also illustrate our method with an example using linear regression to examine the relation between serum reproductive hormone concentrations and bone mineral density loss in midlife women in the Michigan Bone Health and Metabolism Study. Existing methods fail to adjust appropriately for bias due to measurement error in the regression setting, particularly when measurement error is substantial. The proposed method corrects this deficiency.

Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

PubMed

Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

2013-09-01

Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p < 0.01), bizygomatic breadth (p < 0.01) and head height (p < 0.05), and a negative relationship between CI and morphological facial height (p < 0.01) and head circumference (p < 0.01). Moreover, the coefficient and odds ratio of logistic regression analysis showed a greater likelihood for minimum frontal breadth (p < 0.01) and bizygomatic breadth (p < 0.01) to predict round-headedness, and morphological facial height (p < 0.05) and head circumference (p < 0.01) to predict long-headedness. Stepwise regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

Mediation of the relationship between callous-unemotional traits and proactive aggression by amygdala response to fear among children with conduct problems.

PubMed

Lozier, Leah M; Cardinale, Elise M; VanMeter, John W; Marsh, Abigail A

2014-06-01

Among youths with conduct problems, callous-unemotional (CU) traits are known to be an important determinant of symptom severity, prognosis, and treatment responsiveness. But positive correlations between conduct problems and CU traits result in suppressor effects that may mask important neurobiological distinctions among subgroups of children with conduct problems. To assess the unique neurobiological covariates of CU traits and externalizing behaviors in youths with conduct problems and determine whether neural dysfunction linked to CU traits mediates the link between callousness and proactive aggression. This cross-sectional case-control study involved behavioral testing and neuroimaging that were conducted at a university research institution. Neuroimaging was conducted using a 3-T Siemens magnetic resonance imaging scanner. It included 46 community-recruited male and female juveniles aged 10 to 17 years, including 16 healthy control participants and 30 youths with conduct problems with both low and high levels of CU traits. Blood oxygenation level-dependent signal as measured via functional magnetic resonance imaging during an implicit face-emotion processing task and analyzed using whole-brain and region of interest-based analysis of variance and multiple-regression analyses. Analysis of variance revealed no group differences in the amygdala. By contrast, consistent with the existence of suppressor effects, multiple-regression analysis found amygdala responses to fearful expressions to be negatively associated with CU traits (x = 26, y = 0, z = -12; k = 1) and positively associated with externalizing behavior (x = 24, y = 0, z = -14; k = 8) when both variables were modeled simultaneously. Reduced amygdala responses mediated the relationship between CU traits and proactive aggression. The results linked proactive aggression in youths with CU traits to hypoactive amygdala responses to emotional distress cues, consistent with theories that externalizing behaviors, particularly proactive aggression, in youths with these traits stem from deficient empathic responses to distress. Amygdala hypoactivity may represent an intermediate phenotype, offering new insights into effective treatment strategies for conduct problems.

Rape Myth Acceptance, Hypermasculinity, and SAT Scores as Correlates of Moral Development: Understanding Sexually Aggressive Attitudes in First-Year College Men

ERIC Educational Resources Information Center

Tatum, Jerry L.; Foubert, John D.

2009-01-01

Male perpetrated sexual aggression has long been recognized as a serious problem on college campuses. The purpose of this multiple regression correlation study was to assess the relationship between levels of moral development (measured by the Defining Issues Test) and the degree to which first-year college men (N = 161) ascribed to rape…

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

A method for the selection of a functional form for a thermodynamic equation of state using weighted linear least squares stepwise regression

NASA Technical Reports Server (NTRS)

Jacobsen, R. T.; Stewart, R. B.; Crain, R. W., Jr.; Rose, G. L.; Myers, A. F.

1976-01-01

A method was developed for establishing a rational choice of the terms to be included in an equation of state with a large number of adjustable coefficients. The methods presented were developed for use in the determination of an equation of state for oxygen and nitrogen. However, a general application of the methods is possible in studies involving the determination of an optimum polynomial equation for fitting a large number of data points. The data considered in the least squares problem are experimental thermodynamic pressure-density-temperature data. Attention is given to a description of stepwise multiple regression and the use of stepwise regression in the determination of an equation of state for oxygen and nitrogen.

High school science enrollment of black students

NASA Astrophysics Data System (ADS)

Goggins, Ellen O.; Lindbeck, Joy S.

How can the high school science enrollment of black students be increased? School and home counseling and classroom procedures could benefit from variables identified as predictors of science enrollment. The problem in this study was to identify a set of variables which characterize science course enrollment by black secondary students. The population consisted of a subsample of 3963 black high school seniors from The High School and Beyond 1980 Base-Year Survey. Using multiple linear regression, backward regression, and correlation analyses, the US Census regions and grades mostly As and Bs in English were found to be significant predictors of the number of science courses scheduled by black seniors.

Cognitive Rehabilitation for Attention and Memory in people with Multiple Sclerosis: study protocol for a randomised controlled trial (CRAMMS).

PubMed

Lincoln, Nadina B; das Nair, Roshan; Bradshaw, Lucy; Constantinescu, Cris S; Drummond, Avril E R; Erven, Alexandra; Evans, Amy L; Fitzsimmons, Deborah; Montgomery, Alan A; Morgan, Miriam

2015-12-08

People with multiple sclerosis have problems with memory and attention. Cognitive rehabilitation is a structured set of therapeutic activities designed to retrain an individual's memory and other cognitive functions. Cognitive rehabilitation may be provided to teach people strategies to cope with these problems, in order to reduce the impact on everyday life. The effectiveness of cognitive rehabilitation for people with multiple sclerosis has not been established. This is a multi-centre, randomised controlled trial investigating the clinical and cost-effectiveness of a group-based cognitive rehabilitation programme for attention and memory problems for people with multiple sclerosis. Four hundred people with multiple sclerosis will be randomised from at least four centres. Participants will be eligible if they have memory problems, are 18 to 69 years of age, are able to travel to attend group sessions and give informed consent. Participants will be randomised in a ratio of 6:5 to the group rehabilitation intervention plus usual care or usual care alone. Intervention groups will receive 10 weekly sessions of a manualised cognitive rehabilitation programme. The intervention will include both restitution strategies to retrain impaired attention and memory functions and compensation strategies to enable participants to cope with their cognitive problems. All participants will receive a follow-up questionnaire and an assessment by a research assistant at 6 and 12 months after randomisation. The primary outcome is the Multiple Sclerosis Impact Scale (MSIS) Psychological subscale at 12 months. Secondary outcomes include the Everyday Memory Questionnaire, General Health Questionnaire-30, EQ-5D and a service use questionnaire from participants, and the Everyday Memory Questionnaire-relative version and Carer Strain Index from a relative or friend. The primary analysis will be based on intention to treat. A mixed-model regression analysis of the MSIS Psychological subscale at 12 months will be used to estimate the effect of the group cognitive rehabilitation programme. The study will provide evidence regarding the clinical and cost-effectiveness of a group-based cognitive rehabilitation programme for attention and memory problems in people with multiple sclerosis. ISRCTN09697576 . Registered 14 August 2014.

Multiple Linear Regression Analysis of Factors Affecting Real Property Price Index From Case Study Research In Istanbul/Turkey

NASA Astrophysics Data System (ADS)

Denli, H. H.; Koc, Z.

2015-12-01

Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.

Predicting doctor performance outcomes of curriculum interventions: problem-based learning and continuing competence.

PubMed

Norman, Geoffrey R; Wenghofer, Elizabeth; Klass, Daniel

2008-08-01

Problem-based learning (PBL) is an educational strategy designed to enhance self-assessment, self-directed learning and lifelong learning. The present study examines a peer review programme to determine whether the impact of PBL on continuing competence can be detected in practice. This study aimed to establish whether McMaster graduates who graduated between 1972 and 1991 were any less likely to be identified as having issues of competence by a systematic peer review programme than graduates of other Ontario medical schools. We identified a total of 1166 doctors who had graduated after 1972 and had completed a mandated peer review programme. Of these, 108 had graduated from McMaster and 857 from other Canadian schools. School of graduation was cross-tabulated against peer rating. A secondary analysis examined predictors of ratings using multiple regression. We found that 4% of McMaster graduates and 5% of other graduates were deemed to demonstrate cause for concern or serious concern, and that 24% of McMaster doctors and 28% of other doctors were rated as excellent. These differences were not significant. Multiple regression indicated that certification by family medicine or a specialty, female gender and younger age were all predictors of practice outcomes, but school of graduation was not. There is no evidence from this study that PBL graduates are better able to maintain competence than graduates of conventional schools. The study highlights potential problems in attempting to link undergraduate educational interventions to doctor performance outcomes.

Domain General Mediators of the Relation between Kindergarten Number Sense and First-Grade Mathematics Achievement

PubMed Central

Hassinger-Das, Brenna; Jordan, Nancy C.; Glutting, Joseph; Irwin, Casey; Dyson, Nancy

2013-01-01

Domain general skills that mediate the relation between kindergarten number sense and first-grade mathematics skills were investigated. Participants were 107 children who displayed low number sense in the fall of kindergarten. Controlling for background variables, multiple regression analyses showed that attention problems and executive functioning both were unique predictors of mathematics outcomes. Attention problems were more important for predicting first-grade calculation performance while executive functioning was more important for predicting first-grade performance on applied problems. Moreover, both executive functioning and attention problems were unique partial mediators of the relationship between kindergarten and first-grade mathematics skills. The results provide empirical support for developing interventions that target executive functioning and attention problems in addition to instruction in number skills for kindergartners with initial low number sense. PMID:24237789

Domain-general mediators of the relation between kindergarten number sense and first-grade mathematics achievement.

PubMed

Hassinger-Das, Brenna; Jordan, Nancy C; Glutting, Joseph; Irwin, Casey; Dyson, Nancy

2014-02-01

Domain-general skills that mediate the relation between kindergarten number sense and first-grade mathematics skills were investigated. Participants were 107 children who displayed low number sense in the fall of kindergarten. Controlling for background variables, multiple regression analyses showed that both attention problems and executive functioning were unique predictors of mathematics outcomes. Attention problems were more important for predicting first-grade calculation performance, whereas executive functioning was more important for predicting first-grade performance on applied problems. Moreover, both executive functioning and attention problems were unique partial mediators of the relationship between kindergarten and first-grade mathematics skills. The results provide empirical support for developing interventions that target executive functioning and attention problems in addition to instruction in number skills for kindergartners with initial low number sense. Copyright © 2013 Elsevier Inc. All rights reserved.

Perceived school safety is strongly associated with adolescent mental health problems.

PubMed

Nijs, Miesje M; Bun, Clothilde J E; Tempelaar, Wanda M; de Wit, Niek J; Burger, Huibert; Plevier, Carolien M; Boks, Marco P M

2014-02-01

School environment is an important determinant of psychosocial function and may also be related to mental health. We therefore investigated whether perceived school safety, a simple measure of this environment, is related to mental health problems. In a population-based sample of 11,130 secondary school students, we analysed the relationship of perceived school safety with mental health problems using multiple logistic regression analyses to adjust for potential confounders. Mental health problems were defined using the clinical cut-off of the self-reported Strengths and Difficulties Questionnaire. School safety showed an exposure-response relationship with mental health problems after adjustment for confounders. Odds ratios increased from 2.48 ("sometimes unsafe") to 8.05 ("very often unsafe"). The association was strongest in girls and young and middle-aged adolescents. Irrespective of the causal background of this association, school safety deserves attention either as a risk factor or as an indicator of mental health problems.

A rotor optimization using regression analysis

NASA Technical Reports Server (NTRS)

Giansante, N.

1984-01-01

The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Back problems, comorbidities, and their association with wealth.

PubMed

Schofield, Deborah J; Callander, Emily J; Shrestha, Rupendra N; Passey, Megan E; Kelly, Simon J; Percival, Richard

2015-01-01

Studies assessing the economic burden of back problems have given little consideration to the presence of comorbidities. To assess the difference in the value of wealth held by Australians who have back problems and varying numbers of chronic comorbidities. A cross-sectional study. Individuals aged 45 to 64 years in 2009: 4,388 with no chronic health conditions, 1,405 with back problems, and 3,018 with other health conditions. Total wealth (cash, shares, superannuation, investment property, and owner occupied home). Using a microsimulation model (Health&WealthMOD), logistic regression models were used to assess the odds of having any wealth. Linear regression models were used to assess the difference in the value of this wealth. Those with back problems and two comorbidities had 0.16 (95% confidence interval [CI]: 0.06-0.42) times the odds and those with back problems and three or more comorbidities had 0.20 (95% CI: 0.11-0.38) times the odds of having accumulated some wealth than those with no chronic health conditions. Those with back problems and three or more comorbidities had a median value of total wealth of around $150,000, whereas those with back problems only and back problems and one comorbidity had a median value of total wealth of around $250,500. There was no significant difference in the amount of wealth accumulated by those with back problems and at least one comorbidity and those with other health conditions and the same number of comorbidities. However, those with only one health condition (excluding back problems) had 65% more wealth than those with back problems only (95% CI: 5.1-161.2). This study highlights the importance of considering multiple morbidities when discussing the relationship between back problems and economic circumstances. Copyright © 2015 Elsevier Inc. All rights reserved.

Co-Occurring Psychosocial Problems and HIV Risk Among Women Attending Drinking Venues in a South African Township: A Syndemic Approach

PubMed Central

Pitpitan, Eileen V.; Kalichman, Seth C.; Eaton, Lisa A.; Cain, Demetria; Sikkema, Kathleen J.; Watt, Melissa H.; Skinner, Donald; Pieterse, Desiree

2012-01-01

Background In South Africa, women comprise the majority of HIV infections. Syndemics, or co-occurring epidemics and risk factors, have been applied to understanding HIV risk among marginalized groups. Purpose To apply the syndemic framework to examine psychosocial problems that co-occur among women attending drinking venues in South Africa, and to test how the co-occurrence of these problems may exacerbate risk for HIV infection. Method 560 women from a Cape Town township provided data on multiple psychosocial problems, including food insufficiency, depression, abuse experiences, problem drinking, and sexual behaviors. Results Bivariate associations among the syndemic factors showed a high degree of co-occurrence and regression analyses showed an additive effect of psychosocial problems on HIV risk behaviors. Conclusions These results demonstrate the utility of a syndemic framework to understand co-occurring psychosocial problems among women in South Africa. HIV prevention interventions should consider the compounding effects of psychosocial problems among women. PMID:23054944

The evaluation of the National Long Term Care Demonstration. 2. Estimation methodology.

PubMed Central

Brown, R S

1988-01-01

Channeling effects were estimated by comparing the post-application experience of the treatment and control groups using multiple regression. A variety of potential threats to the validity of the results, including sample composition issues, data issues, and estimation issues, were identified and assessed. Of all the potential problems examined, the only one determined to be likely to cause widespread distortion of program impact estimates was noncomparability of the baseline data. To avoid this distortion, baseline variables judged to be noncomparably measured were excluded from use as control variables in the regression equation. (Where they existed, screen counterparts to these noncomparable baseline variables were used as substitutes.) All of the other potential problems with the sample, data, or regression estimation approach were found to have little or no actual effect on impact estimates or their interpretation. Broad implementation of special procedures, therefore, was not necessary. The study did find that, because of the frequent use of proxy respondents, the estimated effects of channeling on clients' well-being actually may reflect impacts on the well-being of the informal caregiver rather than the client. This and other isolated cases in which there was some evidence of a potential problem for specific outcome variables were identified and examined in detail in technical reports dealing with those outcomes. Where appropriate, alternative estimates were presented. PMID:3130329

Estimating standard errors in feature network models.

PubMed

Frank, Laurence E; Heiser, Willem J

2007-05-01

Feature network models are graphical structures that represent proximity data in a discrete space while using the same formalism that is the basis of least squares methods employed in multidimensional scaling. Existing methods to derive a network model from empirical data only give the best-fitting network and yield no standard errors for the parameter estimates. The additivity properties of networks make it possible to consider the model as a univariate (multiple) linear regression problem with positivity restrictions on the parameters. In the present study, both theoretical and empirical standard errors are obtained for the constrained regression parameters of a network model with known features. The performance of both types of standard error is evaluated using Monte Carlo techniques.

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

PubMed

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

2018-02-12

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

Contributions of sociodemographic factors to criminal behavior

PubMed Central

Mundia, Lawrence; Matzin, Rohani; Mahalle, Salwa; Hamid, Malai Hayati; Osman, Ratna Suriani

2016-01-01

We explored the extent to which prisoner sociodemographic variables (age, education, marital status, employment, and whether their parents were married or not) influenced offending in 64 randomly selected Brunei inmates, comprising both sexes. A quantitative field survey design ideal for the type of participants used in a prison context was employed to investigate the problem. Hierarchical multiple regression analysis with backward elimination identified prisoner marital status and age groups as significantly related to offending. Furthermore, hierarchical multinomial logistic regression analysis with backward elimination indicated that prisoners’ age, primary level education, marital status, employment status, and parental marital status as significantly related to stealing offenses with high odds ratios. All 29 nonrecidivists were false negatives and predicted to reoffend upon release. Similarly, all 33 recidivists were projected to reoffend after release. Hierarchical binary logistic regression analysis revealed age groups (24–29 years and 30–35 years), employed prisoner, and primary level education as variables with high likelihood trends for reoffending. The results suggested that prisoner interventions (educational, counseling, and psychotherapy) in Brunei should treat not only antisocial personality, psychopathy, and mental health problems but also sociodemographic factors. The study generated offending patterns, trends, and norms that may inform subsequent investigations on Brunei prisoners. PMID:27382342

On Bayesian methods of exploring qualitative interactions for targeted treatment.

PubMed

Chen, Wei; Ghosh, Debashis; Raghunathan, Trivellore E; Norkin, Maxim; Sargent, Daniel J; Bepler, Gerold

2012-12-10

Providing personalized treatments designed to maximize benefits and minimizing harms is of tremendous current medical interest. One problem in this area is the evaluation of the interaction between the treatment and other predictor variables. Treatment effects in subgroups having the same direction but different magnitudes are called quantitative interactions, whereas those having opposite directions in subgroups are called qualitative interactions (QIs). Identifying QIs is challenging because they are rare and usually unknown among many potential biomarkers. Meanwhile, subgroup analysis reduces the power of hypothesis testing and multiple subgroup analyses inflate the type I error rate. We propose a new Bayesian approach to search for QI in a multiple regression setting with adaptive decision rules. We consider various regression models for the outcome. We illustrate this method in two examples of phase III clinical trials. The algorithm is straightforward and easy to implement using existing software packages. We provide a sample code in Appendix A. Copyright © 2012 John Wiley & Sons, Ltd.

Contribution of problem-solving skills to fear of recurrence in breast cancer survivors.

PubMed

Akechi, Tatuo; Momino, Kanae; Yamashita, Toshinari; Fujita, Takashi; Hayashi, Hironori; Tsunoda, Nobuyuki; Iwata, Hiroji

2014-05-01

Although fear of recurrence is a major concern among breast cancer survivors after surgery, no standard strategies exist that alleviate their distress. This study examined the association of patients' problem-solving skills and fear of recurrence and psychological distress among breast cancer survivors. Randomly selected, ambulatory, female patients with breast cancer participated in this study. They were asked to complete the Concerns about Recurrence Scale (CARS) and the Hospital Anxiety and Depression Scale. Multiple regression analyses were used to examine their associations. Data were obtained from 317 patients. Patients' problem-solving skills were significantly associated with all subscales of fear of recurrence and overall worries measured by the CARS. In addition, patients' problem-solving skills were significantly associated with both their anxiety and depression. Our findings warrant clinical trials to investigate effectiveness of psychosocial intervention program, including enhancing patients' problem-solving skills and reducing fear of recurrence among breast cancer survivors.

Causes of coal-miner absenteeism. Information Circular/1987

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

Peters, R.H.; Randolph, R.F.

The Bureau of Mines report describes several significant problems associated with absenteeism among underground coal miners. The vast empirical literature on employee absenteeism is reviewed, and a conceptual model of the factors that cause absenteeism among miners is presented. Portions of the model were empirically tested by performing correlational and multiple regression analyses on data collected from a group of 64 underground coal miners. The results of these tests are presented and discussed.

A Unified Framework for Association Analysis with Multiple Related Phenotypes

PubMed Central

Stephens, Matthew

2013-01-01

We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations – that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5–10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data. PMID:23861737

Personality and self-reported use of mobile phones for games.

PubMed

Phillips, James G; Butt, Sarah; Blaszczynski, Alex

2006-12-01

Mobile phones are popular devices that may generate problems for a section of the community. A previous study using the Eysenck Personality Questionnaire found that extraverts with low self-esteem reported more problems with their mobile phone use. The present study used the NEO FI and Coopersmith Self-Esteem Inventory to predict the self reported mobile phone use of 112 participants. Multiple regression found that people low on agreeableness were more likely to use their mobile phones to play games. The findings imply an interplay between personality traits and excessive or problematic use on mobile phones that is relevant to proposed innovations such as gambling on mobile phones.

Do social support, stigma, and social problem-solving skills predict depressive symptoms in people living with HIV? A mediation analysis.

PubMed

White, Worawan; Grant, Joan S; Pryor, Erica R; Keltner, Norman L; Vance, David E; Raper, James L

2012-01-01

Social support, stigma, and social problem solving may be mediators of the relationship between sign and symptom severity and depressive symptoms in people living with HIV (PLWH). However, no published studies have examined these individual variables as mediators in PLWH. This cross-sectional, correlational study of 150 PLWH examined whether social support, stigma, and social problem solving were mediators of the relationship between HIV-related sign and symptom severity and depressive symptoms. Participants completed self-report questionnaires during their visits at two HIV outpatient clinics in the Southeastern United States. Using multiple regression analyses as a part of mediation testing, social support, stigma, and social problem solving were found to be partial mediators of the relationship between sign and symptom severity and depressive symptoms, considered individually and as a set.

Parent-reported suicidal behavior and correlates among adolescents in China.

PubMed

Liu, Xianchen; Sun, Zhenxiao; Yang, Yanyun

2008-01-01

Suicidal risk begins to increase during adolescence and is associated with multiple biological, psychological, social, and cultural factors. This study examined the prevalence and psychosocial factors of parent-reported suicidal behavior in Chinese adolescents. A community sample of 1920 adolescents in China participated in an epidemiological study. Parents completed a structured questionnaire including child suicidal behavior, illness history, mental health problems, family history, parenting, and family environment. Multiple logistic regression was used for data analysis. Overall, 2.4% of the sample talked about suicide in the previous 6 months, 3.2% had deliberately hurt themselves or attempted suicide, and 5.1% had either suicidal talk or self-harm. The rate of suicidal behavior increased as adolescents aged. Multivariate logistic regression indicated that the following factors were significantly associated with elevated risk for suicidal behavior: depressive/anxious symptoms, poor maternal health, family conflict, and physical punishment of parental discipline style. Suicidal behavior was reported by parents. No causal relationships could be made based on cross-sectional data. The prevalence rate of parent-reported suicidal behavior is markedly lower than self-reported rate in previous research. Depressive/anxious symptoms and multiple family environmental factors are associated with suicidal behavior in Chinese adolescents.

Does distraction facilitate problem-focused coping with job stress? A 1 year longitudinal study.

PubMed

Shimazu, Akihito; Schaufeli, Wilmar B

2007-10-01

This study examined the sole and combined effects of problem-focused coping and distraction on employee well-being (i.e., stress responses and job performance) using two-wave panel survey data with a 1-year time lag. Participants were 488 male employees, who worked for a construction machinery company in western Japan. Hierarchical multiple regression analyses were conducted to examine whether distraction moderates the relationship of problem-focused coping with well-being. More use of problem-focused coping was negatively related to subsequent stress responses among those high in distraction. The combination of high problem-focused coping and high distraction was positively related to subsequent job performance, although it was limited only to the high job stress situation. Results suggest that the combination of high problem-focused coping and high distraction may lead to lower stress responses and better performance (but only in high job stress situations for performance) than the combination of high problem-focused coping and low distraction, at least for male blue-collar workers.

Optimal Wavelength Selection on Hyperspectral Data with Fused Lasso for Biomass Estimation of Tropical Rain Forest

NASA Astrophysics Data System (ADS)

Takayama, T.; Iwasaki, A.

2016-06-01

Above-ground biomass prediction of tropical rain forest using remote sensing data is of paramount importance to continuous large-area forest monitoring. Hyperspectral data can provide rich spectral information for the biomass prediction; however, the prediction accuracy is affected by a small-sample-size problem, which widely exists as overfitting in using high dimensional data where the number of training samples is smaller than the dimensionality of the samples due to limitation of require time, cost, and human resources for field surveys. A common approach to addressing this problem is reducing the dimensionality of dataset. Also, acquired hyperspectral data usually have low signal-to-noise ratio due to a narrow bandwidth and local or global shifts of peaks due to instrumental instability or small differences in considering practical measurement conditions. In this work, we propose a methodology based on fused lasso regression that select optimal bands for the biomass prediction model with encouraging sparsity and grouping, which solves the small-sample-size problem by the dimensionality reduction from the sparsity and the noise and peak shift problem by the grouping. The prediction model provided higher accuracy with root-mean-square error (RMSE) of 66.16 t/ha in the cross-validation than other methods; multiple linear analysis, partial least squares regression, and lasso regression. Furthermore, fusion of spectral and spatial information derived from texture index increased the prediction accuracy with RMSE of 62.62 t/ha. This analysis proves efficiency of fused lasso and image texture in biomass estimation of tropical forests.

Macrocell path loss prediction using artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.

2014-04-01

The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.

Real-world implications of apathy among older adults: Independent associations with activities of daily living and quality of life.

PubMed

Tierney, Savanna M; Woods, Steven Paul; Weinborn, Michael; Bucks, Romola S

2018-03-13

Apathy is common in older adults and has been linked to adverse health outcomes. The current study examined whether apathy contributes to problems managing activities of daily living (ADLs) and lower quality of life (QoL) in older adults. Participants included 83 community-dwelling older adults. Apathy was assessed using a composite of the self and family-rating scales from the Frontal Systems Behavioral Scale (FrSBe). A knowledgeable informant completed the Activities of Daily Living Questionnaire (ADLQ), and participants completed the World Health Organization Quality of Life (WHOQol) scale. Nominal logistic regressions controlling for age, anxiety and depression symptoms, chronic medical conditions, and global cognition revealed that higher levels of apathy were significantly associated with a wide range of mild ADL problems. In parallel, a multiple linear regression indicated that greater apathy was significantly associated with lower QoL independent of ADL problems, anxious and depressive symptomology, chronic medical conditions, global cognition and age. Findings suggest that apathy confers an increased risk of problems in the independent management of daily activities and poorer well-being among community-dwelling older adults. Neurobehavioral and pharmacological interventions to improve apathy may have beneficial effects on the daily lives of older adults.

Mediation of the Relationship Between Callous-Unemotional Traits and Proactive Aggression by Amygdala Response to Fear Among Children With Conduct Problems

PubMed Central

Lozier, Leah M.; Cardinale, Elise M.; VanMeter, John W.; Marsh, Abigail A.

2015-01-01

Importance Among youths with conduct problems, callous-unemotional (CU) traits are known to be an important determinant of symptom severity, prognosis, and treatment responsiveness. But positive correlations between conduct problems and CU traits result in suppressor effects that may mask important neurobiological distinctions among subgroups of children with conduct problems. Objective To assess the unique neurobiological covariates of CU traits and externalizing behaviors in youths with conduct problems and determine whether neural dysfunction linked to CU traits mediates the link between callousness and proactive aggression. Design, Setting, and Participants This cross-sectional case-control study involved behavioral testing and neuroimaging that were conducted at a university research institution. Neuroimaging was conducted using a 3-T Siemens magnetic resonance imaging scanner. It included 46 community-recruited male and female juveniles aged 10 to 17 years, including 16 healthy control participants and 30 youths with conduct problems with both low and high levels of CU traits. Main Outcomes and Measures Blood oxygenation level–dependent signal as measured via functional magnetic resonance imaging during an implicit face-emotion processing task and analyzed using whole-brain and region of interest–based analysis of variance and multiple-regression analyses. Results Analysis of variance revealed no group differences in the amygdala. By contrast, consistent with the existence of suppressor effects, multiple-regression analysis found amygdala responses to fearful expressions to be negatively associated with CU traits (x = 26, y = 0, z = −12; k = 1) and positively associated with externalizing behavior (x = 24, y = 0, z = −14; k = 8) when both variables were modeled simultaneously. Reduced amygdala responses mediated the relationship between CU traits and proactive aggression. Conclusions and Relevance The results linked proactive aggression in youths with CU traits to hypoactive amygdala responses to emotional distress cues, consistent with theories that externalizing behaviors, particularly proactive aggression, in youths with these traits stem from deficient empathic responses to distress. Amygdala hypoactivity may represent an intermediate phenotype, offering new insights into effective treatment strategies for conduct problems. PMID:24671141

GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA

PubMed Central

Zheng, Qi; Peng, Limin; He, Xuming

2015-01-01

Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically prespecified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal. PMID:26604424

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

The relation between stressful life events and adjustment in elementary school children: the role of social support and social problem-solving skills.

PubMed

Dubow, E F; Tisak, J

1989-12-01

This study investigated the relation between stressful life events and adjustment in elementary school children, with particular emphasis on the potential main and stress-buffering effects of social support and social problem-solving skills. Third through fifth graders (N = 361) completed social support and social problem-solving measures. Their parents provided ratings of stress in the child's environment and ratings of the child's behavioral adjustment. Teachers provided ratings of the children's behavioral and academic adjustment. Hierarchical multiple regressions revealed significant stress-buffering effects for social support and problem-solving skills on teacher-rated behavior problems, that is, higher levels of social support and problem-solving skills moderated the relation between stressful life events and behavior problems. A similar stress-buffering effect was found for problem-solving skills on grade-point average and parent-rated behavior problems. In terms of children's competent behaviors, analyses supported a main effect model of social support and problem-solving. Possible processes accounting for the main and stress-buffering effects are discussed.

Causal correlation of foliar biochemical concentrations with AVIRIS spectra using forced entry linear regression

NASA Technical Reports Server (NTRS)

Dawson, Terence P.; Curran, Paul J.; Kupiec, John A.

1995-01-01

A major goal of airborne imaging spectrometry is to estimate the biochemical composition of vegetation canopies from reflectance spectra. Remotely-sensed estimates of foliar biochemical concentrations of forests would provide valuable indicators of ecosystem function at regional and eventually global scales. Empirical research has shown a relationship exists between the amount of radiation reflected from absorption features and the concentration of given biochemicals in leaves and canopies (Matson et al., 1994, Johnson et al., 1994). A technique commonly used to determine which wavelengths have the strongest correlation with the biochemical of interest is unguided (stepwise) multiple regression. Wavelengths are entered into a multivariate regression equation, in their order of importance, each contributing to the reduction of the variance in the measured biochemical concentration. A significant problem with the use of stepwise regression for determining the correlation between biochemical concentration and spectra is that of 'overfitting' as there are significantly more wavebands than biochemical measurements. This could result in the selection of wavebands which may be more accurately attributable to noise or canopy effects. In addition, there is a real problem of collinearity in that the individual biochemical concentrations may covary. A strong correlation between the reflectance at a given wavelength and the concentration of a biochemical of interest, therefore, may be due to the effect of another biochemical which is closely related. Furthermore, it is not always possible to account for potentially suitable waveband omissions in the stepwise selection procedure. This concern about the suitability of stepwise regression has been identified and acknowledged in a number of recent studies (Wessman et al., 1988, Curran, 1989, Curran et al., 1992, Peterson and Hubbard, 1992, Martine and Aber, 1994, Kupiec, 1994). These studies have pointed to the lack of a physical link between wavelengths chosen by stepwise regression and the biochemical of interest, and this in turn has cast doubts on the use of imaging spectrometry for the estimation of foliar biochemical concentrations at sites distant from the training sites. To investigate this problem, an analysis was conducted on the variation in canopy biochemical concentrations and reflectance spectra using forced entry linear regression.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

Quality of life of patients who undergo breast reconstruction after mastectomy: effects of personality characteristics.

PubMed

Bellino, Silvio; Fenocchio, Marina; Zizza, Monica; Rocca, Giuseppe; Bogetti, Paolo; Bogetto, Filippo

2011-01-01

Reconstruction after mastectomy has become an integral part of breast cancer treatment. The effects of psychological factors on quality of life after reconstruction have been poorly investigated. The authors examined clinical and personality characteristics related to quality of life in patients receiving reconstructive surgery. All patients received immediate reconstruction and were evaluated in the week before tissue expander implantation (T0) with a semistructured interview for demographic and clinical characteristics, the Temperament and Character Inventory, the Inventory of Interpersonal Problems, the Short Form Health Survey, the Severity Item of the Clinical Global Impression, the Hamilton Depression Rating Scale, and the Hamilton Anxiety Rating Scale. Assessment with the Short Form was repeated 3 months after expander placement (T1). Statistics were calculated with univariate regression and analysis of variance. Significant variables were included in a multiple regression analysis to identify factors related to the change T1-T0 of the mean of the Short Form-transformed scores. Results were significant when p was less than or equal to 0.05. Fifty-seven women were enrolled. Results of multiple regression analysis showed that the Temperament and Character Inventory personality dimension harm avoidance and the Inventory of Interpersonal Problems domain vindictive/self-centered were significantly and independently related to the change in Short Form mean score. Personality dimensions and patterns of interpersonal functioning produce significant effects on patients' quality of life during breast reconstruction. Patients with high harm avoidance are apprehensive and doubtful. Restoration of body image could help them to reduce social anxiety and insecurity. Vindictive/self-centered patients are resentful and aggressive. Breast reconstruction could symbolize the conclusion of a reparative process and fulfill the desire of revenge on cancer.

Psychological factors are associated with subjective cognitive complaints 2 months post-stroke.

PubMed

Nijsse, Britta; van Heugten, Caroline M; van Mierlo, Marloes L; Post, Marcel W M; de Kort, Paul L M; Visser-Meily, Johanna M A

2017-01-01

The aim of this study was to investigate which psychological factors are related to post-stroke subjective cognitive complaints, taking into account the influence of demographic and stroke-related characteristics, cognitive deficits and emotional problems. In this cross-sectional study, 350 patients were assessed at 2 months post-stroke, using the Checklist for Cognitive and Emotional consequences following stroke (CLCE-24) to identify cognitive complaints. Psychological factors were: proactive coping, passive coping, self-efficacy, optimism, pessimism, extraversion, and neuroticism. Associations between CLCE-24 cognition score and psychological factors, emotional problems (depressive symptoms and anxiety), cognitive deficits, and demographic and stroke characteristics were examined using Spearman correlations and multiple regression analyses. Results showed that 2 months post-stroke, 270 patients (68.4%) reported at least one cognitive complaint. Age, sex, presence of recurrent stroke(s), comorbidity, cognitive deficits, depressive symptoms, anxiety, and all psychological factors were significantly associated with the CLCE-24 cognition score in bivariate analyses. Multiple regression analysis showed that psychological factors explained 34.7% of the variance of cognitive complaints independently, and 8.5% (p < .001) after taking all other factors into account. Of all psychological factors, proactive coping was independently associated with cognitive complaints (p < .001), showing that more proactive coping related to less cognitive complaints. Because cognitive complaints are common after stroke and are associated with psychological factors, it is important to focus on these factors in rehabilitation programmes.

Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results

ERIC Educational Resources Information Center

Warne, Russell T.

2011-01-01

Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

The influence of family management style on psychosocial problems of childhood cancer survivors in Korea.

PubMed

Kim, Dong Hee; Im, Yeo Jin

2015-04-01

To examine the psychosocial problems of childhood cancer survivors in Korea and investigate whether such problems are influenced by family management style. Family members of 158 childhood cancer survivors answered a questionnaire on demographic and illness characteristics, described psychosocial problems in their children using the Pediatric Symptom Checklist (PSC), and completed the Family Management Measure (FaMM). Perceived economic status and all six subscales of the FaMM were significantly correlated with children's psychosocial problems. In a multiple regression model, the Family Life Difficulty and Parental Mutuality scales of the FaMM were each independent predictors of psychosocial problems in young cancer survivors. A detailed care plan designed to (1) promote balance between the management of a child's condition and normal family life and (2) encourage parents to share their feelings with one another and provide mutual support should be required to improve psychosocial outcomes for childhood cancer survivors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Short-term electric power demand forecasting based on economic-electricity transmission model

NASA Astrophysics Data System (ADS)

Li, Wenfeng; Bai, Hongkun; Liu, Wei; Liu, Yongmin; Wang, Yubin Mao; Wang, Jiangbo; He, Dandan

2018-04-01

Short-term electricity demand forecasting is the basic work to ensure safe operation of the power system. In this paper, a practical economic electricity transmission model (EETM) is built. With the intelligent adaptive modeling capabilities of Prognoz Platform 7.2, the econometric model consists of three industrial added value and income levels is firstly built, the electricity demand transmission model is also built. By multiple regression, moving averages and seasonal decomposition, the problem of multiple correlations between variables is effectively overcome in EETM. The validity of EETM is proved by comparison with the actual value of Henan Province. Finally, EETM model is used to forecast the electricity consumption of the 1-4 quarter of 2018.

Associations between self-rated health and personality.

PubMed

Aiken-Morgan, Adrienne T; Bichsel, Jacqueline; Savla, Jyoti; Edwards, Christopher L; Whitfield, Keith E

2014-01-01

The goal of our study was to examine how Big Five personality factors predict variability in self-rated health in a sample of older African Americans from the Baltimore Study of Black Aging. Personality was measured by the NEO Personality Inventory-Revised, and self-rated health was assessed by the Health Problems Checklist. The study sample had 202 women and 87 men. Ages ranged from 49 to 90 years (M = 67.2 years, SD = 8.55), and average years of formal education was 10.8 (SD = 3.3). Multiple linear regressions showed that neuroticism and extraversion were significant regression predictors of self-rated health, after controlling for demographic factors. These findings suggest individual personality traits may influence health ratings, behaviors, and decision-making among older African Americans.

Minimization of annotation work: diagnosis of mammographic masses via active learning

NASA Astrophysics Data System (ADS)

Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

2018-06-01

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.

Minimization of annotation work: diagnosis of mammographic masses via active learning.

PubMed

Zhao, Yu; Zhang, Jingyang; Xie, Hongzhi; Zhang, Shuyang; Gu, Lixu

2018-05-22

The prerequisite for establishing an effective prediction system for mammographic diagnosis is the annotation of each mammographic image. The manual annotation work is time-consuming and laborious, which becomes a great hindrance for researchers. In this article, we propose a novel active learning algorithm that can adequately address this problem, leading to the minimization of the labeling costs on the premise of guaranteed performance. Our proposed method is different from the existing active learning methods designed for the general problem as it is specifically designed for mammographic images. Through its modified discriminant functions and improved sample query criteria, the proposed method can fully utilize the pairing of mammographic images and select the most valuable images from both the mediolateral and craniocaudal views. Moreover, in order to extend active learning to the ordinal regression problem, which has no precedent in existing studies, but is essential for mammographic diagnosis (mammographic diagnosis is not only a classification task, but also an ordinal regression task for predicting an ordinal variable, viz. the malignancy risk of lesions), multiple sample query criteria need to be taken into consideration simultaneously. We formulate it as a criteria integration problem and further present an algorithm based on self-adaptive weighted rank aggregation to achieve a good solution. The efficacy of the proposed method was demonstrated on thousands of mammographic images from the digital database for screening mammography. The labeling costs of obtaining optimal performance in the classification and ordinal regression task respectively fell to 33.8 and 19.8 percent of their original costs. The proposed method also generated 1228 wins, 369 ties and 47 losses for the classification task, and 1933 wins, 258 ties and 185 losses for the ordinal regression task compared to the other state-of-the-art active learning algorithms. By taking the particularities of mammographic images, the proposed AL method can indeed reduce the manual annotation work to a great extent without sacrificing the performance of the prediction system for mammographic diagnosis.

The relationship between interpersonal problems and occupational stress in physicians.

PubMed

Falkum, Erik; Vaglum, Per

2005-01-01

This article examined the associations between occupational stress and interpersonal problems in physicians. A nationwide representative sample of Norwegian physicians received the 64-item version of the Inventory of Interpersonal Problems (IIP-64) (N=862, response rate=70%) and six instruments measuring occupational stress. Comparison of means, correlation and reliability statistics and multiple regression analyses were applied. The IIP-64 total score had a significant impact on job satisfaction, perceived unrealistic expectancies, communication with colleagues and nurses and on stress from interaction with patients. Being overly subassertive was related to low job satisfaction. Being overly expressive was linked to the experience of unrealistic expectancies from others and lack of positive feedback, whereas overly competitive physicians tended to have poorer relationships with both colleagues and nurses. Addressing interpersonal problems in medical school and postgraduate training may be a valuable measure to prevent job stress and promote quality of care.

Mental health problems of Dutch adolescents: the association with adolescents' and their parents' educational level.

PubMed

Havas, Jano; Bosma, Hans; Spreeuwenberg, Cor; Feron, Frans J

2010-06-01

We studied the hypothesis of socioeconomic equalization regarding adolescents' mental health problems by examining whether a low educational level of adolescents and their parents shows independent (cumulative) or dependent (including interactive) associations with adolescents' mental health problems, or whether equalization occurred. Cross-sectional data were obtained from the preventive Youth Health Care Centre in a relatively deprived Dutch former mining area. Participants were 1861 adolescents aged 13 or 14 years (response rate 71.7%). The self-administered Dutch version of the Strengths and Difficulties Questionnaire (SDQ) was used to identify adolescents' mental health problems. Multiple logistic regression analyses were used to examine the associations, and linear regression models to check the robustness of the findings. A low educational level of adolescents was strongly related to their mental health problems (OR = 5.37; 95% CI: 3.31-8.70). The initially high odds ratios for adolescents with low-educated parents (OR = 1.72; 95% CI: 1.14-2.59) disappeared after controlling for the adolescents' own educational level (OR = 1.12; 95% CI: 0.73-1.74). In terms of interactions, no specifically increased odds were found, e.g. for low-educated adolescents with high-educated parents. There was no evidence for socioeconomic equalization regarding adolescents' mental health problems. Lower educated adolescents had substantially higher odds of having mental health problems, regardless of their parents' education. The odds may be affected by differences in intelligence and life events. Youth healthcare workers should collaborate closely with schools to intervene in time, particularly among low-educated adolescents. More interventions are probably needed to reduce these major inequities.

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

PubMed

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Prediction of hearing outcomes by multiple regression analysis in patients with idiopathic sudden sensorineural hearing loss.

PubMed

Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki

2014-12-01

This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.

Diagnostic and psychosocial differences in psychiatrically hospitalized military service members with single versus multiple suicide attempts.

PubMed

Kochanski-Ruscio, Kristen M; Carreno-Ponce, Jaime T; DeYoung, Kathryn; Grammer, Geoffrey; Ghahramanlou-Holloway, Marjan

2014-04-01

Individuals with multiple versus single suicide attempts present a more severe clinical picture and may be at greater risk for suicide. Yet group differences within military samples have been vastly understudied. The objective is to determine demographic, diagnostic, and psychosocial differences, based on suicide attempt status, among military inpatients admitted for suicide-related events. A retrospective chart review design was used with a total of 423 randomly selected medical records of psychiatric admissions to a military hospital from 2001 to 2006. Chi-square analyses indicated that individuals with multiple versus single suicide attempts were significantly more likely to have documented childhood sexual abuse (p =.025); problem substance use (p=.001); mood disorder diagnosis (p=.005); substance disorder diagnosis (p =.050); personality disorder not otherwise specified diagnosis (p =.018); and Axis II traits or diagnosis (p=.038) when compared to those with a single attempt history. Logistic regression analyses showed that males with multiple suicide attempts were more likely to have problem substance use (p=.005) and a mood disorder diagnosis (p =.002), while females with a multiple attempt history were more likely to have a history of childhood sexual (p =.027). Clinically meaningful differences among military inpatients with single versus multiple suicide attempts exist. Targeted Department of Defense suicide prevention and intervention efforts that address the unique needs of these two specific at-risk subgroups are additionally needed. Published by Elsevier Inc.

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

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

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

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

Use of Multiple Regression and Use-Availability Analyses in Determining Habitat Selection by Gray Squirrels (Sciurus Carolinensis)

Treesearch

John W. Edwards; Susan C. Loeb; David C. Guynn

1994-01-01

Multiple regression and use-availability analyses are two methods for examining habitat selection. Use-availability analysis is commonly used to evaluate macrohabitat selection whereas multiple regression analysis can be used to determine microhabitat selection. We compared these techniques using behavioral observations (n = 5534) and telemetry locations (n = 2089) of...

Marital and sexual satisfaction in Chinese families: exploring the moderating effects.

PubMed

Guo, Baorong; Huang, Jin

2005-01-01

This study examines the relationship between marital satisfaction and sexual satisfaction in Chinese families. Hierarchical multiple regression using data from the 1993 China Housing Survey indicates that, when controlling for the other variables, sexual satisfaction has considerable impact on marital satisfaction. We also found that the effects of sexual satisfaction on marital satisfaction are moderated by gender and education. The study suggests that marriage counseling, with an emphasis on promoting awareness of sexual quality, would be helpful in addressing marital problems in Chinese families.

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Building Regression Models: The Importance of Graphics.

ERIC Educational Resources Information Center

Dunn, Richard

1989-01-01

Points out reasons for using graphical methods to teach simple and multiple regression analysis. Argues that a graphically oriented approach has considerable pedagogic advantages in the exposition of simple and multiple regression. Shows that graphical methods may play a central role in the process of building regression models. (Author/LS)

Testing Different Model Building Procedures Using Multiple Regression.

ERIC Educational Resources Information Center

Thayer, Jerome D.

The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

Influence of Disruptive Behavior Disorders on Academic Performance and School Functions of Youths with Attention-Deficit/Hyperactivity Disorder.

PubMed

Liu, Chao-Yu; Huang, Wei-Lieh; Kao, Wei-Chih; Gau, Susan Shur-Fen

2017-12-01

Childhood attention-deficit/hyperactivity disorder (ADHD) and comorbid oppositional defiant disorder/conduct disorder (ODD/CD) are associated with negative school outcomes. The study aimed to examine the impact of ADHD and ODD/CD on various school functions. 395 youths with ADHD (244 with ADHD + ODD/CD and 151 with ADHD only) and 156 controls received semi-structured psychiatric interviews. School functions were assessed and compared between each group with a multiple-level model. The results showed that youths with ADHD had poorer performance across different domains of school functioning. Youths with ADHD + ODD/CD had more behavioral problems but similar academic performance than those with ADHD only. The multiple linear regression models revealed that ADHD impaired academic performance while ODD/CD aggravated behavioral problems. Our findings imply that comorbid ODD/CD may specifically contribute to social difficulties in youths with ADHD. Measures of early detection and intervention for ODD/CD should be conducted to prevent adverse outcomes.

Child, Parent, and Peer Predictors of Early-Onset Substance Use: A Multisite Longitudinal Study

PubMed Central

Kaplow, Julie B.; Curran, Patrick J.; Dodge, Kenneth A.

2009-01-01

The purpose of this study was to identify kindergarten-age predictors of early-onset substance use from demographic, environmental, parenting, child psychological, behavioral, and social functioning domains. Data from a longitudinal study of 295 children were gathered using multiple-assessment methods and multiple informants in kindergarten and 1st grade. Annual assessments at ages 10, 11, and 12 reflected that 21% of children reported having initiated substance use by age 12. Results from longitudinal logistic regression models indicated that risk factors at kindergarten include being male, having a parent who abused substances, lower levels of parental verbal reasoning, higher levels of overactivity, more thought problems, and more social problem solving skills deficits. Children with no risk factors had less than a 10% chance of initiating substance use by age 12, whereas children with 2 or more risk factors had greater than a 50% chance of initiating substance use. Implications for typology, etiology, and prevention are discussed. PMID:12041707

Disclosure of disease status among employed multiple sclerosis patients: association with negative work events and accommodations.

PubMed

Frndak, Seth E; Kordovski, Victoria M; Cookfair, Diane; Rodgers, Jonathan D; Weinstock-Guttman, Bianca; Benedict, Ralph H B

2015-02-01

Unemployment is common in multiple sclerosis (MS) and detrimental to quality of life. Studies suggest disclosure of diagnosis is an adaptive strategy for patients. However, the role of cognitive deficits and psychiatric symptoms in disclosure are not well studied. The goals of this paper were to (a) determine clinical factors most predictive of disclosure, and (b) measure the effects of disclosure on workplace problems and accommodations in employed patients. We studied two overlapping cohorts: a cross-sectional sample (n = 143) to determine outcomes associated with disclosure, and a longitudinal sample (n = 103) compared at four time points over one year on reported problems and accommodations. A case study of six patients, disclosing during monitoring, was also included. Disclosure was associated with greater physical disability but not cognitive impairment. Logistic regression predicting disclosure status retained physical disability, accommodations and years of employment (p < 0.0001). Disclosed patients reported more work problems and accommodations over time. The case study revealed that reasons for disclosing are multifaceted, including connection to employer, decreased mobility and problems at work. Although cognitive impairment is linked to unemployment, it does not appear to inform disclosure decisions. Early disclosure may help maintain employment if followed by appropriate accommodations. © The Author(s), 2014.

Poor sleep quality and nightmares are associated with non-suicidal self-injury in adolescents.

PubMed

Liu, Xianchen; Chen, Hua; Bo, Qi-Gui; Fan, Fang; Jia, Cun-Xian

2017-03-01

Non-suicidal self-injury (NSSI) is prevalent and is associated with increased risk of suicidal behavior in adolescents. This study examined which sleep variables are associated with NSSI, independently from demographics and mental health problems in Chinese adolescents. Participants consisted of 2090 students sampled from three high schools in Shandong, China and had a mean age of 15.49 years. Participants completed a sleep and health questionnaire to report their demographic and family information, sleep duration and sleep problems, impulsiveness, hopelessness, internalizing and externalizing problems, and NSSI. A series of regression analyses were conducted to examine the associations between sleep variables and NSSI. Of the sample, 12.6 % reported having ever engaged in NSSI and 8.8 % engaged during the last year. Univariate logistic analyses demonstrated that multiple sleep variables including short sleep duration, insomnia symptoms, poor sleep quality, sleep insufficiency, unrefreshed sleep, sleep dissatisfaction, daytime sleepiness, fatigue, snoring, and nightmares were associated with increased risk of NSSI. After adjusting for demographic and mental health variables, NSSI was significantly associated with sleeping <6 h per night, poor sleep quality, sleep dissatisfaction, daytime sleepiness, and frequent nightmares. Stepwise logistic regression model demonstrated that poor sleep quality (OR = 2.18, 95 % CI = 1.37-3.47) and frequent nightmares (OR = 2.88, 95 % CI = 1.45-5.70) were significantly independently associated with NSSI. In conclusion, while multiple sleep variables are associated with NSSI, poor sleep quality and frequent nightmares are independent risk factors of NSSI. These findings may have important implications for further research of sleep self-harm mechanisms and early detection and prevention of NSSI in adolescents.

Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

PubMed

Yang, Chi-Chuan; Chen, Yi-Hau; Chang, Hsing-Yi

2017-09-20

Childhood and adolescenthood overweight or obesity, which may be quantified through the body mass index (BMI), is strongly associated with adult obesity and other health problems. Motivated by the child and adolescent behaviors in long-term evolution (CABLE) study, we are interested in individual, family, and school factors associated with marginal quantiles of longitudinal adolescent BMI values. We propose a new method for composite marginal quantile regression analysis for longitudinal outcome data, which performs marginal quantile regressions at multiple quantile levels simultaneously. The proposed method extends the quantile regression coefficient modeling method introduced by Frumento and Bottai (Biometrics 2016; 72:74-84) to longitudinal data accounting suitably for the correlation structure in longitudinal observations. A goodness-of-fit test for the proposed modeling is also developed. Simulation results show that the proposed method can be much more efficient than the analysis without taking correlation into account and the analysis performing separate quantile regressions at different quantile levels. The application to the longitudinal adolescent BMI data from the CABLE study demonstrates the practical utility of our proposal. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Behavioral and psychosocial factors associated with suicidal ideation among adolescents.

PubMed

Lee, GyuYoung; Ham, Ok Kyung

2018-04-10

Suicidal ideation poses a serious threat to the well-being of adolescents and is the strongest risk factor for suicide. Indeed, Korea ranks first among Organisation for Economic Cooperation and Development countries regarding the age-standardized suicide rates. In the present study, we examined multiple levels of factors associated with the suicidal ideation of adolescents in Korea by applying the Ecological Models of Health Behavior. A cross-sectional study was conducted with a convenience sample of 860 adolescents. The instruments included the Beck Depression Inventory and the Adolescent Mental Health and Problem Behavior Questionnaire. The data were analyzed using hierarchical multiple regression. Sixteen percent of participants reported suicidal ideation. Intrapersonal (sleep disturbance, Internet game addiction, destructive behavior, and depressive symptoms) and interpersonal factors (family conflicts and peer victimization) were associated with suicidal ideation. Because multiple factors were associated with suicidal ideation among adolescents, both intrapersonal (sleep disturbance, Internet game addiction, and depression) and interpersonal factors (family conflicts and peer problems) should be considered in the development of suicide-prevention programs. These programs could include campaigns changing the norms (permissive attitudes toward school violence) and the development of strict and rigorous school non-violence policies. © 2018 John Wiley & Sons Australia, Ltd.

Preliminary evidence for good psychometric properties of the Norwegian version of the Brief Problems Monitor (BPM).

PubMed

Richter, Jörg

2015-04-01

Methods to assess intervention progress and outcome for frequent use are needed. To provide preliminary information about psychometric properties for the Norwegian version of the Brief Problems Monitor. Cronbach's alpha scores and intra-class correlation coefficients as indicators for internal consistency (reliability) and Pearson correlation coefficients between corresponding subscales of the long and short ASEBA form versions as well as multiple regression coefficients to explore the predictive power of the reduced item-set related to the corresponding scale-scores of the long version were calculated in large, representative data sets of Norwegian children and adolescents. Cronbach's alpha scores of the Norwegian version of the BPM subscales varied between 0.67 (attention BPM-youth) and 0.88 (attention BPM-teacher) and between 0.90 (BPM-youth) and 0.96 (BPM-teacher) for its total problem score. Corresponding subscales from the long versions and the BPM as well as the total problems scores were closely correlated with coefficients of high effect size (all r > 0.80). The variance of the items of the BPM explained about three-quarters or more of the variance in the corresponding subscales of the long version. The Norwegian BPM has good psychometric properties in terms of 1) being acceptable to good internal consistency and in terms of 2) regression coefficients of high effect size from the BPM items to the problem-scale scores of the long versions as validity indicators. Its use in clinical practice and research can be recommended.

Risk and protective factors for psychological distress among adolescents: a family study in the Nord-Trøndelag Health Study.

PubMed

Myklestad, Ingri; Røysamb, Espen; Tambs, Kristian

2012-05-01

The study aimed to investigate potential adolescent and parental psychosocial risk and protective factors for psychological distress among adolescents and, in addition, to examine potential gender and age differences in the effects of risk factors on adolescent psychological distress. Data were collected among 8,984 Norwegian adolescents (13-19 years) and their parents in the Nord-Trøndelag Health Study (HUNT). The outcome measure was psychological distress (SCL-5). Bivariate regression analysis with generalized estimating equation (GEE) model showed that all parental self-reported variables (mental distress, substance use, social network, economic problems, unemployment and family structure) and adolescents' self-reported variables (leisure activities, social support from friends, school-related problems and substance use) were significantly associated with psychological distress among adolescents. Results revealed that in a multiple regression analysis with a GEE model, adolescent psychosocial variables, specifically academic-related problems and being bullied at school, emerged as the strongest predictors of psychological distress among adolescents after controlling for age, gender, and all parental and adolescent variables. The following psychosocial risk factors were significantly more important for girl's psychological distress compared to boys: problems with academic achievement, conduct problems in school, frequency of being drunk, smoking, dissatisfaction in school, living alone and seen parents being drunk. Academic achievement and being bullied at school were the psychosocial factors most strongly associated with psychological distress among adolescents. Parental factors had an indirect effect on adolescent psychological distress, through adolescents' psychosocial factors.

Relationship between menopause and health-related quality of life in middle-aged Chinese women: a cross-sectional study.

PubMed

Liu, Kuo; He, Liu; Tang, Xun; Wang, Jinwei; Li, Na; Wu, Yiqun; Marshall, Roger; Li, Jingrong; Zhang, Zongxin; Liu, Jianjiang; Xu, Haitao; Yu, Liping; Hu, Yonghua

2014-01-10

Chinese menopausal women comprise a large population and the women in it experience menopausal symptoms in many different ways. Their health related quality of life (HRQOL) is not particularly well studied. Our study intends to evaluate the influence of menopause on HRQOL and explore other risk factors for HRQOL in rural China. An interview study was conducted from June to August 2010 in Beijing based on cross-sectional design. 1,351 women aged 40-59 were included in the study. HRQOL was measured using the EuroQol Group's 5-domain (EQ5D) questionnaire. Comparison of HRQOL measures (EQ5D index and EQ5D-VAS scores) was done between different menopausal groups. Logistic regression and multiple regression analysis were performed to adjust potential confounders and explore other risk factors for health problems and HRQOL measures. Postmenopausal women who had menopause for 2-5 years (+1b stage) were more likely to suffer mobility problems (OR = 1.835, p = 0.008) after multiple adjustment. Menopause was also related to impaired EQ5D index and EQ5D-VAS scores after adjustment for age. Among menopausal groups categorized by menopausal duration, a consistent decrement in EQ5D index and EQ5D-VAS scores, that is, worsening HRQOL, was observed (p < 0.05). Multiple regression analysis revealed low education level and physical activity were associated with EQ5D index (β = -0.080, p = 0.003, and β = 0.056, p = 0.040, respectively). Cigarette smoking and chronic disease were associated with EQ5D index (β = -0.135, p < 0.001 and β = -0.104, p < 0.001, respectively) and EQ5D-VAS (β = -0.057, P = 0.034 and β = -0.214, p < 0.001, respectively). Reduction in physical function was found within the first five years after menopause. Worsening EQ5D index and EQ5D-VAS scores were related to menopause. Education level, physical activity, cigarette smoking, and chronic disease history were associated with HRQOL in middle aged Chinese rural women.

Adolescents’ internalizing problems following traumatic brain injury are related to parents’ psychiatric symptoms

PubMed Central

Peterson, Robin L.; Kirkwood, Michael W.; Taylor, H. Gerry; Stancin, Terry; Brown, Tanya M.; Wade, Shari L.

2013-01-01

Background A small body of previous research has demonstrated that pediatric traumatic brain injury increases risk for internalizing problems, but findings have varied regarding their predictors and correlates. Methods We examined the level and correlates of internalizing symptoms in 130 teens who had sustained a complicated mild to severe TBI within the past 1 to 6 months. Internalizing problems were measured via both maternal and paternal report Child Behavior Checklist. We also measured family functioning, parent psychiatric symptoms, and post-injury teen neurocognitive function. Results Mean parental ratings of internalizing problems were within the normal range. Depending on informant, 22–26% of the sample demonstrated clinically elevated internalizing problems. In multiple and binary logistic regression models, only parent psychiatric symptoms consistently provided unique prediction of teen internalizing symptoms. For maternal but not paternal report, female gender was associated with greater internalizing problems. Conclusion Parent and teen emotional problems are associated following adolescent TBI. Possible reasons for this relationship, including the effects of TBI on the family unit, are discussed. PMID:22935574

Maternal acceptance and consistency of discipline as buffers of divorce stressors on children's psychological adjustment problems.

PubMed

Wolchik, S A; Wilcox, K L; Tein, J Y; Sandler, I N

2000-02-01

This study examines whether two aspects of mothering--acceptance and consistency of discipline--buffer the effect of divorce stressors on adjustment problems in 678 children, ages 8 to 15, whose families had divorced within the past 2 years. Children reported on divorce stressors; both mothers and children reported on mothering and internalizing and externalizing problems. Multiple regressions indicate that for maternal report of mothering, acceptance interacted with divorce stressors in predicting both dimensions of adjustment problems, with the pattern of findings supporting a stress-buffering effect. For child report of mothering, acceptance, consistency of discipline, and divorce stressors interacted in predicting adjustment problems. The relation between divorce stressors and internalizing and externalizing problems is stronger for children who report low acceptance and low consistency of discipline than for children who report either low acceptance and high consistency of discipline or high acceptance and low consistency of discipline. Children reporting high acceptance and high consistency of discipline have the lowest levels of adjustment problems. Implications of these results for understanding variability in children's postdivorce adjustment and interventions for divorced families are discussed.

Care Coordination for the Chronically Ill: Understanding the Patient's Perspective

PubMed Central

Maeng, Daniel D; Martsolf, Grant R; Scanlon, Dennis P; Christianson, Jon B

2012-01-01

Objective To identify factors associated with perception of care coordination problems among chronically ill patients. Methods Patient-level data were obtained from a random-digit dial telephone survey of adults with chronic conditions. The survey measured respondents' self-report of care coordination problems and level of patient activation, using the Patient Activation Measure (PAM-13). Logistic regression was used to assess association between respondents' self-report of care coordination problems and a set of patient characteristics. Results Respondents in the highest activation stage had roughly 30–40 percent lower odds of reporting care coordination problems compared to those in the lowest stage (p < .01). Respondents with multiple chronic conditions were significantly more likely to report coordination problems than those with hypertension only. Respondents' race/ethnicity, employment, insurance status, income, and length of illness were not significantly associated with self-reported care coordination problems. Conclusion We conclude that patient activation and complexity of chronic illness are strongly associated with patients' self-report of care coordination problems. Developing targeted strategies to improve care coordination around these patient characteristics may be an effective way to address the issue. PMID:22985032

The Relationship Between Age of Gambling Onset and Adolescent Problematic Gambling Severity

PubMed Central

Rahman, Ardeshir S.; Pilver, Corey E.; Desai, Rani A.; Steinberg, Marvin A.; Rugle, Loreen; Krishnan-Sarin, Suchitra; Potenza, Marc N.

2012-01-01

The aim of this study was to characterize the association between problem gambling severity and multiple health, functioning and gambling variables in adolescents aged 13–18 stratified by age of gambling onset. Survey data in 1624 Connecticut high school students stratified by age of gambling onset (≤11 years vs. ≥ 12 years) were analyzed in descriptive analyses and in logistic regression models. Earlier age of onset was associated with problem gambling severity as indexed by a higher frequency of at-risk/problem gambling (ARPG). Most health, functioning and gambling measures were similarly associated with problem gambling severity in the earlier- and later-age-of-gambling-onset groups with the exception of participation in non-strategic forms of gambling, which was more strongly associated with ARPG in the earlier-onset (OR=1.74, 95%CI=[1.26, 2.39]) as compared to later-onset (OR=0.94, 95%CI=[0.60, 1.48]) group (Interaction OR=1.91, 95%CI=[1.18, 3.26]). Post-hoc analysis revealed that earlier-onset ARPG was more strongly associated with multiple forms of non-strategic gambling including lottery (instant, traditional) and slot-machine gambling. The finding that problem gambling severity is more closely associated with multiple non-strategic forms of gambling amongst youth with earlier onset of gambling highlights the relevance of these types of youth gambling. The extent to which non-strategic forms of gambling may serve as a gateway to other forms of gambling or risk behaviors warrants additional study, and efforts targeting youth gambling should consider how best to address non-strategic gambling through education, prevention, treatment and policy efforts. PMID:22410208

Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration

NASA Technical Reports Server (NTRS)

Krishnamoorthy, K.; Kulkarni, Pandurang M.; Mathew, Thomas

1996-01-01

Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.

Performance of bias-correction methods for exposure measurement error using repeated measurements with and without missing data.

PubMed

Batistatou, Evridiki; McNamee, Roseanne

2012-12-10

It is known that measurement error leads to bias in assessing exposure effects, which can however, be corrected if independent replicates are available. For expensive replicates, two-stage (2S) studies that produce data 'missing by design', may be preferred over a single-stage (1S) study, because in the second stage, measurement of replicates is restricted to a sample of first-stage subjects. Motivated by an occupational study on the acute effect of carbon black exposure on respiratory morbidity, we compare the performance of several bias-correction methods for both designs in a simulation study: an instrumental variable method (EVROS IV) based on grouping strategies, which had been recommended especially when measurement error is large, the regression calibration and the simulation extrapolation methods. For the 2S design, either the problem of 'missing' data was ignored or the 'missing' data were imputed using multiple imputations. Both in 1S and 2S designs, in the case of small or moderate measurement error, regression calibration was shown to be the preferred approach in terms of root mean square error. For 2S designs, regression calibration as implemented by Stata software is not recommended in contrast to our implementation of this method; the 'problematic' implementation of regression calibration although substantially improved with use of multiple imputations. The EVROS IV method, under a good/fairly good grouping, outperforms the regression calibration approach in both design scenarios when exposure mismeasurement is severe. Both in 1S and 2S designs with moderate or large measurement error, simulation extrapolation severely failed to correct for bias. Copyright © 2012 John Wiley & Sons, Ltd.

Associations between yoga practice and joint problems: a cross-sectional survey among 9151 Australian women.

PubMed

Lauche, Romy; Schumann, Dania; Sibbritt, David; Adams, Jon; Cramer, Holger

2017-07-01

Yoga exercises have been associated with joint problems recently, indicating that yoga practice might be potentially dangerous for joint health. This study aimed to analyse whether regular yoga practice is associated with the frequency of joint problems in upper middle-aged Australian women. Women aged 62-67 years from the Australian Longitudinal Study on Women's Health (ALSWH) were questioned in 2013 whether they experienced regular joint pain or problems in the past 12 months and whether they regularly practiced yoga. Associations of joint problems with yoga practice were analysed using Chi-squared tests and multiple logistic regression modelling. Of 9151 women, 29.8% reported regular problems with stiff or painful joints, and 15.2, 11.9, 18.1 and 15.9% reported regular problems with shoulders, hips, knees and feet, respectively, in the past 12 months. Yoga was practiced sometimes by 10.1% and often by 8.4% of women. Practicing yoga was not associated with upper or lower limb joint problems. No association between yoga practice and joint problems has been identified. Further studies are warranted for conclusive judgement of benefits and safety of yoga in relation to joint problems.

The comparison of robust partial least squares regression with robust principal component regression on a real

NASA Astrophysics Data System (ADS)

Polat, Esra; Gunay, Suleyman

2013-10-01

One of the problems encountered in Multiple Linear Regression (MLR) is multicollinearity, which causes the overestimation of the regression parameters and increase of the variance of these parameters. Hence, in case of multicollinearity presents, biased estimation procedures such as classical Principal Component Regression (CPCR) and Partial Least Squares Regression (PLSR) are then performed. SIMPLS algorithm is the leading PLSR algorithm because of its speed, efficiency and results are easier to interpret. However, both of the CPCR and SIMPLS yield very unreliable results when the data set contains outlying observations. Therefore, Hubert and Vanden Branden (2003) have been presented a robust PCR (RPCR) method and a robust PLSR (RPLSR) method called RSIMPLS. In RPCR, firstly, a robust Principal Component Analysis (PCA) method for high-dimensional data on the independent variables is applied, then, the dependent variables are regressed on the scores using a robust regression method. RSIMPLS has been constructed from a robust covariance matrix for high-dimensional data and robust linear regression. The purpose of this study is to show the usage of RPCR and RSIMPLS methods on an econometric data set, hence, making a comparison of two methods on an inflation model of Turkey. The considered methods have been compared in terms of predictive ability and goodness of fit by using a robust Root Mean Squared Error of Cross-validation (R-RMSECV), a robust R2 value and Robust Component Selection (RCS) statistic.

Valid Statistical Analysis for Logistic Regression with Multiple Sources

NASA Astrophysics Data System (ADS)

Fienberg, Stephen E.; Nardi, Yuval; Slavković, Aleksandra B.

Considerable effort has gone into understanding issues of privacy protection of individual information in single databases, and various solutions have been proposed depending on the nature of the data, the ways in which the database will be used and the precise nature of the privacy protection being offered. Once data are merged across sources, however, the nature of the problem becomes far more complex and a number of privacy issues arise for the linked individual files that go well beyond those that are considered with regard to the data within individual sources. In the paper, we propose an approach that gives full statistical analysis on the combined database without actually combining it. We focus mainly on logistic regression, but the method and tools described may be applied essentially to other statistical models as well.

A principal component regression model to forecast airborne concentration of Cupressaceae pollen in the city of Granada (SE Spain), during 1995-2006.

PubMed

Ocaña-Peinado, Francisco M; Valderrama, Mariano J; Bouzas, Paula R

2013-05-01

The problem of developing a 2-week-on ahead forecast of atmospheric cypress pollen levels is tackled in this paper by developing a principal component multiple regression model involving several climatic variables. The efficacy of the proposed model is validated by means of an application to real data of Cupressaceae pollen concentration in the city of Granada (southeast of Spain). The model was applied to data from 11 consecutive years (1995-2005), with 2006 being used to validate the forecasts. Based on the work of different authors, factors as temperature, humidity, hours of sun and wind speed were incorporated in the model. This methodology explains approximately 75-80% of the variability in the airborne Cupressaceae pollen concentration.

Cumulative Risk and Impact Modeling on Environmental Chemical and Social Stressors.

PubMed

Huang, Hongtai; Wang, Aolin; Morello-Frosch, Rachel; Lam, Juleen; Sirota, Marina; Padula, Amy; Woodruff, Tracey J

2018-03-01

The goal of this review is to identify cumulative modeling methods used to evaluate combined effects of exposures to environmental chemicals and social stressors. The specific review question is: What are the existing quantitative methods used to examine the cumulative impacts of exposures to environmental chemical and social stressors on health? There has been an increase in literature that evaluates combined effects of exposures to environmental chemicals and social stressors on health using regression models; very few studies applied other data mining and machine learning techniques to this problem. The majority of studies we identified used regression models to evaluate combined effects of multiple environmental and social stressors. With proper study design and appropriate modeling assumptions, additional data mining methods may be useful to examine combined effects of environmental and social stressors.

Puzzling Findings in Studying the Outcome of “Real World” Adolescent Mental Health Services: The TRAILS Study

PubMed Central

Jörg, Frederike; Ormel, Johan; Reijneveld, Sijmen A.; Jansen, Daniëlle E. M. C.; Verhulst, Frank C.; Oldehinkel, Albertine J.

2012-01-01

Background The increased use and costs of specialist child and adolescent mental health services (MHS) urge us to assess the effectiveness of these services. The aim of this paper is to compare the course of emotional and behavioural problems in adolescents with and without MHS use in a naturalistic setting. Method and Findings Participants are 2230 (pre)adolescents that enrolled in a prospective cohort study, the TRacking Adolescents' Individual Lives Survey (TRAILS). Response rate was 76%, mean age at baseline 11.09 (SD 0.56), 50.8% girls. We used data from the first three assessment waves, covering a six year period. Multiple linear regression analysis, propensity score matching, and data validation were used to compare the course of emotional and behavioural problems of adolescents with and without MHS use. The association between MHS and follow-up problem score (β 0.20, SE 0.03, p-value<0.001) was not confounded by baseline severity, markers of adolescent vulnerability or resilience nor stressful life events. The propensity score matching strategy revealed that follow-up problem scores of non-MHS-users decreased while the problem scores of MHS users remained high. When taking into account future MHS (non)use, it appeared that problem scores decreased with limited MHS use, albeit not as much as without any MHS use, and that problem scores with continuous MHS use remained high. Data validation showed that using a different outcome measure, multiple assessment waves and multiple imputation of missing values did not alter the results. A limitation of the study is that, although we know what type of MHS participants used, and during which period, we lack information on the duration of the treatment. Conclusions The benefits of MHS are questionable. Replication studies should reveal whether a critical examination of everyday care is necessary or an artefact is responsible for these results. PMID:23028584

Puzzling findings in studying the outcome of "real world" adolescent mental health services: the TRAILS study.

PubMed

Jörg, Frederike; Ormel, Johan; Reijneveld, Sijmen A; Jansen, Daniëlle E M C; Verhulst, Frank C; Oldehinkel, Albertine J

2012-01-01

The increased use and costs of specialist child and adolescent mental health services (MHS) urge us to assess the effectiveness of these services. The aim of this paper is to compare the course of emotional and behavioural problems in adolescents with and without MHS use in a naturalistic setting. Participants are 2230 (pre)adolescents that enrolled in a prospective cohort study, the TRacking Adolescents' Individual Lives Survey (TRAILS). Response rate was 76%, mean age at baseline 11.09 (SD 0.56), 50.8% girls. We used data from the first three assessment waves, covering a six year period. Multiple linear regression analysis, propensity score matching, and data validation were used to compare the course of emotional and behavioural problems of adolescents with and without MHS use. The association between MHS and follow-up problem score (β 0.20, SE 0.03, p-value<0.001) was not confounded by baseline severity, markers of adolescent vulnerability or resilience nor stressful life events. The propensity score matching strategy revealed that follow-up problem scores of non-MHS-users decreased while the problem scores of MHS users remained high. When taking into account future MHS (non)use, it appeared that problem scores decreased with limited MHS use, albeit not as much as without any MHS use, and that problem scores with continuous MHS use remained high. Data validation showed that using a different outcome measure, multiple assessment waves and multiple imputation of missing values did not alter the results. A limitation of the study is that, although we know what type of MHS participants used, and during which period, we lack information on the duration of the treatment. The benefits of MHS are questionable. Replication studies should reveal whether a critical examination of everyday care is necessary or an artefact is responsible for these results.

Employment barriers, skills, and aspirations among unemployed job seekers with and without social anxiety disorder.

PubMed

Himle, Joseph A; Weaver, Addie; Bybee, Deborah; O'Donnell, Lisa; Vlnka, Sarah; Laviolette, Wayne; Steinberger, Edward; Golenberg, Zipora; Levine, Debra Siegel

2014-07-01

The literature has consistently demonstrated that social anxiety disorder has substantial negative impacts on occupational functioning. However, to date, no empirical work has focused on understanding the specific nature of vocational problems among persons with social anxiety disorder. This study examined the association between perceived barriers to employment, employment skills, and job aspirations and social anxiety among adults seeking vocational rehabilitation services. Data from intake assessments (June 2010-December 2011) of 265 low-income, unemployed adults who initiated vocational rehabilitation services in urban Michigan were examined to assess perceived barriers to employment, employment skills, job aspirations, and demographic characteristics among participants who did or did not screen positive for social anxiety disorder. Bivariate and multiple logistic regression analyses were performed. After adjustment for other factors, the multiple logistic regression analysis revealed that perceiving more employment barriers involving experience and skills, reporting fewer skills related to occupations requiring social skills, and having less education were significantly associated with social anxiety disorder. Participants who screened positive for social anxiety disorder were significantly less likely to aspire to social jobs. Employment-related characteristics that were likely to have an impact on occupational functioning were significantly different between persons with and without social anxiety problems. Identifying these differences in employment barriers, skills, and job aspirations revealed important information for designing psychosocial interventions for treatment of social anxiety disorder. The findings underscored the need for vocational services professionals to assess and address social anxiety among their clients.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

A Quantile Regression Approach to Understanding the Relations Between Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

PubMed Central

Tighe, Elizabeth L.; Schatschneider, Christopher

2015-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in Adult Basic Education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. PMID:25351773

the impact of personality on depression among university students in Taiwan.

PubMed

Chang, Shu-Man; Law, Daniel W; Chang, Her-Kun

2011-01-01

Depression in Taiwanese university students is a significant problem in terms of life and financial costs. The purpose of this study was to examine the impact of four selected personality traits, namely interpersonal problems, ideas of being persecuted, social students in introversion, and self depreciation, on the inclination to be depressed among students in Taiwanese university. A self-report survey was administered to students at a Taiwanese university and consisted of three parts: demographics, the Chinese version of the Basic Personality Inventory (BPI), and the Taiwanese Depression Questionnaire. The level of depression among students was assessed, and the relationships among the various variables were explored using analysis of variance (ANOVA) and regression. Altogether, 255 students successfully completed the survey. Overall, 37.62% of students were suffering from depression, including 4.7% who indicated that they were severely depressed, 18.30% who were moderately depressed, and 14% who were mildly depressed. In a multiple-regression model, ideas of being persecuted and self depreciation were both significant when predicting an inclination to be depressed. Depression is a problem for many university students in Taiwan. Understanding which personality traits are related to depression in Taiwanese students is important for student affair administrators and medical professionals and will help them to prevent and treat this debilitating illness.

Working memory dysfunctions predict social problem solving skills in schizophrenia.

PubMed

Huang, Jia; Tan, Shu-ping; Walsh, Sarah C; Spriggens, Lauren K; Neumann, David L; Shum, David H K; Chan, Raymond C K

2014-12-15

The current study aimed to examine the contribution of neurocognition and social cognition to components of social problem solving. Sixty-seven inpatients with schizophrenia and 31 healthy controls were administrated batteries of neurocognitive tests, emotion perception tests, and the Chinese Assessment of Interpersonal Problem Solving Skills (CAIPSS). MANOVAs were conducted to investigate the domains in which patients with schizophrenia showed impairments. Correlations were used to determine which impaired domains were associated with social problem solving, and multiple regression analyses were conducted to compare the relative contribution of neurocognitive and social cognitive functioning to components of social problem solving. Compared with healthy controls, patients with schizophrenia performed significantly worse in sustained attention, working memory, negative emotion, intention identification and all components of the CAIPSS. Specifically, sustained attention, working memory and negative emotion identification were found to correlate with social problem solving and 1-back accuracy significantly predicted the poor performance in social problem solving. Among the dysfunctions in schizophrenia, working memory contributed most to deficits in social problem solving in patients with schizophrenia. This finding provides support for targeting working memory in the development of future social problem solving rehabilitation interventions. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Fuzzy regression modeling for tool performance prediction and degradation detection.

PubMed

Li, X; Er, M J; Lim, B S; Zhou, J H; Gan, O P; Rutkowski, L

2010-10-01

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.

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

PubMed

Marill, Keith A

2004-01-01

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

Demographic characteristics, social competence, and behavior problems in children with gender identity disorder: a cross-national, cross-clinic comparative analysis.

PubMed

Cohen-Kettenis, Peggy T; Owen, Allison; Kaijser, Vanessa G; Bradley, Susan J; Zucker, Kenneth J

2003-02-01

This study examined demographic characteristics, social competence, and behavior problems in clinic-referred children with gender identity problems in Toronto, Canada (N = 358), and Utrecht, The Netherlands (N = 130). The Toronto sample was, on average, about a year younger than the Utrecht sample at referral, had a higher percentage of boys, had a higher mean IQ, and was less likely to be living with both parents. On the Child Behavior Checklist (CBCL), both groups showed, on average, clinical range scores in both social competence and behavior problems. A CBCL-derived measure of poor peer relations showed that boys in both clinics had worse ratings than did the girls. A multiple regression analysis showed that poor peer relations were the strongest predictor of behavior problems in both samples. This study-the first cross-national, cross-clinic comparative analysis of children with gender identity disorder-found far more similarities than differences in both social competence and behavior problems. The most salient demographic difference was age at referral. Cross-national differences in factors that might influence referral patterns are discussed.

Effects of prenatal marijuana exposure on child behavior problems at age 10.

PubMed

Goldschmidt, L; Day, N L; Richardson, G A

2000-01-01

This is a prospective study of the effects of prenatal marijuana exposure on child behavior problems at age 10. The sample consisted of low-income women attending a prenatal clinic. Half of the women were African-American and half were Caucasian. The majority of the women decreased their use of marijuana during pregnancy. The assessments of child behavior problems included the Child Behavior Checklist (CBCL), Teacher's Report Form (TRF), and the Swanson, Noland, and Pelham (SNAP) checklist. Multiple and logistic regressions were employed to analyze the relations between marijuana use and behavior problems of the children, while controlling for the effects of other extraneous variables. Prenatal marijuana use was significantly related to increased hyperactivity, impulsivity, and inattention symptoms as measured by the SNAP, increased delinquency as measured by the CBCL, and increased delinquency and externalizing problems as measured by the TRF. The pathway between prenatal marijuana exposure and delinquency was mediated by the effects of marijuana exposure on inattention symptoms. These findings indicate that prenatal marijuana exposure has an effect on child behavior problems at age 10.

Parenting approaches and digital technology use of preschool age children in a Chinese community.

PubMed

Wu, Cynthia Sau Ting; Fowler, Cathrine; Lam, Winsome Yuk Yin; Wong, Ho Ting; Wong, Charmaine Hei Man; Yuen Loke, Alice

2014-05-07

Young children are using digital technology (DT) devices anytime and anywhere, especially with the invention of smart phones and the replacement of desktop computers with digital tablets. Although research has shown that parents play an important role in fostering and supporting preschoolers' developing maturity and decisions about DT use, and in protecting them from potential risk due to excessive DT exposure, there have been limited studies conducted in Hong Kong focusing on parent-child DT use. This study had three objectives: 1) to explore parental use of DTs with their preschool children; 2) to identify the DT content that associated with child behavioral problems; and 3) to investigate the relationships between approaches adopted by parents to control children's DT use and related preschooler behavioral problems. This exploratory quantitative study was conducted in Hong Kong with 202 parents or guardians of preschool children between the ages of 3 and 6 attending kindergarten. The questionnaire was focused on four aspects, including 1) participants' demographics; 2) pattern of DT use; 3) parenting approach to manage the child's DT use; and 4) child behavioral and health problems related to DT use. Multiple regression analysis was adopted as the main data analysis method for identifying the DT or parental approach-related predictors of the preschooler behavioral problems. In the multiple linear regression model, the 'restrictive approach score' was the only predictor among the three parental approaches (B:1.66, 95% CI: [0.21, 3.11], p < 0.05). Moreover, the viewing of antisocial behavior cartoons by children also significantly increased the tendency of children to have behavioral problem (B:3.84, 95% CI: [1.66, 6.02], p < 0.01). Since preschool children's cognitive and functional abilities are still in the developmental stage, parents play a crucial role in fostering appropriate and safe DT use. It is suggested that parents practice a combination of restrictive, instructive and co-using approaches, rather than a predominately restrictive approach, to facilitate their child's growth and development. Further studies are needed to explore the parent-child relationship and parents' self-efficacy when managing the parent-child DT use, to develop strategies to guide children in healthy DT use.

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

2010-01-01

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

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

PubMed

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

2018-02-01

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

Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test

PubMed Central

Zhao, Ni; Chen, Jun; Carroll, Ian M.; Ringel-Kulka, Tamar; Epstein, Michael P.; Zhou, Hua; Zhou, Jin J.; Ringel, Yehuda; Li, Hongzhe; Wu, Michael C.

2015-01-01

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals’ microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. “Optimal” MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for. PMID:25957468

Contributions to "k"-Means Clustering and Regression via Classification Algorithms

ERIC Educational Resources Information Center

Salman, Raied

2012-01-01

The dissertation deals with clustering algorithms and transforming regression problems into classification problems. The main contributions of the dissertation are twofold; first, to improve (speed up) the clustering algorithms and second, to develop a strict learning environment for solving regression problems as classification tasks by using…

On the reliable and flexible solution of practical subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm for solving subset regression problems is described. The algorithm performs a QR decomposition with a new column-pivoting strategy, which permits subset selection directly from the originally defined regression parameters. This, in combination with a number of extensions of the new technique, makes the method a very flexible tool for analyzing subset regression problems in which the parameters have a physical meaning.

The impact of early behavior disturbances on academic achievement in high school.

PubMed

Breslau, Joshua; Miller, Elizabeth; Breslau, Naomi; Bohnert, Kipling; Lucia, Victoria; Schweitzer, Julie

2009-06-01

Previous research has indicated that childhood behavioral disturbances predict lower scores on academic tests and curtail educational attainment. It is unknown which types of childhood behavioral problems are most likely to predict these outcomes. An ethnically diverse cohort was assessed at 6 years of age for behavioral problems and IQ and at 17 years of age for academic achievement in math and reading. Of the original cohort of 823 children, 693 (84%) had complete data. Multiple regressions were used to estimate associations of attention and internalizing and externalizing problems at age 6 and with math and reading achievement at age 17, adjusting for IQ and indicators of family socioeconomic status. Adjusting for IQ, inner-city community, and maternal education and marital status, teacher ratings of attention, internalizing behavior, and externalizing problems at age 6 significantly predict math and reading achievement at age 17. When types of problems are examined simultaneously, attention problems predict math and reading achievement with little attenuation, whereas the influence of externalizing and internalizing problems is materially reduced and not significant. Interventions that target attention problems at school entry should be tested as a potential avenue for improving educational achievement.

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

Sample size determination for logistic regression on a logit-normal distribution.

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

The relationship between satisfaction with life, ADHD symptoms, and associated problems among university students.

PubMed

Gudjonsson, Gisli H; Sigurdsson, Jon Fridrik; Eyjolfsdottir, Gudrun Agusta; Smari, Jakob; Young, Susan

2009-05-01

To ascertain whether ADHD symptoms, and associated problems, are negatively related to subjective well-being. The Satisfaction With Life Scale (SWLS) was completed by 369 university students, along with the Reasoning & Rehabilitation (R&R) ADHD Training Evaluation (RATE), the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) Scale for current ADHD symptoms, and the Depression Anxiety Stress Scales (DASS). The SWLS was negatively correlated with all the other measures, and the strongest correlations were with the Total RATE score. A multiple regression analysis showed that the variables in the study accounted for 22% and 25% of the variance of the SWLS among males and females, respectively. Among males poor social functioning was the best predictor of dissatisfaction with life, whereas among females it was poor emotional control. Both ADHD symptoms and associated problems are significantly related to poorer satisfaction with life.

Lay beliefs about the efficacy of self-reliance, seeking help and external control as strategies for overcoming obesity, drug addiction, marital problems, stuttering and insomnia.

PubMed

Furnham, Adrian; McDermott, Mark R

1994-07-01

This study was concerned with peoples' beliefs about the importance of twenty-four different contributors towards overcoming five relatively common personal health problems, namely: obesity, drug addiction, marital difficulties, stuttering and insomnia. One hundred and twenty-two subjects completed a five-page questionnaire indicating how effective each of these contributors were to overcoming the problems as specified. Factor analysis revealed an interpretable structure similar to previous studies (Luk and Bond, 1992): the emerging three factors were labelled ' self-reliance", "seeking help" and "external control". Multiple regression showed that few individual difference variables as measured were related to perceived relevance of the different contributors. The results were discussed in terms of subjects' beliefs concerning the value of self-reliance as opposed to seeking help, and in relation to the importance of understanding lay beliefs about the efficacy of different forms of intervention.

Factors associated with perception of singing voice handicap.

PubMed

Cohen, Seth M; Noordzij, J Pieter; Garrett, C Gaelyn; Ossoff, Robert H

2008-04-01

This study will determine factors that influence the self-perceived handicap associated with singing voice problems. A prospective cohort. Singers presenting to a voice clinic prospectively completed the Singing Voice Handicap Index (SVHI) before evaluation and treatment. Demographic data, singing style, professional status, duration of symptoms, medical problems, and diagnosis were collected. Univariate and multivariate analysis was performed. One hundred seventy-one singers completed the SVHI. The duration of symptoms, being an amateur singer or singing teacher, benign vocal fold lesions, and neurologic voice disorders were associated with increased SVHI scores (P < 0.05, multiple linear regression). Age greater than 50 years and gospel singing were predictive of increased SVHI scores only on univariate analysis (P < 0.05, t test). Singers experience significant handicap as a result of their singing problems with certain factors associated with greater impairment. Targeting interventions at patients more severely affected may improve outcomes.

Motivation Types and Mental Health of UK Hospitality Workers.

PubMed

Kotera, Yasuhiro; Adhikari, Prateek; Van Gordon, William

2018-01-01

The primary purposes of this study were to (i) assess levels of different types of work motivation in a sample of UK hospitality workers and make a cross-cultural comparison with Chinese counterparts and (ii) identify how work motivation and shame-based attitudes towards mental health explain the variance in mental health problems in UK hospitality workers. One hundred three UK hospitality workers completed self-report measures, and correlation and multiple regression analyses were conducted to identify significant relationships. Findings demonstrate that internal and external motivation levels were higher in UK versus Chinese hospitality workers. Furthermore, external motivation was more significantly associated with shame and mental health problems compared to internal motivation. Motivation accounted for 34-50% of mental health problems. This is the first study to explore the relationship between motivation, shame, and mental health in UK hospitality workers. Findings suggest that augmenting internal motivation may be a novel means of addressing mental health problems in this worker population.

Back disorders and health problems among subway train operators exposed to whole-body vibration.

PubMed

Johanning, E

1991-12-01

Back disease associated with whole-body vibration has not been evaluated for subway train operators. A recent study demonstrated that this group is exposed to whole-body vibration at levels above the international standard. To investigate this risk further, a self-administered questionnaire survey was conducted among subway train operators (N = 492) and a similar reference group (N = 92). The operators had a higher prevalence than the referents in all aspects of back problems, particularly for cervical and lower back pain. In a multiple logistic regression model, the odds ratio for sciatic pain among subway train operators was 3.9 (95% CI 1.7-8.6); the operators also had a higher risk of hearing-related problems (odds ratio 3.2, 95% CI 0.6-17.4) and of gastrointestinal problems (odds ratio 1.6, 95% CI 1.1-2.5). Although a cumulative dose-response relationship could not be statistically demonstrated, the findings appear to be related to exposure to whole-body vibration and inadequate ergonomic conditions.

Interactions between child and parent temperament and child behavior problems.

PubMed

Rettew, David C; Stanger, Catherine; McKee, Laura; Doyle, Alicia; Hudziak, James J

2006-01-01

Few studies of temperament have tested goodness-of-fit theories of child behavior problems. In this study, we test the hypothesis that interactions between child and parent temperament dimensions predict levels of child psychopathology after controlling for the effects of these dimensions individually. Temperament and psychopathology were assessed in a total of 175 children (97 boys, 78 girls; mean age, 10.99 years; SD, 3.66 years) using composite scores from multiple informants of the Junior Temperament and Character Inventory and the Achenbach System of Empirically Based Assessment. Parent temperament was assessed using the adult version of the Temperament and Character Inventory. Statistical analyses included multiple regression procedures to assess the contribution of child-parent temperament interactions after controlling for demographic variables, other types of child psychopathology, and the individual Temperament and Character Inventory and Junior Temperament and Character Inventory dimensions. Interactions between child and parent temperament dimensions predicted higher levels of externalizing, internalizing, and attention problems over and above the effects of these dimensions alone. Among others, the combination of high child novelty seeking with high maternal novelty was associated with child attention problems, whereas the combination of high child harm avoidance and high father harm avoidance was associated with increased child internalizing problems. Many child temperament dimensions also exerted significant effects independently. The association between a child temperament trait and psychopathology can be dependent upon the temperament of parents. These data lend support to previous theories of the importance of goodness-of-fit.

Tuning Parameters in Heuristics by Using Design of Experiments Methods

NASA Technical Reports Server (NTRS)

Arin, Arif; Rabadi, Ghaith; Unal, Resit

2010-01-01

With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently.

A Quantile Regression Approach to Understanding the Relations Among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students.

PubMed

Tighe, Elizabeth L; Schatschneider, Christopher

2016-07-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82%-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. © Hammill Institute on Disabilities 2014.

Algorithm For Solution Of Subset-Regression Problems

NASA Technical Reports Server (NTRS)

Verhaegen, Michel

1991-01-01

Reliable and flexible algorithm for solution of subset-regression problem performs QR decomposition with new column-pivoting strategy, enables selection of subset directly from originally defined regression parameters. This feature, in combination with number of extensions, makes algorithm very flexible for use in analysis of subset-regression problems in which parameters have physical meanings. Also extended to enable joint processing of columns contaminated by noise with those free of noise, without using scaling techniques.

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study

NASA Astrophysics Data System (ADS)

Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah

2017-08-01

In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.

Sex of the GP--20 years on.

PubMed

Harrison, Christopher M; Britt, Helena C; Charles, Janice

2011-08-15

Previous research with the Australian Morbidity and Treatment Survey (1990-1991) showed significant differences in general practitioner characteristics and patient mix of male and female GPs. Even after adjusting for these, it was seen that male and female GPs managed different types of medical conditions. The proportion of female GPs increased from 19.6% in 1990-1991 to 37.1% in 2009-2010. This study investigates whether differences remain two decades later. Analysis of 2009-2010 Bettering the Evaluation and Care of Health (BEACH) data examining GP characteristics, patient encounter characteristics, patient reasons for encounter (RFE), problem types managed and management methods used, by GP sex. Whether GP sex was an independent predictor of problem types being managed, or management methods used, was tested using multiple logistic regressions and Poisson regression. 988 GPs recorded 98 800 GP-patient encounters. Adjusted differences in clinical activity of male and female GPs. After adjustment, compared with male GPs, females recorded more RFEs about general and unspecified issues and endocrine, female genital, pregnancy and family planning problems; and fewer concerning the musculoskeletal, respiratory, skin and male genital systems. Female GPs managed more general and unspecified, digestive, circulatory, psychological, endocrine, female genital and social problems; recorded nearly 20% more clinical treatments and referrals; recorded nearly 10% more imaging and pathology tests; and 4.3% fewer medications. After two decades, even with increased numbers of female GPs, the differences in problems managed by male and female GPs remain, and will probably continue. Female GPs use more resources per encounter, but may not use more resources in terms of annual patient care.

Examining the Matthew effect on the motivation and ability to stay at work after heart disease.

PubMed

Meland, Eivind; Grønhaug, Siri; Oystese, Kristin; Mildestvedt, Thomas

2011-07-01

Cardiac rehabilitation should safeguard that socioeconomic factors or other differences that affect people's cardiovascular health are not further aggravated after healthcare treatment. The study examines whether socioeconomic status, emotional problems, or the severity of disease affect people's ability to continue to work after heart disease. We also examined if these effects can be explained by differences in motivational factors. 217 patients (41 women) from the Krokeide Rehabilitation Centre in Bergen participated. Multiple linear regression analysis was used to examine motivational differences, and logistic regression analysis was used to examine whether socioeconomic factors or other differences affected people's ability to continue to work after heart disease. Self-efficacy for future work strongly impacted the likelihood of being incapacitated for work during the 2-year follow-up. The household's total income and emotional problems were statistically significant related to patients dropping out from work in the course of the observation. The association between emotional problems and future work was mediated by motivational problems. The relation between income and future incapacity for work could not be explained by motivational factors. The study shows a clear Matthew effect on people's ability to continue to work after heart disease as low-income groups and people with emotional problems are more at risk of dropping out of work. This Matthew effect was, however, only explained by the motivational difficulties for the association between emotional distress and dropping out of work and not for the impact of household income on the likelihood of leaving work.

A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy.

PubMed

Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

2017-09-01

Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula "mFIM at discharge = mFIM effectiveness × (91 points - mFIM at admission) + mFIM at admission" was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Epidemiology of multiple childhood traumatic events: child abuse, parental psychopathology, and other family-level stressors.

PubMed

Menard, C B; Bandeen-Roche, K J; Chilcoat, H D

2004-11-01

Multiple family-level childhood stressors are common and are correlated. It is unknown if clusters of commonly co-occurring stressors are identifiable. The study was designed to explore family-level stressor clustering in the general population, to estimate the prevalence of exposure classes, and to examine the correlation of sociodemographic characteristics with class prevalence. Data were collected from an epidemiological sample and analyzed using latent class regression. A six-class solution was identified. Classes were characterized by low risk (prevalence=23%), universal high risk (7 %), family conflict (11 %), household substance problems (22 %), non-nuclear family structure (24 %), parent's mental illness (13 %). Class prevalence varied with race and welfare status, not gender. Interventions for childhood stressors are person-focused; the analytic approach may uniquely inform resource allocation.

Cephalometric landmark detection in dental x-ray images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Lee, Hansang; Park, Minseok; Kim, Junmo

2017-03-01

In dental X-ray images, an accurate detection of cephalometric landmarks plays an important role in clinical diagnosis, treatment and surgical decisions for dental problems. In this work, we propose an end-to-end deep learning system for cephalometric landmark detection in dental X-ray images, using convolutional neural networks (CNN). For detecting 19 cephalometric landmarks in dental X-ray images, we develop a detection system using CNN-based coordinate-wise regression systems. By viewing x- and y-coordinates of all landmarks as 38 independent variables, multiple CNN-based regression systems are constructed to predict the coordinate variables from input X-ray images. First, each coordinate variable is normalized by the length of either height or width of an image. For each normalized coordinate variable, a CNN-based regression system is trained on training images and corresponding coordinate variable, which is a variable to be regressed. We train 38 regression systems with the same CNN structure on coordinate variables, respectively. Finally, we compute 38 coordinate variables with these trained systems from unseen images and extract 19 landmarks by pairing the regressed coordinates. In experiments, the public database from the Grand Challenges in Dental X-ray Image Analysis in ISBI 2015 was used and the proposed system showed promising performance by successfully locating the cephalometric landmarks within considerable margins from the ground truths.

UCODE, a computer code for universal inverse modeling

USGS Publications Warehouse

Poeter, E.P.; Hill, M.C.

1999-01-01

This article presents the US Geological Survey computer program UCODE, which was developed in collaboration with the US Army Corps of Engineers Waterways Experiment Station and the International Ground Water Modeling Center of the Colorado School of Mines. UCODE performs inverse modeling, posed as a parameter-estimation problem, using nonlinear regression. Any application model or set of models can be used; the only requirement is that they have numerical (ASCII or text only) input and output files and that the numbers in these files have sufficient significant digits. Application models can include preprocessors and postprocessors as well as models related to the processes of interest (physical, chemical and so on), making UCODE extremely powerful for model calibration. Estimated parameters can be defined flexibly with user-specified functions. Observations to be matched in the regression can be any quantity for which a simulated equivalent value can be produced, thus simulated equivalent values are calculated using values that appear in the application model output files and can be manipulated with additive and multiplicative functions, if necessary. Prior, or direct, information on estimated parameters also can be included in the regression. The nonlinear regression problem is solved by minimizing a weighted least-squares objective function with respect to the parameter values using a modified Gauss-Newton method. Sensitivities needed for the method are calculated approximately by forward or central differences and problems and solutions related to this approximation are discussed. Statistics are calculated and printed for use in (1) diagnosing inadequate data or identifying parameters that probably cannot be estimated with the available data, (2) evaluating estimated parameter values, (3) evaluating the model representation of the actual processes and (4) quantifying the uncertainty of model simulated values. UCODE is intended for use on any computer operating system: it consists of algorithms programmed in perl, a freeware language designed for text manipulation and Fortran90, which efficiently performs numerical calculations.

Use of Empirical Estimates of Shrinkage in Multiple Regression: A Caution.

ERIC Educational Resources Information Center

Kromrey, Jeffrey D.; Hines, Constance V.

1995-01-01

The accuracy of four empirical techniques to estimate shrinkage in multiple regression was studied through Monte Carlo simulation. None of the techniques provided unbiased estimates of the population squared multiple correlation coefficient, but the normalized jackknife and bootstrap techniques demonstrated marginally acceptable performance with…

Enhance-Synergism and Suppression Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, W. Michael

2004-01-01

Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…

A simulation study on Bayesian Ridge regression models for several collinearity levels

NASA Astrophysics Data System (ADS)

Efendi, Achmad; Effrihan

2017-12-01

When analyzing data with multiple regression model if there are collinearities, then one or several predictor variables are usually omitted from the model. However, there sometimes some reasons, for instance medical or economic reasons, the predictors are all important and should be included in the model. Ridge regression model is not uncommon in some researches to use to cope with collinearity. Through this modeling, weights for predictor variables are used for estimating parameters. The next estimation process could follow the concept of likelihood. Furthermore, for the estimation nowadays the Bayesian version could be an alternative. This estimation method does not match likelihood one in terms of popularity due to some difficulties; computation and so forth. Nevertheless, with the growing improvement of computational methodology recently, this caveat should not at the moment become a problem. This paper discusses about simulation process for evaluating the characteristic of Bayesian Ridge regression parameter estimates. There are several simulation settings based on variety of collinearity levels and sample sizes. The results show that Bayesian method gives better performance for relatively small sample sizes, and for other settings the method does perform relatively similar to the likelihood method.

Hurricane Katrina’s Impact on the Mental Health of Adolescent Female Offenders

PubMed Central

Robertson, Angela A.; Morse, David T.; Baird-Thomas, Connie

2008-01-01

Exposure to multiple traumatic events and high rates of mental health problems are common among juvenile offenders. This study draws on Conservation of Resources (COR) stress theory to examine the impact of a specific trauma, Hurricane Katrina, relative to other adverse life events on the mental health of female adolescent offenders in Mississippi. Teenage girls (N = 258, 69% African American) were recruited from 4 juvenile detention centers and the state training school. Participants were interviewed about the occurrence and timing of adverse life events and hurricane-related experiences and completed a self-administered mental health assessment. Hierarchical linear regression models were used to identify predictors of anxiety and depression. Pre-hurricane family stressors, pre-hurricane traumatic events, hurricane-related property damage, and receipt of hurricane-related financial assistance significantly predicted symptoms of anxiety and depression. Findings support COR theory. Family stressors had the greatest influence on symptoms of anxiety and depression, highlighting the need for family-based services that address the multiple, inter-related problems and challenges in the lives of female juvenile offenders. PMID:19296263

Anxiety and posttraumatic stress symptom pathways to substance use problems among community women experiencing intimate partner violence.

PubMed

Jaquier, Véronique; Flanagan, Julianne C; Sullivan, Tami P

2015-01-01

Although intimate partner violence (IPV) has demonstrated strong associations with anxiety and posttraumatic stress, these constructs have rarely been examined simultaneously in IPV research. Gaps in knowledge remain as to their differential associations to substance use problems among IPV-victimized women. A sample of 143 community women self-reported on their current IPV victimization, mental health and substance use problems. Hierarchical entry multiple regressions were used to test for the direct and indirect effects of psychological, physical, and sexual IPV to alcohol and drug problems through anxiety and posttraumatic stress. Higher anxiety symptom severity and higher physical IPV severity were associated with greater alcohol and drug problems. Higher posttraumatic stress symptom severity was associated with greater alcohol and drug problems. Mediation analyses indicated (i) significant indirect pathways of IPV types to alcohol problems through posttraumatic stress symptom severity controlling for anxiety symptom severity and (ii) significant indirect pathways of IPV types to drug problems through anxiety symptom severity controlling for posttraumatic stress symptom severity. In examining the indirect pathways of psychological, physical, and sexual IPV to substance use problems this study highlights that anxiety and posttraumatic stress symptom severity have unique effects on alcohol and drug problems among IPV-victimized women.

PROSPECTIVE ASSOCIATIONS OF DEPRESSIVE RUMINATION AND SOCIAL PROBLEM SOLVING WITH DEPRESSION: A 6-MONTH LONGITUDINAL STUDY(.).

PubMed

Hasegawa, Akira; Hattori, Yosuke; Nishimura, Haruki; Tanno, Yoshihiko

2015-06-01

The main purpose of this study was to examine whether depressive rumination and social problem solving are prospectively associated with depressive symptoms. Nonclinical university students (N = 161, 64 men, 97 women; M age = 19.7 yr., SD = 3.6, range = 18-61) recruited from three universities in Japan completed the Beck Depression Inventory-Second Edition (BDI-II), the Ruminative Responses Scale, Social Problem-Solving Inventory-Revised Short Version (SPSI-R:S), and the Means-Ends Problem-Solving Procedure at baseline, and the BDI-II again at 6 mo. later. A stepwise multiple regression analysis with the BDI-II and all subscales of the rumination and social problem solving measures as independent variables indicated that only the BDI-II scores and the Impulsivity/carelessness style subscale of the SPSI-R:S at Time 1 were significantly associated with BDI-II scores at Time 2 (β = 0.73, 0.12, respectively; independent variables accounted for 58.8% of the variance). These findings suggest that in Japan an impulsive and careless problem-solving style was prospectively associated with depressive symptomatology 6 mo. later, as contrasted with previous findings of a cycle of rumination and avoidance problem-solving style.

Infant malnutrition predicts conduct problems in adolescents

PubMed Central

Galler, Janina R.; Bryce, Cyralene P.; Waber, Deborah P.; Hock, Rebecca S.; Harrison, Robert; Eaglesfield, G. David; Fitzmaurice, Garret

2013-01-01

Objectives The purpose of this study was to compare the prevalence of conduct problems in a well-documented sample of Barbadian adolescents malnourished as infants and a demographic comparison group and to determine the extent to which cognitive impairment and environmental factors account for this association. Methods Behavioral symptoms were assessed using a 76-item self-report scale in 56 Barbadian youth (11–17 years of age) with histories of protein–energy malnutrition (PEM) limited to the first year of life and 60 healthy classmates. Group comparisons were carried out by longitudinal and cross-sectional multiple regression analyses at 3 time points in childhood and adolescence. Results Self-reported conduct problems were more prevalent among previously malnourished youth (P < 0.01). Childhood IQ and home environmental circumstances partially mediated the association with malnutrition. Teacher-reported classroom behaviors at earlier ages were significantly correlated with youth conduct problems, confirming the continuity of conduct problems through childhood and adolescence. Discussion Self-reported conduct problems are elevated in children and adolescents with histories of early childhood malnutrition. Later vulnerability to increased conduct problems appears to be mediated by the more proximal neurobehavioral effects of the malnutrition on cognitive function and by adverse conditions in the early home environment. PMID:22584048

Infant malnutrition predicts conduct problems in adolescents.

PubMed

Galler, Janina R; Bryce, Cyralene P; Waber, Deborah P; Hock, Rebecca S; Harrison, Robert; Eaglesfield, G David; Fitzmaurice, Garret

2012-07-01

The purpose of this study was to compare the prevalence of conduct problems in a well-documented sample of Barbadian adolescents malnourished as infants and a demographic comparison group and to determine the extent to which cognitive impairment and environmental factors account for this association. Behavioral symptoms were assessed using a 76-item self-report scale in 56 Barbadian youth (11-17 years of age) with histories of protein-energy malnutrition (PEM) limited to the first year of life and 60 healthy classmates. Group comparisons were carried out by longitudinal and cross-sectional multiple regression analyses at 3 time points in childhood and adolescence. Self-reported conduct problems were more prevalent among previously malnourished youth (P < 0.01). Childhood IQ and home environmental circumstances partially mediated the association with malnutrition. Teacher-reported classroom behaviors at earlier ages were significantly correlated with youth conduct problems, confirming the continuity of conduct problems through childhood and adolescence. Self-reported conduct problems are elevated in children and adolescents with histories of early childhood malnutrition. Later vulnerability to increased conduct problems appears to be mediated by the more proximal neurobehavioral effects of the malnutrition on cognitive function and by adverse conditions in the early home environment.

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

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

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.

2008-01-01

This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

This memorandum gives instructions for the use and operation of a revised version of RAWS, a multiple regression analysis program. The program...of preprocessed data, the directed retention of variable, listing of the matrix of the normal equations and its inverse, and the bypassing of the regression analysis to provide the input variable statistics only. (Author)

Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

DTIC Science & Technology

2017-03-23

PUBLIC RELEASE; DISTRIBUTION UNLIMITED Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and... Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle Follow this and additional works at: https://scholar.afit.edu...afit.edu. Recommended Citation Trudelle, Ryan C., "Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and

Forgiveness Working: Forgiveness, Health, and Productivity in the Workplace.

PubMed

Toussaint, Loren; Worthington, Everett L; Van Tongeren, Daryl R; Hook, Joshua; Berry, Jack W; Shivy, Victoria A; Miller, Andrea J; Davis, Don E

2018-01-01

Associations between forgiveness and health promotion in the workplace were examined as mediating effects of workplace interpersonal stress. Cross-sectional. Multiple Washington, DC, office-based and Midwestern manufacturing workplaces. Study 1: 108 employees (40 males and 68 females); mean age was 32.4 years. Study 2: 154 employees (14 males and 140 females); mean age was 43.9 years. Questionnaires measured forgiveness, unproductivity, absenteeism, stress, and health problems. Bivariate and multiple correlation/regression and structural equation models were used. Indirect effects were estimated with bootstrapping methods. In study 1, forgiveness of a specific workplace offense was inversely associated with unproductivity ( r = -.35, P < .001) and mental ( r = -.32, P = .001) and physical ( r = -.19, P = .044) health problems. In study 2, trait forgiveness was inversely associated with unproductivity (β = -.20, P = .016) and mental (β = -.31, P < .001) and physical health problems (β = -.28, P = .001), and workplace interpersonal stress partially mediated these associations (indirect effects = -.03, -.04, -.05, respectively). The association of forgiveness and occupational outcomes is robust. Forgiveness may be associated with outcomes by (at least partially) reducing stress related to workplace offenses. Forgiveness may be an effective means of coping following being emotionally hurt on the job that may promote good health, well-being, and productivity.

The influence of family stability on self-control and adjustment.

PubMed

Malatras, Jennifer Weil; Israel, Allen C

2013-07-01

The aim of the present study was to replicate previous evidence for a model in which self-control mediates the relationship between family stability and internalizing symptoms, and to evaluate a similar model with regard to externalizing problems. Participants were 155 female and 134 male undergraduates--mean age of 19.03 years. Participants completed measures of stability in the family of origin (Stability of Activities in the Family Environment), self-control (Self-Control scale), current externalizing (Adult Self-Report), and internalizing problems (Beck Depression Inventory II and Beck Anxiety Inventory). Multiple regression analyses largely support the proposed model for both the externalizing and internalizing domains. Family stability may foster the development of self-control and, in turn, lead to positive adjustment. © 2012 Wiley Periodicals, Inc.

Problems with change in R2 as applied to theory of reasoned action research.

PubMed

Trafimow, David

2004-12-01

The paradigm of choice for theory of reasoned action research seems to depend largely on the notion of change in variance accounted for (DeltaR2) as new independent variables are added to a multiple regression equation. If adding a particular independent variable of interest increases the variance in the dependent variable that can be accounted for by the list of independent variables, then the research is deemed to be 'successful', and the researcher is considered to have made a convincing argument about the importance of the new variable. In contrast to this trend, I present arguments that suggest serious problems with the paradigm, and conclude that studies on attitude-behaviour relations would advance the field of psychology to a far greater extent if researchers abandoned it.

Impulsive responses to emotion as a transdiagnostic vulnerability to internalizing and externalizing symptoms.

PubMed

Johnson, Sheri L; Carver, Charles S; Joormann, Jutta

2013-09-25

This study explored the hypothesis that impulsive reactions to heightened emotion may reflect a transdiagnostic vulnerability to both externalizing and internalizing symptoms. A sample of undergraduates completed self-report measures of aggression, borderline personality disorder symptoms, anxiety symptoms, and alcohol problems, and a subset completed interviews that assessed suicidality. All participants also completed self-report measures relating to impulsivity. We predicted that emotion-reactive impulsivity, but not other aspects of impulsivity, would be related to the set of psychopathology symptoms. Multiple regression analyses found that emotion-reactive impulsivity was uniquely related to each of the psychopathology scales, whereas non-emotion-relevant impulsivity was uniquely related only to alcohol problems. Discussion focuses on limitations and clinical implications. © 2013 Elsevier B.V. All rights reserved.

On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

PubMed

Mizutani, Eiji; Demmel, James W

2003-01-01

This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

A comparison of unemployed job-seekers with and without social anxiety

PubMed Central

Himle, Joseph A; Weaver, Addie; Bybee, Deborah; O'Donnell, Lisa; Vlnka, Sarah; Laviolette, Wayne; Steinberger, Edward; Zipora, Golenberg; Levine, Debra Siegel

2014-01-01

Objective Literature consistently demonstrates that social anxiety disorder has substantial negative impacts on occupational functioning. However, to date, no identified empirical work has focused on understanding the specific nature of vocational problems among persons with social anxiety disorder. This study examines the association between employment-related factors (i.e., barriers to employment; skills related to employment; and job aspirations) and social anxiety among a sample of adults seeking vocational rehabilitation services. Methods Data from intake assessments, including a screen for social anxiety disorder, of 265 low-income, unemployed adults who initiated vocational rehabilitation services in urban Michigan was examined to assess differences in barriers to employment, employment skills, job aspirations, and demographic characteristics among participants who screened positive for social anxiety disorder compared to those who did not. Bivariate and multiple logistic regression analyses were performed. Results Multiple logistic regression analysis revealed that greater perceived experience and skill barriers to employment, fewer skills related to social-type occupations, and less education were significantly associated with social anxiety, after adjusting for other factors. Bivariate analysis also suggested that participants who screened positive for social anxiety disorder were significantly less likely to aspire to social jobs. Conclusions Employment-related factors likely impacting occupational functioning were significantly different between persons with and without social anxiety problems. Identifying these differences in employment barriers, skills, and job aspirations offer potentially important functional targets for psychosocial interventions aimed at social anxiety disorder and suggest the need for vocational service professionals to assess and address social anxiety among their clients. PMID:24733524

Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems

PubMed Central

Zainudin, Suhaila; Arif, Shereena M.

2017-01-01

Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767

Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

ERIC Educational Resources Information Center

Bulcock, J. W.

The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

The psychological impact of terrorism: an epidemiologic study of posttraumatic stress disorder and associated factors in victims of the 1995-1996 bombings in France.

PubMed

Verger, Pierre; Dab, William; Lamping, Donna L; Loze, Jean-Yves; Deschaseaux-Voinet, Céline; Abenhaim, Lucien; Rouillon, Frédéric

2004-08-01

A wave of bombings struck France in 1995 and 1996, killing 12 people and injuring more than 200. The authors conducted follow-up evaluations with the victims in 1998 to determine the prevalence of and factors associated with posttraumatic stress disorder (PTSD). Victims directly exposed to the bombings (N=228) were recruited into a retrospective, cross-sectional study. Computer-assisted telephone interviews were conducted to evaluate PTSD, per DSM-IV criteria, and to assess health status before the attack, initial injury severity and perceived threat at the time of attack, and psychological symptoms, cosmetic impairment, hearing problems, and health service use at the time of the follow-up evaluation. Factors associated with PTSD were investigated with univariate logistic regression followed by multiple logistic regression analyses. A total of 196 respondents (86%) participated in the study. Of these, 19% had severe initial physical injuries (hospitalization exceeding 1 week). Problems reported at the follow-up evaluation included attack-related hearing problems (51%), cosmetic impairment (33%), and PTSD (31%) (95% confidence interval=24.5%-37.5%). Results of logistic regression analyses indicated that the risk of PTSD was significantly higher among women (odds ratio=2.54), participants age 35-54 (odds ratio=2.83), and those who had severe initial injuries (odds ratio=2.79) or cosmetic impairment (odds ratio=2.74) or who perceived substantial threat during the attack (odds ratio=3.99). The high prevalence of PTSD 2.6 years on average after a terrorist attack emphasizes the need for improved health services to address the intermediate and long-term consequences of terrorism.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Breast Cancer and Modifiable Lifestyle Factors in Argentinean Women: Addressing Missing Data in a Case-Control Study

PubMed Central

Coquet, Julia Becaria; Tumas, Natalia; Osella, Alberto Ruben; Tanzi, Matteo; Franco, Isabella; Diaz, Maria Del Pilar

2016-01-01

A number of studies have evidenced the effect of modifiable lifestyle factors such as diet, breastfeeding and nutritional status on breast cancer risk. However, none have addressed the missing data problem in nutritional epidemiologic research in South America. Missing data is a frequent problem in breast cancer studies and epidemiological settings in general. Estimates of effect obtained from these studies may be biased, if no appropriate method for handling missing data is applied. We performed Multiple Imputation for missing values on covariates in a breast cancer case-control study of Córdoba (Argentina) to optimize risk estimates. Data was obtained from a breast cancer case control study from 2008 to 2015 (318 cases, 526 controls). Complete case analysis and multiple imputation using chained equations were the methods applied to estimate the effects of a Traditional dietary pattern and other recognized factors associated with breast cancer. Physical activity and socioeconomic status were imputed. Logistic regression models were performed. When complete case analysis was performed only 31% of women were considered. Although a positive association of Traditional dietary pattern and breast cancer was observed from both approaches (complete case analysis OR=1.3, 95%CI=1.0-1.7; multiple imputation OR=1.4, 95%CI=1.2-1.7), effects of other covariates, like BMI and breastfeeding, were only identified when multiple imputation was considered. A Traditional dietary pattern, BMI and breastfeeding are associated with the occurrence of breast cancer in this Argentinean population when multiple imputation is appropriately performed. Multiple Imputation is suggested in Latin America’s epidemiologic studies to optimize effect estimates in the future. PMID:27892664

Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

ERIC Educational Resources Information Center

Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

2010-01-01

Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…

Effects of personality traits on collaborative performance in problem-based learning tutorials

PubMed Central

Jang, Hye Won; Park, Seung Won

2016-01-01

Objectives To examine the relationship between students’ collaborative performance in a problem-based learning (PBL) environment and their personality traits. Methods This retrospective, cross-sectional study was conducted using student data of a PBL program between 2013 and 2014 at Sungkyunkwan University School of Medicine, Seoul, South Korea. Eighty students were included in the study. Student data from the Temperament and Character Inventory were used as a measure of their personality traits. Peer evaluation scores during PBL were used as a measure of students’ collaborative performance. Results Simple regression analyses indicated that participation was negatively related to harm avoidance and positively related to persistence, whereas preparedness for the group work was negatively related to reward dependence. On multiple regression analyses, low reward dependence remained a significant predictor of preparedness. Grade-point average (GPA) was negatively associated with novelty seeking and cooperativeness and was positively associated with persistence. Conclusion Medical students who are less dependent on social reward are more likely to complete assigned independent work to prepare for the PBL tutorials. The findings of this study can help educators better understand and support medical students who are at risk of struggling in collaborative learning environments. PMID:27874153

Effects of personality traits on collaborative performance in problem-based learning tutorials.

PubMed

Jang, Hye Won; Park, Seung Won

2016-12-01

To examine the relationship between students' collaborative performance in a problem-based learning (PBL) environment and their personality traits. Methods:This retrospective, cross-sectional study was conducted using student data of a PBL program between 2013 and 2014 at Sungkyunkwan University School of Medicine, Seoul, South Korea. Eighty students were included in the study. Student data from the Temperament and Character Inventory were used as a measure of their personality traits. Peer evaluation scores during PBL were used as a measure of students' collaborative performance. Results: Simple regression analyses indicated that participation was negatively related to harm avoidance and positively related to persistence, whereas preparedness for the group work was negatively related to reward dependence. On multiple regression analyses, low reward dependence remained a significant predictor of preparedness. Grade-point average (GPA)  was negatively associated with novelty seeking and cooperativeness and was positively associated with persistence.  Conclusion: Medical students who are less dependent on social reward are more likely to complete assigned independent work to prepare for the PBL tutorials. The findings of this study can help educators better understand and support medical students who are at risk of struggling in collaborative learning environments.

Determinants of Psychosocial Difficulties Experienced by Persons with Brain Disorders: Towards a ‘Horizontal Epidemiology’ Approach

PubMed Central

Sabariego, Carla; Coenen, Michaela; Ballert, Carolina; Cabello, Maria; Leonardi, Matilde; Anczewska, Marta; Pitkänen, Tuuli; Raggi, Alberto; Mellor, Blanca; Covelli, Venusia; Świtaj, Piotr; Levola, Jonna; Schiavolin, Silvia; Chrostek, Anna; Bickenbach, Jerome; Chatterji, Somnath; Cieza, Alarcos

2015-01-01

Background Persons with brain disorders experience significant psychosocial difficulties (PSD) in daily life, e.g. problems with managing daily routine or emotional lability, and the level of the PSD depends on social, physical and political environments, and psychologic-personal determinants. Our objective is to determine a brief set of environmental and psychologic-personal factors that are shared determinants of PSD among persons with different brain disorders. Methods Cross-sectional study, convenience sample of persons with either dementia, stroke, multiple sclerosis, epilepsy, migraine, depression, schizophrenia, substance dependence or Parkinson’s disease. Random forest regression and classical linear regression were used in the analyses. Results 722 subjects were interviewed in four European countries. The brief set of determinants encompasses presence of comorbidities, health status appraisal, stressful life events, personality changes, adaptation, self-esteem, self-worth, built environment, weather, and health problems in the family. Conclusions The identified brief set of common determinants of PSD can be used to support the implementation of cross-cutting interventions, social actions and policy tools to lower PSD experienced by persons with brain disorders. This set complements a recently proposed reliable and valid direct metric of PSD for brain disorders called PARADISE24. PMID:26675663

Does subtype matter? Assessing the effects of maltreatment on functioning in preadolescent youth in out-of-home care

PubMed Central

Petrenko, Christie L. M.; Friend, Angela; Garrido, Edward F.; Taussig, Heather N.; Culhane, Sara E.

2012-01-01

Objectives Attempts to understand the effects of maltreatment subtypes on childhood functioning are complicated by the fact that children often experience multiple subtypes. This study assessed the effects of maltreatment subtypes on the cognitive, academic, and mental health functioning of preadolescent youth in out-of-home care using both “variable-centered” and “person-centered” statistical analytic approaches to modeling multiple subtypes of maltreatment. Methods Participants included 334 preadolescent youth (ages 9 to 11) placed in out-of-home care due to maltreatment. The occurrence and severity of maltreatment subtypes (physical abuse, sexual abuse, physical neglect, and supervisory neglect) were coded from child welfare records. The relationships between maltreatment subtypes and children’s cognitive, academic, and mental health functioning were evaluated with the following approaches: “Variable-centered” analytic methods: Regression approach: Multiple regression was used to estimate the effects of each maltreatment subtype (separate analyses for occurrence and severity), controlling for the other subtypes. Hierarchical approach: Contrast coding was used in regression analyses to estimate the effects of discrete maltreatment categories that were assigned based on a subtype occurrence hierarchy (sexual abuse > physical abuse > physical neglect > supervisory neglect). “Person-centered” analytic method: Latent class analysis was used to group children with similar maltreatment severity profiles into discrete classes. The classes were then compared to determine if they differed in terms of their ability to predict functioning. Results The approaches identified similar relationships between maltreatment subtypes and children’s functioning. The most consistent findings indicated that maltreated children who experienced physical or sexual abuse were at highest risk for caregiver-reported externalizing behavior problems, and those who experienced physical abuse and/or physical neglect were more likely to have higher levels of caregiver-reported internalizing problems. Children experiencing predominantly low severity supervisory neglect had relatively better functioning than other maltreated youth. Conclusions Many of the maltreatment subtype differences identified within the maltreated sample in the current study are consistent with those from previous research comparing maltreated youth to non-maltreated comparison groups. Results do not support combining supervisory and physical neglect. The “variable-centered” and “person-centered” analytic approaches produced complementary results. Advantages and disadvantages of each approach are discussed. PMID:22947490

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

PubMed

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Sleep problems predict comorbid externalizing behaviors and depression in young adolescents with attention-deficit/hyperactivity disorder.

PubMed

Becker, Stephen P; Langberg, Joshua M; Evans, Steven W

2015-08-01

Children and adolescents with attention-deficit/hyperactivity disorder (ADHD) experience high rates of sleep problems and are also at increased risk for experiencing comorbid mental health problems. This study provides an initial examination of the 1-year prospective association between sleep problems and comorbid symptoms in youth diagnosed with ADHD. Participants were 81 young adolescents (75 % male) carefully diagnosed with ADHD and their parents. Parents completed measures of their child's sleep problems and ADHD symptoms, oppositional defiant disorder (ODD) symptoms, and general externalizing behavior problems at baseline (M age = 12.2) and externalizing behaviors were assessed again 1 year later. Adolescents completed measures of anxiety and depression at both time-points. Medication use was not associated with sleep problems or comorbid psychopathology symptoms. Regression analyses indicated that, above and beyond demographic characteristics, ADHD symptom severity, and initial levels of comorbidity, sleep problems significantly predicted greater ODD symptoms, general externalizing behavior problems, and depressive symptoms 1 year later. Sleep problems were not concurrently or prospectively associated with anxiety. Although this study precludes making causal inferences, it does nonetheless provide initial evidence of sleep problems predicting later comorbid externalizing behaviors and depression symptoms in youth with ADHD. Additional research is needed with larger samples and multiple time-points to further examine the interrelations of sleep problems and comorbidity.

Predictors of intimate partner problem-related suicides among suicide decedents in Kentucky

PubMed Central

Comiford, Ashley L.; Sanderson, Wayne T.; Chesnut, Lorie; Brown, Sabrina

2016-01-01

Abstract: Background: Suicide is the 10th leading cause of death in the United States. Furthermore, intimate partner problems are amid the top precipitating circumstances among suicide decedents. The aim of this study was to determine circumstantial associations of intimate partner problem-related suicides in suicide decedents in Kentucky. Methods: All suicides that were reported to the Kentucky Violent Death Reporting System between 2005 and 2012 were eligible for this study. Multiple logistic regression was used to explore predictors (precipitating health-related problems, life stressors, and criminal/legal issues) of intimate partner problem-related suicides. Results: Of the 4,754 suicides, included in this study, approximately 17% had intimate partner problems prior to suicide. In the adjusted analysis, mental health issues, alcohol problems, history of suicides attempts, suicides precipitated by another crime, and other legal problems increased the odds of having an intimate partner-related suicide. However, having physical health problems, prior to the suicide, decreased the odds of intimate partner-related suicide. Conclusions: These results provide insight for the development of suicide interventions for individuals with intimate partner problems by targeting risk factors that are prevalent among this population. Moreover, these results may help marriage/relationship and/or family/divorce court representatives identify individuals with intimate partner problems more at risk for suicide and alleviate the influence these suicide risk factors have on individuals experiencing Intimate partner problems. PMID:27092956

Predictors of intimate partner problem-related suicides among suicide decedents in Kentucky.

PubMed

Comiford, Ashley L; Sanderson, Wayne T; Chesnut, Lorie; Brown, Sabrina

2016-07-01

Suicide is the 10th leading cause of death in the United States. Furthermore, intimate partner problems are amid the top precipitating circumstances among suicide decedents. The aim of this study was to determine circumstantial associations of intimate partner problem-related suicides in suicide decedents in Kentucky. All suicides that were reported to the Kentucky Violent Death Reporting System between 2005 and 2012 were eligible for this study. Multiple logistic regression was used to explore predictors (precipitating health-related problems, life stressors, and criminal/legal issues) of intimate partner problem-related suicides. Of the 4,754 suicides, included in this study, approximately 17% had intimate partner problems prior to suicide. In the adjusted analysis, mental health issues, alcohol problems, history of suicides attempts, suicides precipitated by another crime, and other legal problems increased the odds of having an intimate partner-related suicide. However, having physical health problems, prior to the suicide, decreased the odds of intimate partner-related suicide. These results provide insight for the development of suicide interventions for individuals with intimate partner problems by targeting risk factors that are prevalent among this population. Moreover, these results may help marriage/relationship and/or family/divorce court representatives identify individuals with intimate partner problems more at risk for suicide and alleviate the influence these suicide risk factors have on individuals experiencing Intimate partner problems. © 2016 KUMS, All rights reserved.

One-Year Prospective Study on Passion and Gambling Problems in Poker Players.

PubMed

Morvannou, Adèle; Dufour, Magali; Brunelle, Natacha; Berbiche, Djamal; Roy, Élise

2018-06-01

The concept of passion is relevant to understanding gambling behaviours and gambling problems. Longitudinal studies are useful to better understand the absence and development of gambling problems; however, only one study has specifically considered poker players. Using a longitudinal design, this study aims to determine the influence, 1 year later, of two forms of passion-harmonious and obsessive-on gambling problems in poker players. A total of 116 poker players was recruited from across Quebec, Canada. The outcome variable of interest was participants' category on the Canadian Pathological Gambling Index, and the predictive variable was the Gambling Passion Scale. Multiple logistic regression analyses were conducted to identify independent risk factors of at-risk poker players 1 year later. Obsessive passion at baseline doubled the risk of gambling problems 1 year later (p < 0.01); for harmonious passion, there was no association. Number of gambling activities, drug problems, and impulsivity were also associated with at-risk gambling. This study highlights the links between obsessive passion and at-risk behaviours among poker players. It is therefore important to prevent the development of obsessive passion among poker players.

Mental health status of women in Jordan: a comparative study between attendees of governmental and UN relief and works agency's health care centers.

PubMed

Al-Modallal, Hanan; Hamaideh, Shaher; Mudallal, Rula

2014-05-01

This study aimed at investigating differences in mental health problems between attendees of governmental and United Nations Relief and Works Agency for Palestine Refugees health care centers in Jordan. Further, predictors of mental health problems based on women's demographic profile were investigated. A convenience sample of 620 women attending governmental and United Nations Relief and Works Agency for Palestine Refugees health care centers in Jordan was recruited for this purpose. Independent samples t-tests were used to identify differences in mental health, and multiple linear regression was implemented to identify significant predictors of women's mental health problems. Results indicated an absence of significant differences in mental health problems between attendees of the two types of health care centers. Further, among the demographic indicators that were tested, income, spousal violence, and general health were the predictors of at least three different mental health problems in women. This study highlights opportunities for health professionals to decrease women's propensity for mental health problems by addressing these factors when treating women attending primary care centers in different Jordanian towns, villages, and refugee camps.

A two-stage approach to the depot shunting driver assignment problem with workload balance considerations.

PubMed

Wang, Jiaxi; Gronalt, Manfred; Sun, Yan

2017-01-01

Due to its environmentally sustainable and energy-saving characteristics, railway transportation nowadays plays a fundamental role in delivering passengers and goods. Emerged in the area of transportation planning, the crew (workforce) sizing problem and the crew scheduling problem have been attached great importance by the railway industry and the scientific community. In this paper, we aim to solve the two problems by proposing a novel two-stage optimization approach in the context of the electric multiple units (EMU) depot shunting driver assignment problem. Given a predefined depot shunting schedule, the first stage of the approach focuses on determining an optimal size of shunting drivers. While the second stage is formulated as a bi-objective optimization model, in which we comprehensively consider the objectives of minimizing the total walking distance and maximizing the workload balance. Then we combine the normalized normal constraint method with a modified Pareto filter algorithm to obtain Pareto solutions for the bi-objective optimization problem. Furthermore, we conduct a series of numerical experiments to demonstrate the proposed approach. Based on the computational results, the regression analysis yield a driver size predictor and the sensitivity analysis give some interesting insights that are useful for decision makers.

[Quality of life of primary care patients in Rio de Janeiro and São Paulo, Brasil: associations with stressful life events and mental health].

PubMed

Portugal, Flávia Batista; Campos, Mônica Rodrigues; Gonçalves, Daniel Almeida; Mari, Jair de Jesus; Fortes, Sandra Lúcia Correia Lima

2016-02-01

Quality of life (QoL) is a subjective construct, which can be negatively associated with factors such as mental disorders and stressful life events (SLEs). This article seeks to identify the association between socioeconomic and demographic variables, common mental disorders, symptoms suggestive of depression and anxiety, SLEs with QoL in patients attended in Primary Care (PC). It is a transversal study, conducted with 1,466 patients attended in PC centers in the cities of São Paulo and Rio de Janeiro in 2009 and 2010. Bivariate analysis was performed using the T-test and four multiple linear regressions for each QoL domain. The scores for the physical, psychological, social relations and environment domains were, respectively, 64.7; 64.2; 68.5 and 49.1. By means of multivariate analysis, associations of the physical domain were found with health problems and discrimination; of the psychological domain with discrimination; of social relations with financial/structural problems; of external causes and health problems; and of the environment with financial/structural problems, external causes and discrimination. Mental health variables, health problems and financial/structural problems were the factors negatively associated with QoL.

A two-stage approach to the depot shunting driver assignment problem with workload balance considerations

PubMed Central

Gronalt, Manfred; Sun, Yan

2017-01-01

Due to its environmentally sustainable and energy-saving characteristics, railway transportation nowadays plays a fundamental role in delivering passengers and goods. Emerged in the area of transportation planning, the crew (workforce) sizing problem and the crew scheduling problem have been attached great importance by the railway industry and the scientific community. In this paper, we aim to solve the two problems by proposing a novel two-stage optimization approach in the context of the electric multiple units (EMU) depot shunting driver assignment problem. Given a predefined depot shunting schedule, the first stage of the approach focuses on determining an optimal size of shunting drivers. While the second stage is formulated as a bi-objective optimization model, in which we comprehensively consider the objectives of minimizing the total walking distance and maximizing the workload balance. Then we combine the normalized normal constraint method with a modified Pareto filter algorithm to obtain Pareto solutions for the bi-objective optimization problem. Furthermore, we conduct a series of numerical experiments to demonstrate the proposed approach. Based on the computational results, the regression analysis yield a driver size predictor and the sensitivity analysis give some interesting insights that are useful for decision makers. PMID:28704489

Assessment of Gasoline Prices and its Predictive Power on U.S. Consumers' Retail Spending and Savings

NASA Astrophysics Data System (ADS)

Alvarado-Bonilla, Joel

The rising costs of fuels and specifically gasoline pose an economic challenge to U.S. consumers. Thus, the specific problem considered in this study was a rise in gasoline prices can reduce consumer spending, disposable income, food service traffic, and spending on healthy food, medicines, or visits to the doctor. Aligned with the problem, the purpose of this quantitative multiple correlation study was to examine the economic aspects for a rise in gasoline prices to reduce the six elements in the problem. This study consisted of a correlational design based on a retrospective longitudinal analysis (RLA) to examine gasoline prices versus the economic indexes of: (a) Retail Spending and (b) personal savings (PS). The RLA consisted on historic archival public data from 1978 to 2015. This RLA involved two separate linear multiple regression analyses to measure gasoline price's predictive power (PP) on two indexes while controlling for Unemployment Rate (UR). In summary, regression Formula 1 revealed Gasoline Price had a significant 61.1% PP on Retail Spending. In contrast, Formula 2 had Gasoline Price not having a significant PP on PS. Formula 2 yielded UR with 38.8% PP on PS. Results were significant at p<.01. Gasoline Price's PP on Retail Spending means a spending link to retail items such as: food service traffic, healthy food, medicines, and consumer spending. The UR predictive power on PS was unexpected, but logical from an economic view. Also unexpected was Gasoline Price's non-predictive power on PS, which suggests Americans may not save money when gasoline prices drop. These results shed light on the link of gasoline and UR on U.S. consumer's economy through savings and spending, which can be useful for policy design on gasoline and fuels taxing and pricing. The results serve as a basis for future study on gasoline and economics.

Bayesian Scalar-on-Image Regression with Application to Association Between Intracranial DTI and Cognitive Outcomes

PubMed Central

Huang, Lei; Goldsmith, Jeff; Reiss, Philip T.; Reich, Daniel S.; Crainiceanu, Ciprian M.

2013-01-01

Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian “scalar-on-image” regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients. PMID:23792220

Non-linear quantitative structure-activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase

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

Sadat Hayatshahi, Sayyed Hamed; Abdolmaleki, Parviz; Safarian, Shahrokh

2005-12-16

Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k {sub i} values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, themore » previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.« less

A Model for Oil-Gas Pipelines Cost Prediction Based on a Data Mining Process

NASA Astrophysics Data System (ADS)

Batzias, Fragiskos A.; Spanidis, Phillip-Mark P.

2009-08-01

This paper addresses the problems associated with the cost estimation of oil/gas pipelines during the elaboration of feasibility assessments. Techno-economic parameters, i.e., cost, length and diameter, are critical for such studies at the preliminary design stage. A methodology for the development of a cost prediction model based on Data Mining (DM) process is proposed. The design and implementation of a Knowledge Base (KB), maintaining data collected from various disciplines of the pipeline industry, are presented. The formulation of a cost prediction equation is demonstrated by applying multiple regression analysis using data sets extracted from the KB. Following the methodology proposed, a learning context is inductively developed as background pipeline data are acquired, grouped and stored in the KB, and through a linear regression model provide statistically substantial results, useful for project managers or decision makers.

Estimating Interaction Effects With Incomplete Predictor Variables

PubMed Central

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

2014-01-01

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

Student experiences of participating in five collaborative blended learning courses in Africa and Asia: a survey

PubMed Central

Atkins, Salla; Yan, Weirong; Meragia, Elnta; Mahomed, Hassan; Rosales-Klintz, Senia; Skinner, Donald; Zwarenstein, Merrick

2016-01-01

Background As blended learning (BL; a combination of face-to-face and e-learning methods) becomes more commonplace, it is important to assess whether students find it useful for their studies. ARCADE HSSR and ARCADE RSDH (African Regional Capacity Development for Health Systems and Services Research; Asian Regional Capacity Development for Research on Social Determinants of Health) were unique capacity-building projects, focusing on developing BL in Africa and Asia on issues related to global health. Objective We aimed to evaluate the student experience of participating in any of five ARCADE BL courses implemented collaboratively at institutions from Africa, Asia, and Europe. Design A post-course student survey with 118 students was conducted. The data were collected using email or through an e-learning platform. Data were analysed with SAS, using bivariate and multiple logistic regression. We focused on the associations between various demographic and experience variables and student-reported overall perceptions of the courses. Results In total, 82 students responded to the survey. In bivariate logistic regression, the course a student took [p=0.0067, odds ratio (OR)=0.192; 95% confidence interval (CI): 0.058–0.633], male gender of student (p=0.0474, OR=0.255; 95% CI: 0.066–0.985), not experiencing technical problems (p<0.001, OR=17.286; 95% CI: 4.629–64.554), and reporting the discussion forum as adequate for student needs (p=0.0036, OR=0.165; 95% CI: 0.049–0.555) were found to be associated with a more positive perception of BL, as measured by student rating of the overall helpfulness of the e-learning component to their studies. In contrast, perceiving the assessment as adequate was associated with a worse perception of overall usefulness. In a multiple regression, the course, experiencing no technical problems, and perceiving the discussion as adequate remained significantly associated with a more positively rated perception of the usefulness of the online component of the blended courses. Discussion The results suggest that lack of technical problems and functioning discussion forums are of importance during BL courses focusing on global health-related topics. Through paying attention to these aspects, global health education could be provided using BL approaches to student satisfaction. PMID:27725077

Student experiences of participating in five collaborative blended learning courses in Africa and Asia: a survey.

PubMed

Atkins, Salla; Yan, Weirong; Meragia, Elnta; Mahomed, Hassan; Rosales-Klintz, Senia; Skinner, Donald; Zwarenstein, Merrick

2016-01-01

As blended learning (BL; a combination of face-to-face and e-learning methods) becomes more commonplace, it is important to assess whether students find it useful for their studies. ARCADE HSSR and ARCADE RSDH (African Regional Capacity Development for Health Systems and Services Research; Asian Regional Capacity Development for Research on Social Determinants of Health) were unique capacity-building projects, focusing on developing BL in Africa and Asia on issues related to global health. We aimed to evaluate the student experience of participating in any of five ARCADE BL courses implemented collaboratively at institutions from Africa, Asia, and Europe. A post-course student survey with 118 students was conducted. The data were collected using email or through an e-learning platform. Data were analysed with SAS, using bivariate and multiple logistic regression. We focused on the associations between various demographic and experience variables and student-reported overall perceptions of the courses. In total, 82 students responded to the survey. In bivariate logistic regression, the course a student took [ p =0.0067, odds ratio (OR)=0.192; 95% confidence interval (CI): 0.058-0.633], male gender of student ( p =0.0474, OR=0.255; 95% CI: 0.066-0.985), not experiencing technical problems ( p <0.001, OR=17.286; 95% CI: 4.629-64.554), and reporting the discussion forum as adequate for student needs ( p =0.0036, OR=0.165; 95% CI: 0.049-0.555) were found to be associated with a more positive perception of BL, as measured by student rating of the overall helpfulness of the e-learning component to their studies. In contrast, perceiving the assessment as adequate was associated with a worse perception of overall usefulness. In a multiple regression, the course, experiencing no technical problems, and perceiving the discussion as adequate remained significantly associated with a more positively rated perception of the usefulness of the online component of the blended courses. The results suggest that lack of technical problems and functioning discussion forums are of importance during BL courses focusing on global health-related topics. Through paying attention to these aspects, global health education could be provided using BL approaches to student satisfaction.

The Geometry of Enhancement in Multiple Regression

ERIC Educational Resources Information Center

Waller, Niels G.

2011-01-01

In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…

Problem Gambling Associated with Violent and Criminal Behaviour: A Danish Population-Based Survey and Register Study.

PubMed

Laursen, Bjarne; Plauborg, Rikke; Ekholm, Ola; Larsen, Christina Viskum Lytken; Juel, Knud

2016-03-01

This study compares the number of criminal charges among problem gamblers (N = 384) and non-problem gamblers including non-gamblers (N = 18,241) and examines whether problem gambling is more strongly associated with income-generating crimes like theft, fraud and forgery than other types of crimes such as violent crimes. A cohort study was carried out, based on data from the Danish Health and Morbidity Surveys in 2005 and 2010, which were linked at the individual level with data from The Danish National Criminal Register. Multiple logistic regression analyses were used to determine the association between problem gambling and charges for different categories of crime. We found that problem gamblers had significantly higher odds of being charged than non-problem gamblers (adjusted odds ratio 1.5; 95 % confidence interval 1.1-1.9). The odds ratio for economic crime charges was 2.6 (1.5-4.5), for violence charges 2.2 (1.1-4.5), and for drug charges 3.7 (1.7-8.0). For road traffic charges the odds ratio was 1.3 (1.0-1.8). Hence, there was a strong association between problem gambling and being charged except for road traffic charges; however the association was not stronger for economic charges than for violence and drug charges.

Adjusting data to body size: a comparison of methods as applied to quantitative trait loci analysis of musculoskeletal phenotypes.

PubMed

Lang, Dean H; Sharkey, Neil A; Lionikas, Arimantas; Mack, Holly A; Larsson, Lars; Vogler, George P; Vandenbergh, David J; Blizard, David A; Stout, Joseph T; Stitt, Joseph P; McClearn, Gerald E

2005-05-01

The aim of this study was to compare three methods of adjusting skeletal data for body size and examine their use in QTL analyses. It was found that dividing skeletal phenotypes by body mass index induced erroneous QTL results. The preferred method of body size adjustment was multiple regression. Many skeletal studies have reported strong correlations between phenotypes for muscle, bone, and body size, and these correlations add to the difficulty in identifying genetic influence on skeletal traits that are not mediated through overall body size. Quantitative trait loci (QTL) identified for skeletal phenotypes often map to the same chromosome regions as QTLs for body size. The actions of a QTL identified as influencing BMD could therefore be mediated through the generalized actions of growth on body size or muscle mass. Three methods of adjusting skeletal phenotypes to body size were performed on morphologic, structural, and compositional measurements of the femur and tibia in 200-day-old C57BL/6J x DBA/2 (BXD) second generation (F(2)) mice (n = 400). A common method of removing the size effect has been through the use of ratios. This technique and two alternative techniques using simple and multiple regression were performed on muscle and skeletal data before QTL analyses, and the differences in QTL results were examined. The use of ratios to remove the size effect was shown to increase the size effect by inducing spurious correlations, thereby leading to inaccurate QTL results. Adjustments for body size using multiple regression eliminated these problems. Multiple regression should be used to remove the variance of co-factors related to skeletal phenotypes to allow for the study of genetic influence independent of correlated phenotypes. However, to better understand the genetic influence, adjusted and unadjusted skeletal QTL results should be compared. Additional insight can be gained by observing the difference in LOD score between the adjusted and nonadjusted phenotypes. Identifying QTLs that exert their effects on skeletal phenotypes through body size-related pathways as well as those having a more direct and independent influence on bone are equally important in deciphering the complex physiologic pathways responsible for the maintenance of bone health.

Application of Multiregressive Linear Models, Dynamic Kriging Models and Neural Network Models to Predictive Maintenance of Hydroelectric Power Systems

NASA Astrophysics Data System (ADS)

Lucifredi, A.; Mazzieri, C.; Rossi, M.

2000-05-01

Since the operational conditions of a hydroelectric unit can vary within a wide range, the monitoring system must be able to distinguish between the variations of the monitored variable caused by variations of the operation conditions and those due to arising and progressing of failures and misoperations. The paper aims to identify the best technique to be adopted for the monitoring system. Three different methods have been implemented and compared. Two of them use statistical techniques: the first, the linear multiple regression, expresses the monitored variable as a linear function of the process parameters (independent variables), while the second, the dynamic kriging technique, is a modified technique of multiple linear regression representing the monitored variable as a linear combination of the process variables in such a way as to minimize the variance of the estimate error. The third is based on neural networks. Tests have shown that the monitoring system based on the kriging technique is not affected by some problems common to the other two models e.g. the requirement of a large amount of data for their tuning, both for training the neural network and defining the optimum plane for the multiple regression, not only in the system starting phase but also after a trivial operation of maintenance involving the substitution of machinery components having a direct impact on the observed variable. Or, in addition, the necessity of different models to describe in a satisfactory way the different ranges of operation of the plant. The monitoring system based on the kriging statistical technique overrides the previous difficulties: it does not require a large amount of data to be tuned and is immediately operational: given two points, the third can be immediately estimated; in addition the model follows the system without adapting itself to it. The results of the experimentation performed seem to indicate that a model based on a neural network or on a linear multiple regression is not optimal, and that a different approach is necessary to reduce the amount of work during the learning phase using, when available, all the information stored during the initial phase of the plant to build the reference baseline, elaborating, if it is the case, the raw information available. A mixed approach using the kriging statistical technique and neural network techniques could optimise the result.

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

PubMed

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

2014-01-01

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

The assumed Aalenian stage-long eustatic lowstand did not exist: A review of the fresh evidence from Africa and other continents

NASA Astrophysics Data System (ADS)

Ruban, Dmitry A.; Sallam, Emad S.

2018-03-01

The views of the Jurassic eustatic fluctuations differ significantly: specialists either suggest multiple rises and falls ("Haq's view") or question the idea of global falls ("Hallam's view"). For instance, it is unclear whether there was a stage-long eustatic lowstand in the Aalenian. The presence of the noted alternatives is a serious problem complicating interpretation of events in the geological history. This paper summarizes the evidence of the Aalenian long-term shoreline shifts obtained in different regions of the world since 2000, i.e., after the noted views appeared. This evidence deals with the stratigraphical architecture of regions (interpreted in the present article), the established shoreline shifts (transgressions and regressions), and the knowledge of the regional tectonic activity. The compiled information characterizes "stable" regions located in the different parts of the world (Europe, Asia, Africa, North America, South America, and Australia). It is established that there were no regressions in some of these regions in the Aalenian, whereas regressions in the other regions can be explained by the influence of the tectonic activity. There was no coherence of the basin-scale eustatically-driven regressions (in contrast, the long-term Bajocian eustatic rise is proven by a coherence of regional transgressions). This finding contradicts the idea of the stage-long eustatic lowstand in the Aalenian and, thus, favours the "Hallam's view". This interpretation is in agreement with the present knowledge of the Earth's palaeoclimate and the past plate tectonics. This study demonstrates efficacy of interregional correlation of sea-level changes for resolution of the problem of the alternative views of the Jurassic eustasy.

Unique associations between peer relations and social anxiety in early adolescence.

PubMed

Flanagan, Kelly S; Erath, Stephen A; Bierman, Karen L

2008-10-01

This study examined the unique associations between feelings of social anxiety and multiple dimensions of peer relations (positive peer nominations, peer- and self-reported peer victimization, and self-reported friendship quality) among 383 sixth- and seventh-grade students. Hierarchical regression analysis provided evidence for the unique contribution made by peer relations to social anxiety above that made by adolescents' individual vulnerabilities (i.e., teacher ratings of social behavior, self-reported social appraisals assessed by hypothetical vignettes). Two subgroups of socially anxious adolescents--those with and without peer problems--were distinguished by their social behavior but not their social appraisals.

Limb-darkening and the structure of the Jovian atmosphere

NASA Technical Reports Server (NTRS)

Newman, W. I.; Sagan, C.

1978-01-01

By observing the transit of various cloud features across the Jovian disk, limb-darkening curves were constructed for three regions in the 4.6 to 5.1 mu cm band. Several models currently employed in describing the radiative or dynamical properties of planetary atmospheres are here examined to understand their implications for limb-darkening. The statistical problem of fitting these models to the observed data is reviewed and methods for applying multiple regression analysis are discussed. Analysis of variance techniques are introduced to test the viability of a given physical process as a cause of the observed limb-darkening.

Good Self-Control as a Buffering Agent for Adolescent Substance Use

PubMed Central

Wills, Thomas A.; Ainette, Michael G.

2008-01-01

We tested the prediction that self-control will have buffering effects for adolescent substance use (tobacco, alcohol, and marijuana) with regard to three risk factors: family life events, adolescent life events, and peer substance use. Participants were a sample of public school students (N = 1,767) who were surveyed at four yearly intervals between 6th grade and 9th grade. Good self-control was assessed with multiple indicators including planning and problem solving. Results showed that the impact of all three risk factors on substance use was reduced among persons with higher scores on good self-control. Buffering was found in cross-sectional analyses with multiple regression and in longitudinal analyses in a latent growth model with time-varying covariates. Implications for addressing self-control in prevention programs are discussed. PMID:19071971

Robust Mosaicking of Stereo Digital Elevation Models from the Ames Stereo Pipeline

NASA Technical Reports Server (NTRS)

Kim, Tae Min; Moratto, Zachary M.; Nefian, Ara Victor

2010-01-01

Robust estimation method is proposed to combine multiple observations and create consistent, accurate, dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce higher-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data than is currently possible. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. However, the DEMs currently produced by the ASP often contain errors and inconsistencies due to image noise, shadows, etc. The proposed method addresses this problem by making use of multiple observations and by considering their goodness of fit to improve both the accuracy and robustness of the estimate. The stepwise regression method is applied to estimate the relaxed weight of each observation.

Do mental health problems in childhood predict chronic physical conditions among males in early adulthood? Evidence from a community-based prospective study.

PubMed

Goodwin, R D; Sourander, A; Duarte, C S; Niemelä, S; Multimäki, P; Nikolakaros, G; Helenius, H; Piha, J; Kumpulainen, K; Moilanen, I; Tamminen, T; Almqvist, F

2009-02-01

Previous studies have documented associations between mental and physical health problems in cross-sectional studies, yet little is known about these relationships over time or the specificity of these associations. The aim of the current study was to examine the relationship between mental health problems in childhood at age 8 years and physical disorders in adulthood at ages 18-23 years. Multiple logistic regression analyses were used to examine the relationship between childhood mental health problems, reported by child, parent and teacher, and physical disorders diagnosed by a physician in early adulthood. Significant linkages emerged between childhood mental health problems and obesity, atopic eczema, epilepsy and asthma in early adulthood. Specifically, conduct problems in childhood were associated with a significantly increased likelihood of obesity and atopic eczema; emotional problems were associated with an increased likelihood of epilepsy and asthma; and depression symptoms at age 8 were associated with an increased risk of asthma in early adulthood. Our findings provide the first evidence of an association between mental health problems during childhood and increased risk of specific physical health problems, mainly asthma and obesity, during early adulthood, in a representative sample of males over time. These data suggest that behavioral and emotional problems in childhood may signal vulnerability to chronic physical health problems during early adulthood.

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

PubMed

Marill, Keith A

2004-01-01

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

Paternal mental health and socioemotional and behavioral development in their children.

PubMed

Kvalevaag, Anne Lise; Ramchandani, Paul G; Hove, Oddbjørn; Assmus, Jörg; Eberhard-Gran, Malin; Biringer, Eva

2013-02-01

To examine the association between symptoms of psychological distress in expectant fathers and socioemotional and behavioral outcomes in their children at age 36 months. The current study is based on data from the Norwegian Mother and Child Cohort Study on 31 663 children. Information about fathers' mental health was obtained by self-report (Hopkins Symptom Checklist) in week 17 or 18 of gestation. Information about mothers' pre- and postnatal mental health and children's socioemotional and behavioral development at 36 months of age was obtained from parent-report questionnaires. Linear multiple regression and logistic regression models were performed while controlling for demographics, lifestyle variables, and mothers' mental health. Three percent of the fathers had high levels of psychological distress. Using linear regression models, we found a small positive association between fathers' psychological distress and children's behavioral difficulties, B = 0.19 (95% confidence interval [CI] = 0.15-0.23); emotional difficulties, B = 0.22 (95% CI = 0.18-0.26); and social functioning, B = 0.12 (95% CI = 0.07-0.16). The associations did not change when adjusted for relevant confounders. Children whose fathers had high levels of psychological distress had higher levels of emotional and behavioral problems. This study suggests that some risk of future child emotional, behavioral, and social problems can be identified during pregnancy. The findings are of importance for clinicians and policy makers in their planning of health care in the perinatal period because this represents a significant opportunity for preventive intervention.

Mental health selection and income support dynamics: multiple spell discrete-time survival analyses of welfare receipt.

PubMed

Kiely, Kim M; Butterworth, Peter

2014-04-01

The higher occurrence of common psychiatric disorders among welfare recipients has been attributed to health selection, social causation and underlying vulnerability. The aims of this study were to test for the selection effects of mental health problems on entry and re-entry to working-age welfare payments in respect to single parenthood, unemployment and disability. Nationally representative longitudinal data were drawn from the Household Income and Labour Dynamics in Australia survey. Multiple spell discrete-time survival analyses were conducted using multinomial logistic regression models to test if pre-existing mental health problems predicted transitions to welfare. Analyses were stratified by sex and multivariate adjusted for mental health problems, father's occupation, socioeconomic position, marital status, employment history, smoking status and alcohol consumption, physical function and financial hardship. All covariates were modelled as either lagged effects or when a respondent was first observed to be at risk of income support. Mental health problems were associated with increased risk of entry and re-entry to disability, unemployment and single parenting payments for women, and disability and unemployment payments for men. These associations were attenuated but remained significant after adjusting for contemporaneous risk factors. Although we do not control for reciprocal causation, our findings are consistent with a health selection hypothesis and indicate that mental illness may be a contributing factor to later receipt of different types of welfare payments. We argue that mental health warrants consideration in the design and targeting of social and economic policies.

Adverse Selection in Community Based Health Insurance among Informal Workers in Bangladesh: An EQ-5D Assessment

PubMed Central

Sarker, Abdur Razzaque; Sultana, Marufa; Chakrovorty, Sanchita; Khan, Jahangir A. M.

2018-01-01

Community-based Health Insurance (CBHI) schemes are recommended for providing financial risk protection to low-income informal workers in Bangladesh. We assessed the problem of adverse selection in a pilot CBHI scheme in this context. In total, 1292 (646 insured and 646 uninsured) respondents were surveyed using the Bengali version of the EuroQuol-5 dimensions (EQ-5D) questionnaire for assessing their health status. The EQ-5D scores were estimated using available regional tariffs. Multiple logistic regression was applied for predicting the association between health status and CBHI scheme enrolment. A higher number of insured reported problems in mobility (7.3%; p = 0.002); self-care (7.1%; p = 0.000) and pain and discomfort (7.7%; p = 0.005) than uninsured. The average EQ-5D score was significantly lower among the insured (0.704) compared to the uninsured (0.749). The regression analysis showed that those who had a problem in mobility (OR = 1.65; 95% CI: 1.25–2.17); self-care (OR = 2.29; 95% CI: 1.62–3.25) and pain and discomfort (OR = 1.43; 95% CI: 1.13–1.81) were more likely to join the scheme. Individuals with higher EQ-5D scores (OR = 0.46; 95% CI: 0.31–0.69) were less likely to enroll in the scheme. Given that adverse selection was evident in the pilot CBHI scheme, there should be consideration of this problem when planning scale-up of these kind of schemes. PMID:29385072

Noninvasive spectral imaging of skin chromophores based on multiple regression analysis aided by Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Wiswadarma, Aditya; Hase, Yota; Tanaka, Noriyuki; Maeda, Takaaki; Niizeki, Kyuichi; Aizu, Yoshihisa

2011-08-01

In order to visualize melanin and blood concentrations and oxygen saturation in human skin tissue, a simple imaging technique based on multispectral diffuse reflectance images acquired at six wavelengths (500, 520, 540, 560, 580 and 600nm) was developed. The technique utilizes multiple regression analysis aided by Monte Carlo simulation for diffuse reflectance spectra. Using the absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are deduced numerically in advance, while oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments with human skin of the human hand during upper limb occlusion and of the inner forearm exposed to UV irradiation demonstrated the ability of the method to evaluate physiological reactions of human skin tissue.

Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

ERIC Educational Resources Information Center

Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

2013-01-01

This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

Parenting approaches and digital technology use of preschool age children in a Chinese community

PubMed Central

2014-01-01

Background Young children are using digital technology (DT) devices anytime and anywhere, especially with the invention of smart phones and the replacement of desktop computers with digital tablets. Although research has shown that parents play an important role in fostering and supporting preschoolers’ developing maturity and decisions about DT use, and in protecting them from potential risk due to excessive DT exposure, there have been limited studies conducted in Hong Kong focusing on parent-child DT use. This study had three objectives: 1) to explore parental use of DTs with their preschool children; 2) to identify the DT content that associated with child behavioral problems; and 3) to investigate the relationships between approaches adopted by parents to control children’s DT use and related preschooler behavioral problems. Methods This exploratory quantitative study was conducted in Hong Kong with 202 parents or guardians of preschool children between the ages of 3 and 6 attending kindergarten. The questionnaire was focused on four aspects, including 1) participants’ demographics; 2) pattern of DT use; 3) parenting approach to manage the child’s DT use; and 4) child behavioral and health problems related to DT use. Multiple regression analysis was adopted as the main data analysis method for identifying the DT or parental approach-related predictors of the preschooler behavioral problems. Results In the multiple linear regression model, the ‘restrictive approach score’ was the only predictor among the three parental approaches (B:1.66, 95% CI: [0.21, 3.11], p < 0.05). Moreover, the viewing of antisocial behavior cartoons by children also significantly increased the tendency of children to have behavioral problem (B:3.84, 95% CI: [1.66, 6.02], p < 0.01). Conclusions Since preschool children’s cognitive and functional abilities are still in the developmental stage, parents play a crucial role in fostering appropriate and safe DT use. It is suggested that parents practice a combination of restrictive, instructive and co-using approaches, rather than a predominately restrictive approach, to facilitate their child’s growth and development. Further studies are needed to explore the parent-child relationship and parents’ self-efficacy when managing the parent-child DT use, to develop strategies to guide children in healthy DT use. PMID:24887105

A Simulation Investigation of Principal Component Regression.

ERIC Educational Resources Information Center

Allen, David E.

Regression analysis is one of the more common analytic tools used by researchers. However, multicollinearity between the predictor variables can cause problems in using the results of regression analyses. Problems associated with multicollinearity include entanglement of relative influences of variables due to reduced precision of estimation,…

Factors affecting the social problem-solving ability of baccalaureate nursing students.

PubMed

Lau, Ying

2014-01-01

The hospital environment is characterized by time pressure, uncertain information, conflicting goals, high stakes, stress, and dynamic conditions. These demands mean there is a need for nurses with social problem-solving skills. This study set out to (1) investigate the social problem-solving ability of Chinese baccalaureate nursing students in Macao and (2) identify the association between communication skill, clinical interaction, interpersonal dysfunction, and social problem-solving ability. All nursing students were recruited in one public institute through the census method. The research design was exploratory, cross-sectional, and quantitative. The study used the Chinese version of the Social Problem Solving Inventory short form (C-SPSI-R), Communication Ability Scale (CAS), Clinical Interactive Scale (CIS), and Interpersonal Dysfunction Checklist (IDC). Macao nursing students were more likely to use the two constructive or adaptive dimensions rather than the three dysfunctional dimensions of the C-SPSI-R to solve their problems. Multiple linear regression analysis revealed that communication ability (ß=.305, p<.0001), clinical interaction (ß=.129, p=.047), and interpersonal dysfunction (ß=-.402, p<.0001) were associated with social problem-solving after controlling for covariates. Macao has had no problem-solving training in its educational curriculum; an effective problem-solving training should be implemented as part of the curriculum. With so many changes in healthcare today, nurses must be good social problem-solvers in order to deliver holistic care. Copyright © 2012 Elsevier Ltd. All rights reserved.

Factors associated with adolescent and caregiver reported problems in using asthma medications.

PubMed

Sleath, Betsy; Carpenter, Delesha M; Walsh, Kathleen E; Davis, Scott A; Watson, Claire Hayes; Lee, Charles; Loughlin, Ceila E; Garcia, Nacire; Reuland, Daniel S; Tudor, Gail

2018-04-18

The purpose of this study was to: (a) describe the types of medication problems/concerns youth with asthma and their caregivers reported and (b) examine the association between socio-demographic characteristics and youth and caregiver reported medication problems/concerns. English-and Spanish-speaking youth ages 11-17 with persistent asthma were recruited at four pediatric clinics. Youth were interviewed and caregivers completed questionnaires about reported asthma medication concerns/problems. Multiple logistic regression was used to analyze the data. Three hundred and fifty-nine youth were recruited. Eighty percent of youth and 70% of caregivers reported one or more problems in using asthma medications. The most commonly reported problems by youth were: (a) hard to remember when to take the asthma medication (54%) and (b) hard to use asthma medication at school (34%). Younger children were significantly more likely to report difficulty in understanding their asthma medication's directions and difficulty reading the print on the medication's package. Caregivers' top-reported problem was that it is hard for their child to remember to take their asthma medications (49%). Caregivers without Medicaid were significantly more likely to express difficulty paying for their child's asthma medications. Difficulty remembering to take asthma medication was a significant problem for youth and their caregivers. Providers should work with youth and their caregivers to identify asthma medication problems and discuss strategies to address those problems.

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PubMed Central

Rahman, Md. Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D. W.; Labrique, Alain B.; Rashid, Mahbubur; Christian, Parul; West, Keith P.

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. PMID:29261760

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis.

PubMed

Kabir, Alamgir; Rahman, Md Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D W; Labrique, Alain B; Rashid, Mahbubur; Christian, Parul; West, Keith P

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 - -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.

Schizotypy and Performance on an Insight Problem-Solving Task: The Contribution of Persecutory Ideation.

PubMed

Cosgrave, Jan; Haines, Ross; Golodetz, Stuart; Claridge, Gordon; Wulff, Katharina; van Heugten-van der Kloet, Dalena

2018-01-01

Insight problem solving is thought to underpin creative thought as it incorporates both divergent (generating multiple ideas and solutions) and convergent (arriving at the optimal solution) thinking approaches. The current literature on schizotypy and creativity is mixed and requires clarification. An alternate approach was employed by designing an exploratory web-based study using only correlates of schizotypal traits (paranoia, dissociation, cognitive failures, fantasy proneness, and unusual sleep experiences) and examining which (if any) predicted optimal performance on an insight problem-solving task. One hundred and twenty-one participants were recruited online from the general population and completed the number reduction task. The discovery of the hidden rule (HR) was used as a measure of insight. Multivariate logistic regression analyses highlighted persecutory ideation to best predict the discovery of the HR (OR = 1.05; 95% CI 1.01-1.10, p = 0.017), with a one-point increase in persecutory ideas corresponding to the participant being 5% more likely to discover the HR. This result suggests that persecutory ideation, above other schizotypy correlates, may be involved in insight problem solving.

Psychosocial Consequences of Caregiver Transitions for Maltreated Youth Entering Foster Care: The Moderating Impact of Community Violence Exposure

PubMed Central

Garrido, Edward F.; Culhane, Sara E.; Petrenko, Christie L. M.; Taussig, Heather N.

2011-01-01

Youth who experience a greater number of caregiver transitions during childhood are at risk for developing a host of psychosocial problems. Although researchers have examined individual-level factors that may moderate this association, no known studies have examined the impact of community-level factors. The current study investigated whether community violence exposure moderated the association between number of prior caregiver transitions and increases in levels of externalizing and internalizing problems for a sample of youth entering foster care. Participants included 156 youth (age 9 to 11 at first assessment) removed from their homes because of maltreatment. Youth provided reports of caregiver transitions and community violence exposure at baseline, and caregivers, teachers, and youth reported on externalizing and internalizing problems 18–22 months later. Results from hierarchical multiple regression analyses indicated that youth with a greater number of caregiver transitions and higher levels of community violence exposure evidenced significant increases in levels of psychosocial problems. The results of the study are discussed in terms of their implications for child welfare services. PMID:21729018

The minimal residual QR-factorization algorithm for reliably solving subset regression problems

NASA Technical Reports Server (NTRS)

Verhaegen, M. H.

1987-01-01

A new algorithm to solve test subset regression problems is described, called the minimal residual QR factorization algorithm (MRQR). This scheme performs a QR factorization with a new column pivoting strategy. Basically, this strategy is based on the change in the residual of the least squares problem. Furthermore, it is demonstrated that this basic scheme might be extended in a numerically efficient way to combine the advantages of existing numerical procedures, such as the singular value decomposition, with those of more classical statistical procedures, such as stepwise regression. This extension is presented as an advisory expert system that guides the user in solving the subset regression problem. The advantages of the new procedure are highlighted by a numerical example.

The Interactive Influence of Cannabis-Related Negative Expectancies and Coping Motives on Cannabis Use Behavior and Problems.

PubMed

Foster, Dawn W; Jeffries, Emily R; Zvolensky, Michael J; Buckner, Julia D

2016-09-18

The present study tested whether coping motives for cannabis use moderate the effect of negative expectancies on cannabis use. Participants were 149 (36.2% female, 61.59% non-Hispanic Caucasian) current cannabis users aged 18-36 (M = 21.01, SD = 3.09) who completed measures of cannabis-related expectancies and motives for use. Hierarchical multiple regressions were employed to investigate the predictive value of the interaction between negative expectancies and coping motives on cannabis use outcomes. Results revealed interactions between negative expectancies and coping motives with respect to past 90 day cannabis use frequency and cannabis problems. Global negative effects expectancies were associated with less frequent cannabis use, particularly among those with fewer coping motives. However, negative expectancies were related to more cannabis problems, particularly among those with higher coping motives. These results suggest it may be advisable to take coping motives into account when addressing expectancies among cannabis users.

Importance of hypertension and social isolation in causing sleep disruption in dementia.

PubMed

Eshkoor, Sima Ataollahi; Hamid, Tengku Aizan; Nudin, Siti Sa'adiah Hassan; Mun, Chan Yoke

2014-02-01

This study aimed to determine the effects of diabetes mellitus (DM), hypertension (HT), heart disease, social isolation, and sociodemographic factors on sleep in the elderly patients with dementia. Samples included 1210 noninstitutionalized, Malaysian elderly patients with dementia. The multiple logistic regression analysis was applied to estimate the risk of sleep disturbances among respondents. Approximately 41% of the patients experienced sleep problems. The results showed that age (odds ratio [OR] = 1.02), social isolation (OR = 1.33), and HT (OR = 1.53) significantly increased sleep disruption in respondents (P <.05). Furthermore, education (OR =.63) and non-Malay ethnicity (OR = 0.63) significantly decreased sleep problems (P <.05). It was found that DM, heart disease, sex differences, and marital status were not significant predictors of sleep disturbances (P >.05). It was concluded that age, social isolation, and HT increased sleep disruption but education and ethnic non-Malay reduced the risk of sleep problems. Moreover, HT was the most important variable to increase sleep disturbances in the elderly patients with dementia.

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

PTSD Symptom Severities, Interpersonal Traumas, and Benzodiazepines Are Associated with Substance-Related Problems in Trauma Patients

PubMed Central

Guina, Jeffrey; Nahhas, Ramzi W.; Goldberg, Adam J.; Farnsworth, Seth

2016-01-01

Background: Trauma is commonly associated with substance-related problems, yet associations between specific substances and specific posttraumatic stress disorder symptoms (PTSSs) are understudied. We hypothesized that substance-related problems are associated with PTSS severities, interpersonal traumas, and benzodiazepine prescriptions. Methods: Using a cross-sectional survey methodology in a consecutive sample of adult outpatients with trauma histories (n = 472), we used logistic regression to examine substance-related problems in general (primary, confirmatory analysis), as well as alcohol, tobacco, and illicit drug problems specifically (secondary, exploratory analyses) in relation to demographics, trauma type, PTSSs, and benzodiazepine prescriptions. Results: After adjusting for multiple testing, several factors were significantly associated with substance-related problems, particularly benzodiazepines (AOR = 2.78; 1.99 for alcohol, 2.42 for tobacco, 8.02 for illicit drugs), DSM-5 PTSD diagnosis (AOR = 1.92; 2.38 for alcohol, 2.00 for tobacco, 2.14 for illicit drugs), most PTSSs (especially negative beliefs, recklessness, and avoidance), and interpersonal traumas (e.g., assaults and child abuse). Conclusion: In this clinical sample, there were consistent and strong associations between several trauma-related variables and substance-related problems, consistent with our hypotheses. We discuss possible explanations and implications of these findings, which we hope will stimulate further research, and improve screening and treatment. PMID:27517964

Understanding catastrophizing from a misdirected problem-solving perspective.

PubMed

Flink, Ida K; Boersma, Katja; MacDonald, Shane; Linton, Steven J

2012-05-01

The aim is to explore pain catastrophizing from a problem-solving perspective. The links between catastrophizing, problem framing, and problem-solving behaviour are examined through two possible models of mediation as inferred by two contemporary and complementary theoretical models, the misdirected problem solving model (Eccleston & Crombez, 2007) and the fear-anxiety-avoidance model (Asmundson, Norton, & Vlaeyen, 2004). In this prospective study, a general population sample (n= 173) with perceived problems with spinal pain filled out questionnaires twice; catastrophizing and problem framing were assessed on the first occasion and health care seeking (as a proxy for medically oriented problem solving) was assessed 7 months later. Two different approaches were used to explore whether the data supported any of the proposed models of mediation. First, multiple regressions were used according to traditional recommendations for mediation analyses. Second, a bootstrapping method (n= 1000 bootstrap resamples) was used to explore the significance of the indirect effects in both possible models of mediation. The results verified the concepts included in the misdirected problem solving model. However, the direction of the relations was more in line with the fear-anxiety-avoidance model. More specifically, the mediation analyses provided support for viewing catastrophizing as a mediator of the relation between biomedical problem framing and medically oriented problem-solving behaviour. These findings provide support for viewing catastrophizing from a problem-solving perspective and imply a need to examine and address problem framing and catastrophizing in back pain patients. ©2011 The British Psychological Society.

Predictors of psychological distress after diagnosis in breast cancer patients and patients with benign breast problems.

PubMed

Ando, Noriko; Iwamitsu, Yumi; Kuranami, Masaru; Okazaki, Shigemi; Nakatani, Yuki; Yamamoto, Kenji; Watanabe, Masahiko; Miyaoka, Hitoshi

2011-01-01

The objective of this study was to determine how age and psychological characteristics assessed prior to diagnosis could predict psychological distress in outpatients immediately after disclosure of their diagnosis. This is a longitudinal and prospective study, and participants were breast cancer patients and patients with benign breast problems (BBP). Patients were asked to complete questionnaires to determine levels of the following: trait anxiety (State-Trait Anxiety Inventory), negative emotional suppression (Courtauld Emotional Control Scale), life stress events (Life Experiences Survey), and psychological distress (Profile of Mood Status) prior to diagnosis. They were asked to complete a questionnaire measuring psychological distress after being told their diagnosis. We analyzed a total of 38 women diagnosed with breast cancer and 95 women diagnosed with a BBP. A two-way analysis of variance (prior to, after diagnosis × cancer, benign) showed that psychological distress after diagnosis among breast cancer patients was significantly higher than in patients with a BBP. The multiple regression model accounted for a significant amount of variance in the breast cancer group (model adjusted R(2) = 0.545, p < 0.001), and only trait anxiety was statistically significant (β = 0.778, p < 0.001). In the BBP group, the multiple regression analysis yielded a significant result (model adjusted R(2) = 0.462, p < 0.001), with trait anxiety and negative life changes as statistically significant factors (β = 0.449 and 0.324 respectively; p < 0.01). In both groups, trait anxiety assessed prior to diagnosis was the significant predictor of psychological distress after diagnosis, and might have prospects as a screening method for psychologically vulnerable women. Copyright © 2011 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.

Alcohol consumption patterns among vocational school students in central Thailand.

PubMed

Chaveepojnkamjorn, Wisit

2012-11-01

The objective of this study was to evaluate alcohol consumption patterns among vocational school students in central Thailand. We conducted a cross sectional study among 1,803 vocational students (80.4 % aged < 17 years) in central Thailand using a self-administered questionnaire which consisted of 2 parts: sociodemographic factors and alcohol drinking behavior from December 2007 to February 2008. Descriptive statistics, a chi-square test and multiple logistic regression were used to analyze the data. The results of this study showed 40.9% of male students and 20.9% of female students drank alcoholic beverages. Multiple logistic regression analysis revealed 2 factors were associated with alcohol consumption among male subjects: field of study (OR 1.5, 95% CI 1.1-2.0), and GPA (OR < 2 = 1.8; 95% CI 1.2-2.7; OR > 3 = 0.6; 95% CI 0.4-0.9). The three most popular venues for drinking were at parties (43.1%), at home/in the dormitory (34.9%) and in bars or saloons near the school (20.9%). Fifty-three point two percent of males drinks alcohol 1-2 times per month and time, 47% drank > 2 times per month. Nearly 78% of female students drink alcohol 1-2 times per month and 22% drink alcohol > 2 time per month. Forty point nine percent of male students consumed 1-2 drinks per time and 36% consumed more than 4 drinks per time. Fifty point four percent of females drank 2 drinks per month. One-third of male students said they engaged in binge drinking in a 2-week period and 14% of girls said they binge drank in a 2-week period. Alcohol consumption is a significant problem among Thai vocational school students. Measures for managing this problem are discussed.

Are adolescents with high self-esteem protected from psychosomatic symptomatology?

PubMed

Piko, Bettina F; Varga, Szabolcs; Mellor, David

2016-06-01

This study investigated the role of self-esteem, social (need to belong, loneliness, competitiveness, and shyness), and health (smoking, drinking) behaviors in Hungarian adolescents' psychosomatic symptoms. Our sample of 490 students (ages 14-19 years) from Debrecen (Hungary) completed the questionnaires. Besides descriptive statistics, correlation and multiple regression analyses were applied to test interrelationships. Frequency analysis revealed that fatigue was the most commonly experienced psychosomatic symptom in this sample, followed by sleeping problems and (lower) back pain. Girls reported experiencing more symptoms. Multiple regression analyses suggested that (1) need to belong, shyness, and competitiveness may serve as social behavioral risk factors for adolescents' psychosomatic symptomatology, whereas (2) self-esteem may play a protective role. The role of social and health behaviors was modified when analyzed by gender: the psychosomatic index score was positively related to smoking and shyness among girls, and need to belong among boys. Self-esteem provided protection for both sexes. We conclude that problems with social relationships (namely, unmet need to belong, competitiveness, and shyness) may lead to psychosomatic health complaints, whereas self-esteem may serve as a protection. Findings suggest that social skills training and strengthening self-esteem should be an important part of children's health promotion programs in schools to improve their psychosomatic health and well-being. • Despite being free of serious physical illness, many adolescents often report subjective health complaints, such as psychosomatic symptoms • As children in this life stage develop independence and autonomy, new types of social relationships, and identity, their social needs and skills also change What is new: • Need to belong, shyness, and competitiveness may serve as social behavioral risk factors for adolescents' psychosomatic symptomatology, whereas self-esteem may play a protective role • The role of social and health behaviors may vary by gender.

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings

PubMed Central

Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

2017-01-01

Background Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Methods Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. Results A ‘Rich Picture’ was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. Conclusions When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further consideration. PMID:28062603

The risks for late adolescence of early adolescent marijuana use.

PubMed Central

Brook, J S; Balka, E B; Whiteman, M

1999-01-01

OBJECTIVES: The purpose of this study was to assess the relation of early adolescent marijuana use to late adolescent problem behaviors, drug-related attitudes, drug problems, and sibling and peer problem behavior. METHODS: African American (n = 627) and Puerto Rican (n = 555) youths completed questionnaires in their classrooms initially and were individually interviewed 5 years later. Logistic regression analysis estimated increases in the risk of behaviors or attitudes in late adolescence associated with more frequent marijuana use in early adolescence. RESULTS: Early adolescent marijuana use increased the risk in late adolescence of not graduating from high school; delinquency; having multiple sexual partners; not always using condoms; perceiving drugs as not harmful; having problems with cigarettes, alcohol, and marijuana; and having more friends who exhibit deviant behavior. These relations were maintained with controls for age, sex, ethnicity, and, when available, earlier psychosocial measures. CONCLUSIONS: Early adolescent marijuana use is related to later adolescent problems that limit the acquisition of skills necessary for employment and heighten the risks of contracting HIV and abusing legal and illegal substances. Hence, assessments of and treatments for adolescent marijuana use need to be incorporated in clinical practice. PMID:10511838

Behavioral problems and the occurrence of tobacco, cannabis, and coca paste smoking in Chile: evidence based on multivariate response models for school survey data.

PubMed

Caris, Luis; Anthony, Christopher B; Ríos-Bedoya, Carlos F; Anthony, James C

2009-09-01

In this study we estimate suspected links between youthful behavioral problems and smoking of tobacco, cannabis, and coca paste. In the Republic of Chile, school-attending youths were sampled from all 13 regions of the country, with sample size of 46,907 youths from 8th to 12th grades. A Generalized Estimating Equations (GEE) approach to multiple logistic regression was used to address three interdependent response variables, tobacco smoking, cannabis smoking, and coca paste smoking, and to estimate associations. Drug-specific adjusted slope estimates indicate that youths at the highest levels of behavioral problems are an estimated 1.1 times more likely to have started smoking tobacco, an estimated 1.6 times more likely to have started cannabis smoking, and an estimated 2.0 times more likely to have started coca paste smoking, as compared to youths at the lowest level of behavioral problems (p<0.001). In Chile, there is an association linking behavioral problems with onsets of smoking tobacco and cannabis, as well as coca paste; strength of association is modestly greater for coca paste smoking.

Estimating increment-decrement life tables with multiple covariates from panel data: the case of active life expectancy.

PubMed

Land, K C; Guralnik, J M; Blazer, D G

1994-05-01

A fundamental limitation of current multistate life table methodology-evident in recent estimates of active life expectancy for the elderly-is the inability to estimate tables from data on small longitudinal panels in the presence of multiple covariates (such as sex, race, and socioeconomic status). This paper presents an approach to such an estimation based on an isomorphism between the structure of the stochastic model underlying a conventional specification of the increment-decrement life table and that of Markov panel regression models for simple state spaces. We argue that Markov panel regression procedures can be used to provide smoothed or graduated group-specific estimates of transition probabilities that are more stable across short age intervals than those computed directly from sample data. We then join these estimates with increment-decrement life table methods to compute group-specific total, active, and dependent life expectancy estimates. To illustrate the methods, we describe an empirical application to the estimation of such life expectancies specific to sex, race, and education (years of school completed) for a longitudinal panel of elderly persons. We find that education extends both total life expectancy and active life expectancy. Education thus may serve as a powerful social protective mechanism delaying the onset of health problems at older ages.

A simplified calculation procedure for mass isotopomer distribution analysis (MIDA) based on multiple linear regression.

PubMed

Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio

2016-10-01

We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two 13 C atoms ( 13 C 2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of 13 C 2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% 13 C 2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Summary of Documentation for DYNA3D-ParaDyn's Software Quality Assurance Regression Test Problems

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

Zywicz, Edward

The Software Quality Assurance (SQA) regression test suite for DYNA3D (Zywicz and Lin, 2015) and ParaDyn (DeGroot, et al., 2015) currently contains approximately 600 problems divided into 21 suites, and is a required component of ParaDyn’s SQA plan (Ferencz and Oliver, 2013). The regression suite allows developers to ensure that software modifications do not unintentionally alter the code response. The entire regression suite is run prior to permanently incorporating any software modification or addition. When code modifications alter test problem results, the specific cause must be determined and fully understood before the software changes and revised test answers can bemore » incorporated. The regression suite is executed on LLNL platforms using a Python script and an associated data file. The user specifies the DYNA3D or ParaDyn executable, number of processors to use, test problems to run, and other options to the script. The data file details how each problem and its answer extraction scripts are executed. For each problem in the regression suite there exists an input deck, an eight-processor partition file, an answer file, and various extraction scripts. These scripts assemble a temporary answer file in a specific format from the simulation results. The temporary and stored answer files are compared to a specific level of numerical precision, and when differences are detected the test problem is flagged as failed. Presently, numerical results are stored and compared to 16 digits. At this accuracy level different processor types, compilers, number of partitions, etc. impact the results to various degrees. Thus, for consistency purposes the regression suite is run with ParaDyn using 8 processors on machines with a specific processor type (currently the Intel Xeon E5530 processor). For non-parallel regression problems, i.e., the two XFEM problems, DYNA3D is used instead. When environments or platforms change, executables using the current source code and the new resource are created and the regression suite is run. If differences in answers arise, the new answers are retained provided that the differences are inconsequential. This bootstrap approach allows the test suite answers to evolve in a controlled manner with a high level of confidence. Developers also run the entire regression suite with (serial) DYNA3D. While these results normally differ from the stored (parallel) answers, abnormal termination or wildly different values are strong indicators of potential issues.« less

The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

PubMed

Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

2017-11-01

Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

MULTIPLE REGRESSION MODELS FOR HINDCASTING AND FORECASTING MIDSUMMER HYPOXIA IN THE GULF OF MEXICO

EPA Science Inventory

A new suite of multiple regression models were developed that describe the relationship between the area of bottom water hypoxia along the northern Gulf of Mexico and Mississippi-Atchafalaya River nitrate concentration, total phosphorus (TP) concentration, and discharge. Variabil...

Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis

PubMed Central

Khalil, Mohamed H.; Shebl, Mostafa K.; Kosba, Mohamed A.; El-Sabrout, Karim; Zaki, Nesma

2016-01-01

Aim: This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens’ eggs. Materials and Methods: Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. Results: The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. Conclusion: A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens. PMID:27651666

Good self-control as a buffering agent for adolescent substance use: an investigation in early adolescence with time-varying covariates.

PubMed

Wills, Thomas A; Ainette, Michael G; Stoolmiller, Mike; Gibbons, Frederick X; Shinar, Ori

2008-12-01

This study tested the prediction that self-control would have buffering effects for adolescent substance use (tobacco, alcohol, and marijuana) with regard to 3 risk factors: family life events, adolescent life events, and peer substance use. Participants were a sample of public school students (N = 1,767) who were surveyed at 4 yearly intervals between 6th grade and 9th grade. Good self-control was assessed with multiple indicators (e.g., planning and problem solving). Results showed that the impact of all 3 risk factors on substance use was reduced among persons with higher scores on good self-control. Buffering was found in cross-sectional analyses with multiple regression and in longitudinal analyses in a latent growth model with time-varying covariates. Implications for addressing self-control in prevention programs are discussed. 2008 APA, all rights reserved

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

PubMed

Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A

2017-02-01

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).

A Common Mechanism for Resistance to Oxime Reactivation of Acetylcholinesterase Inhibited by Organophosphorus Compounds

DTIC Science & Technology

2013-01-01

application of the Hammett equation with the constants rph in the chemistry of organophosphorus compounds, Russ. Chem. Rev. 38 (1969) 795–811. [13...of oximes and OP compounds and the ability of oximes to reactivate OP- inhibited AChE. Multiple linear regression equations were analyzed using...phosphonate pairs, 21 oxime/ phosphoramidate pairs and 12 oxime/phosphate pairs. The best linear regression equation resulting from multiple regression anal

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

The suggested "two subjects per variable" (2SPV) rule of thumb in the Austin and Steyerberg article is a chance to bring out some long-established and quite intuitive sample size considerations for both simple and multiple linear regression. This article distinguishes two of the major uses of regression models that imply very different sample size considerations, neither served well by the 2SPV rule. The first is etiological research, which contrasts mean Y levels at differing "exposure" (X) values and thus tends to focus on a single regression coefficient, possibly adjusted for confounders. The second research genre guides clinical practice. It addresses Y levels for individuals with different covariate patterns or "profiles." It focuses on the profile-specific (mean) Y levels themselves, estimating them via linear compounds of regression coefficients and covariates. By drawing on long-established closed-form variance formulae that lie beneath the standard errors in multiple regression, and by rearranging them for heuristic purposes, one arrives at quite intuitive sample size considerations for both research genres. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach.

PubMed

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate - adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

PubMed Central

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

Introduction Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis. Aim The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets. Methods Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA). Results Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed. Conclusion To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering 'hidden' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research. PMID:26080057

The Prediction Properties of Inverse and Reverse Regression for the Simple Linear Calibration Problem

NASA Technical Reports Server (NTRS)

Parker, Peter A.; Geoffrey, Vining G.; Wilson, Sara R.; Szarka, John L., III; Johnson, Nels G.

2010-01-01

The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.

Undergraduate Student Motivation in Modularized Developmental Mathematics Courses

ERIC Educational Resources Information Center

Pachlhofer, Keith A.

2017-01-01

This study used the Motivated Strategies for Learning Questionnaire in modularized courses at three institutions across the nation (N = 189), and multiple regression was completed to investigate five categories of student motivation that predicted academic success and course completion. The overall multiple regression analysis was significant and…

MULGRES: a computer program for stepwise multiple regression analysis

Treesearch

A. Jeff Martin

1971-01-01

MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

Categorical Variables in Multiple Regression: Some Cautions.

ERIC Educational Resources Information Center

O'Grady, Kevin E.; Medoff, Deborah R.

1988-01-01

Limitations of dummy coding and nonsense coding as methods of coding categorical variables for use as predictors in multiple regression analysis are discussed. The combination of these approaches often yields estimates and tests of significance that are not intended by researchers for inclusion in their models. (SLD)

Evaluating Alcoholics Anonymous's effect on drinking in Project MATCH using cross-lagged regression panel analysis.

PubMed

Magura, Stephen; Cleland, Charles M; Tonigan, J Scott

2013-05-01

The objective of the study is to determine whether Alcoholics Anonymous (AA) participation leads to reduced drinking and problems related to drinking within Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity), an existing national alcoholism treatment data set. The method used is structural equation modeling of panel data with cross-lagged partial regression coefficients. The main advantage of this technique for the analysis of AA outcomes is that potential reciprocal causation between AA participation and drinking behavior can be explicitly modeled through the specification of finite causal lags. For the outpatient subsample (n = 952), the results strongly support the hypothesis that AA attendance leads to increases in alcohol abstinence and reduces drinking/ problems, whereas a causal effect in the reverse direction is unsupported. For the aftercare subsample (n = 774), the results are not as clear but also suggest that AA attendance leads to better outcomes. Although randomized controlled trials are the surest means of establishing causal relations between interventions and outcomes, such trials are rare in AA research for practical reasons. The current study successfully exploited the multiple data waves in Project MATCH to examine evidence of causality between AA participation and drinking outcomes. The study obtained unique statistical results supporting the effectiveness of AA primarily in the context of primary outpatient treatment for alcoholism.

The Role of Early Language Difficulties in the Trajectories of Conduct Problems Across Childhood.

PubMed

Yew, Shaun Goh Kok; O'Kearney, Richard

2015-11-01

This study uses latent growth curve modelling to contrast the developmental trajectories of conduct problems across childhood for children with early language difficulties (LD) and those with typical language (TL). It also examines whether the presence of early language difficulties moderates the influence of child, parent and peers factors known to be associated with the development of conduct problems. Unconditional and language status conditional latent growth curves of conduct problems were estimated for a nationally representative cohort of children, comprising of 1627 boys (280 LD) and 1609 girls (159 LD) measured at ages 4-5, 6-7, 8-9 and 10-11. Multiple regression tested interaction between language status and predictors of the level and slope of the development of conduct symptoms. On average, children's conduct problems followed a curvilinear decrease. Compared to their TL peers, LD boys and girls had trajectories of conduct problems that had the same shape but with persistently higher levels. Among boys, LD amplified the contributions of parental hostility and SES and protected against the contributions of sociability and maternal psychological distress to a high level of conduct problems. In low SES boys, LD was a vulnerability to a slower rate of decline in conduct problems. Among girls, LD amplified the contributions of low pro-social behaviour to a higher level and sociability to a slower rate of decline of conduct problems while dampening the contribution of peer problems to a higher level of problems.

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

PubMed

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

Social Anxiety and Cannabis-Related Impairment: The Synergistic Influences of Peer and Parent Descriptive and Injunctive Normative Perceptions.

PubMed

Foster, Dawn W; Garey, Lorra; Buckner, Julia D; Zvolensky, Michael J

2016-06-06

Cannabis users, especially socially anxious cannabis users, are influenced by perceptions of other's use. The present study tested whether social anxiety interacted with perceptions about peer and parent beliefs to predict cannabis-related problems. Participants were 148 (36.5% female, 60.1% non-Hispanic Caucasian) current cannabis users aged 18-36 (M = 21.01, SD = 3.09) who completed measures of perceived descriptive and injunctive norms, social anxiety, and cannabis use behaviors. Hierarchical multiple regressions were employed to investigate the predictive value of the social anxiety X parent injunctive norms X peer norms interaction terms on cannabis use behaviors. Higher social anxiety was associated with more cannabis problems. A three-way interaction emerged between social anxiety, parent injunctive norms, and peer descriptive norms, with respect to cannabis problems. Social anxiety was positively related to more cannabis problems when parent injunctive norms were high (i.e., perceived approval) and peer descriptive norms were low. Results further showed that social anxiety was positively related to more cannabis problems regardless of parent injunctive norms. The present work suggests that it may be important to account for parent influences when addressing normative perceptions among young adult cannabis users. Additional research is needed to determine whether interventions incorporating feedback regarding parent norms impacts cannabis use frequency and problems.

Differential Effects of Formal and Informal Gambling on Symptoms of Problem Gambling During Voluntary Self-Exclusion.

PubMed

McCormick, Amanda V; Cohen, Irwin M; Davies, Garth

2018-01-18

Voluntary self-exclusion (VSE) programs enable problem gamblers to engage in a break from casino-based gambling. The current study analyzed the effects of a VSE program in British Columbia, Canada on problem gambling symptoms and the comparative reductions in problem gambling symptoms when participants abstained from gambling, continued to participate in non-casino based gambling, or attempted to violate their exclusion contract. 269 participants completed two telephone interviews over a 6-month period. Participants were administered the Problem Gambling Severity Index (PGSI). Substantial reductions in PGSI scores were observed after 6 months. Program violators had significantly smaller PGSI Difference Scores by Time 2 compared to those who continued to gamble outside of the casino and those who completely abstained from all gambling. There were no significant differences between those who gambled informally and those who abstained. A multiple regression identified that while access to counselling and length of enrollment also contributed to the reduction in PGSI scores, violation attempts were most strongly associated with smaller reductions in symptoms of problem gambling. These results imply that some gamblers can successfully engage in non-casino based forms of gambling and still experience reductions in symptoms of problem gambling. Future analyses will explore characteristics associated with group membership that may help to identify which participants can successfully engage in non-casino based gambling without re-triggering symptoms of problem gambling.

Predictors of anemia among pregnant women in Westmoreland, Jamaica

PubMed Central

Charles, Alyson M.; Campbell-Stennett, Dianne; Yatich, Nelly; Jolly, Pauline E.

2010-01-01

Anemia in pregnancy is a worldwide problem, but it is most prevalent in the developing world. This research project was conducted to determine the predictors of anemia in pregnant women in Westmoreland, Jamaica. A cross-sectional study design was conducted and descriptive, bivariate, and multiple logistic regression analyses were used. Body mass index, Mid-upper arm circumference, and the number of antenatal care visits showed a statistically significant association with anemia. Based on the results, we believe that maintaining a healthy body weight, and frequently visiting an antenatal clinic, will help to lower the prevalence of anemia among pregnant women in Westmoreland. PMID:20526925

Epidemiological analysis of factors influencing rate of progress in Echinococcus granulosus control in New Zealand.

PubMed Central

Burridge, M. J.; Schwabe, C. W.

1977-01-01

The factors influencing the rate of progress in Echinococcus granulosus control in New Zealand were analysed by hydatid control area using stepwise multiple regression techniques. The results indicated that the rate of progress was related positively to initial E. granulosus prevalence in dogs and the efficiency with which local authorities implemented national control policy, and negatively to the Maori proportion in the local population and the number of dogs per owner. Problems in analysis of the New Zealand data are discussed and improved methods of monitoring progress in hydatid disease control programmes are described. Images Fig. 1 PMID:265340

Advanced Statistics for Exotic Animal Practitioners.

PubMed

Hodsoll, John; Hellier, Jennifer M; Ryan, Elizabeth G

2017-09-01

Correlation and regression assess the association between 2 or more variables. This article reviews the core knowledge needed to understand these analyses, moving from visual analysis in scatter plots through correlation, simple and multiple linear regression, and logistic regression. Correlation estimates the strength and direction of a relationship between 2 variables. Regression can be considered more general and quantifies the numerical relationships between an outcome and 1 or multiple variables in terms of a best-fit line, allowing predictions to be made. Each technique is discussed with examples and the statistical assumptions underlying their correct application. Copyright © 2017 Elsevier Inc. All rights reserved.

Is This Kid a Likely Experimenter or a Likely Persister? An Analysis of Individual-Level and Family-Level Risk Factors Predicting Multiple Offending Among a Group of Adjudicated Youth.

PubMed

Buker, Hasan; Erbay, Ayhan

2018-02-01

To implement effective diversion programs and determine for a well-suited intervention strategy, ascertaining who, among the adjudicated youth, is more likely to involve in multiple offending, rather than desisting after an initial delinquent behavior, is of great significance. The overall objective of this study, therefore, is to contribute to the existing knowledge on assessing the risks for multiple offending during juvenile adjudication processes. In this regard, this study examined the predicting powers of several individual-level and family-level risk factors on multiple offending during adolescence, based on a data set derived from court-ordered social examination reports (SERs) on 400 adjudicated youth in Turkey. Two binomial regression models were implemented to test the predictor values of various risk factors from these two domains. Results indicated the following as significant predictors of multiple offending among the subjects: younger age of onset in delinquency, dropping out of school, having delinquent/drug abusing (risky) friends, being not able to share problems with the family, increased number of siblings, and having a domestically migrated family. Conclusively, these findings were compared with the existing literature, and the policy implications and recommendations for future research were discussed.

Use of Thematic Mapper for water quality assessment

NASA Technical Reports Server (NTRS)

Horn, E. M.; Morrissey, L. A.

1984-01-01

The evaluation of simulated TM data obtained on an ER-2 aircraft at twenty-five predesignated sample sites for mapping water quality factors such as conductivity, pH, suspended solids, turbidity, temperature, and depth, is discussed. Using a multiple regression for the seven TM bands, an equation is developed for the suspended solids. TM bands 1, 2, 3, 4, and 6 are used with logarithm conductivity in a multiple regression. The assessment of regression equations for a high coefficient of determination (R-squared) and statistical significance is considered. Confidence intervals about the mean regression point are calculated in order to assess the robustness of the regressions used for mapping conductivity, turbidity, and suspended solids, and by regressing random subsamples of sites and comparing the resultant range of R-squared, cross validation is conducted.

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

EPA Science Inventory

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Determining the Spatial and Seasonal Variability in OM/OC Ratios across the U.S. Using Multiple Regression

EPA Science Inventory

Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses com...

Analysis and Interpretation of Findings Using Multiple Regression Techniques

ERIC Educational Resources Information Center

Hoyt, William T.; Leierer, Stephen; Millington, Michael J.

2006-01-01

Multiple regression and correlation (MRC) methods form a flexible family of statistical techniques that can address a wide variety of different types of research questions of interest to rehabilitation professionals. In this article, we review basic concepts and terms, with an emphasis on interpretation of findings relevant to research questions…

Tracking the Gender Pay Gap: A Case Study

ERIC Educational Resources Information Center

Travis, Cheryl B.; Gross, Louis J.; Johnson, Bruce A.

2009-01-01

This article provides a short introduction to standard considerations in the formal study of wages and illustrates the use of multiple regression and resampling simulation approaches in a case study of faculty salaries at one university. Multiple regression is especially beneficial where it provides information on strength of association, specific…

Estimating air drying times of lumber with multiple regression

Treesearch

William T. Simpson

2004-01-01

In this study, the applicability of a multiple regression equation for estimating air drying times of red oak, sugar maple, and ponderosa pine lumber was evaluated. The equation allows prediction of estimated air drying times from historic weather records of temperature and relative humidity at any desired location.

Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan

2013-01-01

The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…

Multiple Regression: A Leisurely Primer.

ERIC Educational Resources Information Center

Daniel, Larry G.; Onwuegbuzie, Anthony J.

Multiple regression is a useful statistical technique when the researcher is considering situations in which variables of interest are theorized to be multiply caused. It may also be useful in those situations in which the researchers is interested in studies of predictability of phenomena of interest. This paper provides an introduction to…

Using Monte Carlo Techniques to Demonstrate the Meaning and Implications of Multicollinearity

ERIC Educational Resources Information Center

Vaughan, Timothy S.; Berry, Kelly E.

2005-01-01

This article presents an in-class Monte Carlo demonstration, designed to demonstrate to students the implications of multicollinearity in a multiple regression study. In the demonstration, students already familiar with multiple regression concepts are presented with a scenario in which the "true" relationship between the response and…

Assessing the Impact of Influential Observations on Multiple Regression Analysis on Human Resource Research.

ERIC Educational Resources Information Center

Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.

1999-01-01

A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)

Fatigue as a precursor to polymyalgia rheumatica: an explorative retrospective cohort study.

PubMed

Green, D J; Muller, S; Mallen, C D; Hider, S L

2015-05-01

Polymyalgia rheumatica (PMR) is the commonest inflammatory disorder of older adults. Although not part of the recently published classification criteria, patients with PMR frequently complain of fatigue. We compared consultation for fatigue and sleep problems between individuals with and without PMR. Consulters receiving a Read-coded diagnosis of PMR at nine general practices between 2000 and 2009 were matched by age, gender, general practice, and year of consultation to four patients without PMR. Fatigue and sleep problems were defined using Read codes. Cox regression was used to determine the association between PMR diagnosis and consultation for a fatigue/sleep problem. In total, 549 PMR patients were identified. Their mean (SD) age was 73.9 (8.6) years and 71% of the participants were female. Prior to the index date, 33 PMR patients and 80 matched non-PMR patients consulted with fatigue (0.43 vs. 0.25 consultations per 10 000 person-years, p = 0.006). PMR was associated with significantly more multiple fatigue consultations in the 12 months before PMR diagnosis [hazard ratio (HR) 1.95, 95% confidence interval (CI) 1.23-3.08]; no significant difference was seen in rates of consultations for sleep problems between patients with and without PMR. PMR patients were significantly more likely to have had multiple fatigue consultations before being diagnosed with PMR. Given the overproduction of inflammatory cytokines seen in PMR, this fatigue may represent a prodromal phase prior to consulting with more classical musculoskeletal symptoms. This suggests that clinicians should consider PMR as a potential diagnosis in older patients consulting with fatigue.

A bi-national comparative study of health behaviors of Koreans in South Korea and Korean Americans in California.

PubMed

Ryu, So Yeon; Crespi, Catherine M; Maxwell, Annette E

2013-12-01

Few studies have compared health behaviors of Koreans in their home country and Korean Americans. Using 2009 data from the Community Health Survey (South Korea) and the California Health Interview Survey (USA), we compared native Koreans and Korean Americans, grouped by level of acculturation, on prevalence of specific health behaviors and self-rated health, and conducted multiple logistic regression comparing the odds of these behaviors among the groups adjusted for demographic variables. While Korean Americans exhibit healthier behaviors than Koreans in some areas (e.g., reduced smoking and binge drinking in men, increased utilization of flu vaccinations), we also identified problem behaviors (e.g., increased body weight in Korean American men, uptake of alcohol drinking and smoking among Korean American women). Findings support the critical need for health promotion programs addressing these health behaviors to prevent future health problems among Korean Americans.

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

PubMed Central

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-01-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

A hybrid algorithm for selecting head-related transfer function based on similarity of anthropometric structures

NASA Astrophysics Data System (ADS)

Zeng, Xiang-Yang; Wang, Shu-Guang; Gao, Li-Ping

2010-09-01

As the basic data for virtual auditory technology, head-related transfer function (HRTF) has many applications in the areas of room acoustic modeling, spatial hearing and multimedia. How to individualize HRTF fast and effectively has become an opening problem at present. Based on the similarity and relativity of anthropometric structures, a hybrid HRTF customization algorithm, which has combined the method of principal component analysis (PCA), multiple linear regression (MLR) and database matching (DM), has been presented in this paper. The HRTFs selected by both the best match and the worst match have been applied into obtaining binaurally auralized sounds, which are then used for subjective listening experiments and the results are compared. For the area in the horizontal plane, the localization results have shown that the selection of HRTFs can enhance the localization accuracy and can also abate the problem of front-back confusion.

Studies in Software Cost Model Behavior: Do We Really Understand Cost Model Performance?

NASA Technical Reports Server (NTRS)

Lum, Karen; Hihn, Jairus; Menzies, Tim

2006-01-01

While there exists extensive literature on software cost estimation techniques, industry practice continues to rely upon standard regression-based algorithms. These software effort models are typically calibrated or tuned to local conditions using local data. This paper cautions that current approaches to model calibration often produce sub-optimal models because of the large variance problem inherent in cost data and by including far more effort multipliers than the data supports. Building optimal models requires that a wider range of models be considered while correctly calibrating these models requires rejection rules that prune variables and records and use multiple criteria for evaluating model performance. The main contribution of this paper is to document a standard method that integrates formal model identification, estimation, and validation. It also documents what we call the large variance problem that is a leading cause of cost model brittleness or instability.

Social integration of people with severe mental illness: relationships between symptom severity, professional assistance, and natural support.

PubMed

Tsai, Jack; Desai, Rani A; Rosenheck, Robert A

2012-04-01

Reducing dependency on professionals and social integration has been a major goal of recovery-oriented mental health services. This cross-sectional study examined 531 male outpatients at three public mental health centers in Southern Connecticut. Hierarchical multiple regression analyses were conducted to answer: (1) Do clients who have more severe clinical problems rely more on professional support and mental health services, and rely less on natural supports? (2) Do clients who have greater natural supports rely less on professional support and mental health services? Results found clients with more severe clinical problems do not rely more on professional support and report less natural social support. Natural support was also found to be a complement, rather than a substitute for professional support. These findings suggest the social integration of male clients with severe mental illness may include being more connected to mental health providers even as they develop increasing natural supports.

COINSTAC: Decentralizing the future of brain imaging analysis

PubMed Central

Ming, Jing; Verner, Eric; Sarwate, Anand; Kelly, Ross; Reed, Cory; Kahleck, Torran; Silva, Rogers; Panta, Sandeep; Turner, Jessica; Plis, Sergey; Calhoun, Vince

2017-01-01

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications. PMID:29123643

Orthostatic symptoms, blood pressure and working postures of factory and service workers over an observed workday.

PubMed

Ngomo, Suzy; Messing, Karen; Perrault, Hélène; Comtois, Alain

2008-11-01

North American workers usually stand while working, and prolonged standing is associated with discomfort and cardiovascular problems. Moving may alleviate the problems, but optimum mobility is unknown. The effects of variations in mobility were explored among (1) 34 health care workers whose symptoms of orthostatic intolerance (OI) were recorded after work; (2) 45 factory and laundry workers. Postures were observed over a workday and blood pressure (BP) and heart rate (HR) of both groups were recorded before and after work. Among health care workers, 65% manifested OI symptoms. In a multiple logistic regression, presence of >or= 1 symptom of OI was associated with static postures and being female (p=0.001). More static standing was associated with a larger drop in BP (p=0.04) in both populations. The results suggest that more static standing postures are associated with OI and musculoskeletal symptoms and with a subclinical drop in BP.

One-Year Follow-Up of Suicidal Adolescents: Parental History of Mental Health Problems and Time to Post-Hospitalization Attempt

PubMed Central

King, Cheryl A.; Kerr, David C. R.; Passarelli, Michael N.; Foster, Cynthia Ewell; Merchant, Christopher R.

2017-01-01

This longitudinal study of recently hospitalized suicidal youth examined parental mental health history in addition to several indices of adolescent functioning as risk factors for time-to-suicide attempt over a 1-year period. Participants were 352 adolescents (253 girls, 99 boys; ages 13–17 years) who participated in self-report and interview assessments within 1 week of hospitalization and 6 weeks, 3, 6, and 12 months post-hospitalization. Multivariable proportional hazards regression modeled time-to-suicide attempt. Results indicate that adolescents were almost twice as likely to make a suicide attempt if they had at least one biological parent with mental health problems. Risk was also increased for adolescents with baseline histories of multiple previous suicide attempts, more severe suicidal ideation and more severe functional impairment. Findings suggest the need to consider the family system when intervening with suicidal youth. PMID:19967398

Data-driven discovery of partial differential equations.

PubMed

Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2017-04-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

Differential Associations of UPPS-P Impulsivity Traits With Alcohol Problems.

PubMed

McCarty, Kayleigh N; Morris, David H; Hatz, Laura E; McCarthy, Denis M

2017-07-01

The UPPS-P model posits that impulsivity comprises five factors: positive urgency, negative urgency, lack of planning, lack of perseverance, and sensation seeking. Negative and positive urgency are the traits most consistently associated with alcohol problems. However, previous work has examined alcohol problems either individually or in the aggregate, rather than examining multiple problem domains simultaneously. Recent work has also questioned the utility of distinguishing between positive and negative urgency, as this distinction did not meaningfully differ in predicting domains of psychopathology. The aims of this study were to address these issues by (a) testing unique associations of UPPS-P with specific domains of alcohol problems and (b) determining the utility of distinguishing between positive and negative urgency as risk factors for specific alcohol problems. Associations between UPPS-P traits and alcohol problem domains were examined in two cross-sectional data sets using negative binomial regression models. In both samples, negative urgency was associated with social/interpersonal, self-perception, risky behaviors, and blackout drinking problems. Positive urgency was associated with academic/occupational and physiological dependence problems. Both urgency traits were associated with impaired control and self-care problems. Associations for other UPPS-P traits did not replicate across samples. Results indicate that negative and positive urgency have differential associations with alcohol problem domains. Results also suggest a distinction between the type of alcohol problems associated with these traits-negative urgency was associated with problems experienced during a drinking episode, whereas positive urgency was associated with alcohol problems that result from longer-term drinking trends.

Prevalence of behavioral problems and related family functioning among middle school students in an eastern city of China.

PubMed

Ma, Xiquan; Yao, Yuhong; Zhao, Xudong

2013-03-01

This study was carried out to explore the prevalence of behavioral problems among adolescents in junior high school as well as their families' levels of function or dysfunction that contribute to children's behavioral problems in Mainland China. One thousand, four hundred and seventy-six adolescents (ages 12-17 years) and their families participated in the study. Parents completed a self-administered questionnaire consisting of the Child Behavior Checklist (CBCL), Family Assessment Device (FAD) and a number of demographic questions. Student's t-tests, chi-square tests and stepwise multiple regression models were performed to examine the variables. The estimated prevalence of behavioral problems was 10.5% based on the cutoff point for behavioral problems according to the CBCL. Behavioral problems identified by the CBCL occurred differently at various developmental stages (F = 10.06, P = 0.007). The study showed that inappropriate affective responsiveness, poor affective involvement and low ability of problem solving in the family were significantly associated with increased risk for externalizing behavior problems and total behavior problems of boys. Inappropriate affective responsiveness and poor communication in the family were significantly associated with increased risk for internalizing problems for boys. Poorly established patterns of family behavior were important factors contributing to the development of externalizing behavior problems, internalizing behavior problems and total behavior problems for girls'. The present findings suggest that functional levels of family are associated with the adolescent's mental health, and that specific family dynamics may influence the development of behavioral problems among adolescents in China. Copyright © 2012 Blackwell Publishing Asia Pty Ltd.

Early breastfeeding problems: A mixed method study of mothers' experiences.

PubMed

Feenstra, Maria Monberg; Jørgine Kirkeby, Mette; Thygesen, Marianne; Danbjørg, Dorthe B; Kronborg, Hanne

2018-06-01

Breastfeeding problems are common and associated with early cessation. Still length of postpartum hospital stay has been reduced. This leaves new mothers to establish breastfeeding at home with less support from health care professionals. The objective was to explore mothers' perspectives on when breastfeeding problems were the most challenging and prominent early postnatal. The aim was also to identify possible factors associated with the breastfeeding problems. In a cross-sectional study, a mixed method approach was used to analyse postal survey data from 1437 mothers with full term singleton infants. Content analysis was used to analyse mothers' open text descriptions of their most challenging breastfeeding problem. Multiple logistic regression was used to calculate odds ratios for early breastfeeding problems according to sociodemographic- and psychosocial factors. Up to 40% of the mothers had experienced early breastfeeding problems. The problems were associated with the mother, the infant and to lack of support from health care professionals. Most prominent problems were infant's inability to latch on (40%) and mothers having sore, wounded and cracked nipples (38%). Pain often occurred when experiencing breastfeeding problems. Factors associated with the problems were primiparity, lower self-efficacy and lower self-perceived knowledge of breastfeeding. Mothers with no or short education reported less frequently breastfeeding problems. Breastfeeding problems occurred frequently in the early postnatal period and often caused breastfeeding to be painful. Health care professionals should prepare mothers to deal with possible breastfeeding problems. New support options should be reviewed in an early postnatal discharge setting. Copyright © 2018 Elsevier B.V. All rights reserved.

Relationships Between Problem-Gambling Severity and Psychopathology as Moderated by Income.

PubMed

Sanacora, Rachel L; Whiting, Seth W; Pilver, Corey E; Hoff, Rani A; Potenza, Marc N

2016-09-01

Background and aims Problem and pathological gambling have been associated with elevated rates of both Axis-I and Axis-II psychiatric disorders. Although both problem gambling and psychiatric disorders have been reported as being more prevalent among lower income vs. middle/higher income groups, how income might moderate the relationship between problem-gambling severity and psychopathology is incompletely understood. To examine the associations between problem-gambling severity and psychopathology in lower income and middle/higher income groups. Methods Data from the first wave of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) (n = 43,093) were analyzed in adjusted logistic regression models to investigate the relationships between problem-gambling severity and psychiatric disorders within and across income groups. Results Greater problem-gambling severity was associated with increased odds of multiple psychiatric disorders for both lower income and middle/higher income groups. Income moderated the association between problem/pathological gambling and alcohol abuse/dependence, with a stronger association seen among middle/higher income respondents than among lower income respondents. Discussion and conclusions The findings that problem-gambling severity is related to psychopathology across income groups suggest a need for public health initiatives across social strata to reduce the impact that problem/pathological gambling may have in relation to psychopathology. Middle/higher income populations, perhaps owing to the availability of more "disposable income," may be at greater risk for co-occurring gambling and alcohol-use psychopathology and may benefit preferentially from interventions targeting both gambling and alcohol use.

Neuropsychological and structural brain lesions in multiple sclerosis: a regional analysis.

PubMed

Swirsky-Sacchetti, T; Mitchell, D R; Seward, J; Gonzales, C; Lublin, F; Knobler, R; Field, H L

1992-07-01

Quantified lesion scores derived from MRI correlate significantly with neuropsychological testing in patients with multiple sclerosis (MS). Variables used to reflect disease severity include total lesion area (TLA), ventricular-brain ratio, and size of the corpus callosum. We used these general measures of cerebral lesion involvement as well as specific ratings of lesion involvement by frontal, temporal, and parieto-occipital regions to quantify the topographic distribution of lesions and consequent effects upon cognitive function. Lesions were heavily distributed in the parieto-occipital regions bilaterally. Neuropsychological tests were highly related to all generalized measures of cerebral involvement, with TLA being the best predictor of neuropsychological deficit. Mean TLA for the cognitively impaired group was 28.30 cm2 versus 7.41 cm2 for the cognitively intact group (p less than 0.0001). Multiple regression analyses revealed that left frontal lobe involvement best predicted impaired abstract problem solving, memory, and word fluency. Left parieto-occipital lesion involvement best predicted deficits in verbal learning and complex visual-integrative skills. Analysis of regional cerebral lesion load may assist in understanding the particular pattern and course of cognitive deficits in MS.

Communicative participation restrictions in multiple sclerosis: associated variables and correlation with social functioning.

PubMed

Yorkston, Kathryn M; Baylor, Carolyn; Amtmann, Dagmar

2014-01-01

Individuals with multiple sclerosis (MS) are at risk for communication problems that may restrict their ability to take participation in important life roles such as maintenance of relationships, work, or household management. The aim of this project is to examine selected demographic and symptom-related variables that may contribute to participation restrictions. This examination is intended to aid clinicians in predicting who might be at risk for such restrictions and what variables may be targeted in interventions. Community-dwelling adults with MS (n=216) completed a survey either online or using paper forms. The survey included the 46-item version of the Communicative Participation Item Bank, demographics (age, sex, living situation, employment status, education, and time since onset of diagnosis of MS), and self-reported symptom-related variables (physical activity, emotional problems, fatigue, pain, speech severity, and cognitive/communication skills). In order to identify predictors of restrictions in communicative participation, these variables were entered into a backwards stepwise multiple linear regression analysis. Five variables (cognitive/communication skills, speech severity, speech usage, physical activity, and education) were statistically significant predictors of communication participation. In order to examine the relationship of communicative participation and social role variables, bivariate Spearman correlations were conducted. Results suggest only a fair to moderate relationship between communicative participation and measures of social roles. Communicative participation is a complex construct associated with a number of self-reported variables. Clinicians should be alert to risk factors for reduced communicative participation including reduced cognitive and speech skills, lower levels of speech usage, limitations in physical activities and higher levels of education. The reader will be able to: (a) describe the factors that may restrict participation in individuals with multiple sclerosis; (b) list measures of social functioning that may be pertinent in adults with multiple sclerosis; (c) discuss factors that can be used to predict communicative participation in multiple sclerosis. Copyright © 2014 Elsevier Inc. All rights reserved.

A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.

Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models.

PubMed

Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L

2017-01-01

Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).

Estimating a child's age from an image using whole body proportions.

PubMed

Lucas, Teghan; Henneberg, Maciej

2017-09-01

The use and distribution of child pornography is an increasing problem. Forensic anthropologists are often asked to estimate a child's age from a photograph. Previous studies have attempted to estimate the age of children from photographs using ratios of the face. Here, we propose to include body measurement ratios into age estimates. A total of 1603 boys and 1833 girls aged 5-16 years were measured over a 10-year period. They are 'Cape Coloured' children from South Africa. Their age was regressed on ratios derived from anthropometric measurements of the head as well as the body. Multiple regression equations including four ratios for each sex (head height to shoulder and hip width, knee width, leg length and trunk length) have a standard error of 1.6-1.7 years. The error is of the same order as variation of differences between biological and chronological ages of the children. Thus, the error cannot be minimised any further as it is a direct reflection of a naturally occurring phenomenon.

Impact of neonatal risk and temperament on behavioral problems in toddlers born preterm.

PubMed

Guilherme Monte Cassiano, Rafaela; Gaspardo, Claudia Maria; Cordaro Bucker Furini, Guilherme; Martinez, Francisco Eulogio; Martins Linhares, Maria Beatriz

2016-12-01

Children born preterm are at risk for later developmental disorders. The present study examined the predictive effects of neonatal, sociodemographic, and temperament characteristics on behavioral outcomes at toddlerhood, in children born preterm. The sample included 100 toddlers born preterm and with very-low-birth-weight, and their mothers. Neonatal characteristics were evaluated using medical records. The mothers were interviewed using the Early Childhood Behavior Questionnaire for temperament assessment, and the Child Behavior Checklist for behavioral assessment. Multiple linear regression analyses were performed. Predictors of 39% of the variability of the total behavioral problems in toddlers born prematurely were: temperament with more Negative Affectivity and less Effortful Control, lower family socioeconomic status, and younger mothers at childbirth. Temperament with more Negative Affectivity and less Effortful Control and lower family socioeconomic status were predictors of 23% of the variability of internalizing behavioral problems. Additionally, 37% of the variability of externalizing behavioral problems was explained by temperament with more Negative Affectivity and less Effortful Control, and younger mothers at childbirth. The neonatal characteristics and stressful events in the neonatal intensive care unit did not predict behavioral problems at toddlerhood. However, temperament was a consistent predictor of behavioral problems in toddlers born preterm. Preventive follow-up programs could assess dispositional traits of temperament to provide early identification of preterm infants at high-risk for behavioral problems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

ERIC Educational Resources Information Center

Li, Spencer D.

2011-01-01

Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

A Simple and Convenient Method of Multiple Linear Regression to Calculate Iodine Molecular Constants

ERIC Educational Resources Information Center

Cooper, Paul D.

2010-01-01

A new procedure using a student-friendly least-squares multiple linear-regression technique utilizing a function within Microsoft Excel is described that enables students to calculate molecular constants from the vibronic spectrum of iodine. This method is advantageous pedagogically as it calculates molecular constants for ground and excited…

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

An Exploratory Study of Face-to-Face and Cyberbullying in Sixth Grade Students

ERIC Educational Resources Information Center

Accordino, Denise B.; Accordino, Michael P.

2011-01-01

In a pilot study, sixth grade students (N = 124) completed a questionnaire assessing students' experience with bullying and cyberbullying, demographic information, quality of parent-child relationship, and ways they have dealt with bullying/cyberbullying in the past. Two multiple regression analyses were conducted. The multiple regression analysis…

The Use of Multiple Regression and Trend Analysis to Understand Enrollment Fluctuations. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Campbell, S. Duke; Greenberg, Barry

The development of a predictive equation capable of explaining a significant percentage of enrollment variability at Florida International University is described. A model utilizing trend analysis and a multiple regression approach to enrollment forecasting was adapted to investigate enrollment dynamics at the university. Four independent…

The Use of Multiple Regression Models to Determine if Conjoint Analysis Should Be Conducted on Aggregate Data.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

1996-01-01

In a conjoint-analysis consumer-preference study, researchers must determine whether the product factor estimates, which measure consumer preferences, should be calculated and interpreted for each respondent or collectively. Multiple regression models can determine whether to aggregate data by examining factor-respondent interaction effects. This…

Double Cross-Validation in Multiple Regression: A Method of Estimating the Stability of Results.

ERIC Educational Resources Information Center

Rowell, R. Kevin

In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is…

Waste management under multiple complexities: Inexact piecewise-linearization-based fuzzy flexible programming

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

Sun Wei; Huang, Guo H., E-mail: huang@iseis.org; Institute for Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan, S4S 0A2

2012-06-15

Highlights: Black-Right-Pointing-Pointer Inexact piecewise-linearization-based fuzzy flexible programming is proposed. Black-Right-Pointing-Pointer It's the first application to waste management under multiple complexities. Black-Right-Pointing-Pointer It tackles nonlinear economies-of-scale effects in interval-parameter constraints. Black-Right-Pointing-Pointer It estimates costs more accurately than the linear-regression-based model. Black-Right-Pointing-Pointer Uncertainties are decreased and more satisfactory interval solutions are obtained. - Abstract: To tackle nonlinear economies-of-scale (EOS) effects in interval-parameter constraints for a representative waste management problem, an inexact piecewise-linearization-based fuzzy flexible programming (IPFP) model is developed. In IPFP, interval parameters for waste amounts and transportation/operation costs can be quantified; aspiration levels for net system costs, as well as tolerancemore » intervals for both capacities of waste treatment facilities and waste generation rates can be reflected; and the nonlinear EOS effects transformed from objective function to constraints can be approximated. An interactive algorithm is proposed for solving the IPFP model, which in nature is an interval-parameter mixed-integer quadratically constrained programming model. To demonstrate the IPFP's advantages, two alternative models are developed to compare their performances. One is a conventional linear-regression-based inexact fuzzy programming model (IPFP2) and the other is an IPFP model with all right-hand-sides of fussy constraints being the corresponding interval numbers (IPFP3). The comparison results between IPFP and IPFP2 indicate that the optimized waste amounts would have the similar patterns in both models. However, when dealing with EOS effects in constraints, the IPFP2 may underestimate the net system costs while the IPFP can estimate the costs more accurately. The comparison results between IPFP and IPFP3 indicate that their solutions would be significantly different. The decreased system uncertainties in IPFP's solutions demonstrate its effectiveness for providing more satisfactory interval solutions than IPFP3. Following its first application to waste management, the IPFP can be potentially applied to other environmental problems under multiple complexities.« less

Analyzing Multilevel Data: An Empirical Comparison of Parameter Estimates of Hierarchical Linear Modeling and Ordinary Least Squares Regression

ERIC Educational Resources Information Center

Rocconi, Louis M.

2011-01-01

Hierarchical linear models (HLM) solve the problems associated with the unit of analysis problem such as misestimated standard errors, heterogeneity of regression and aggregation bias by modeling all levels of interest simultaneously. Hierarchical linear modeling resolves the problem of misestimated standard errors by incorporating a unique random…

Predicting quality of life in multiple sclerosis: accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change.

PubMed

Benedict, Ralph H B; Wahlig, Elizabeth; Bakshi, Rohit; Fishman, Inna; Munschauer, Frederick; Zivadinov, Robert; Weinstock-Guttman, Bianca

2005-04-15

Health-related quality of life (HQOL) is poor in multiple sclerosis (MS) but the clinical precipitants of the problem are not well understood. Previous correlative studies demonstrated relationships between various clinical parameters and diminished HQOL in MS. Unfortunately, these studies failed to account for multiple predictors in the same analysis. We endeavored to determine what clinical parameters account for most variance in predicting HQOL, and employability, while accounting for disease course, physical disability, fatigue, cognition, mood disorder, personality, and behavior disorder. In 120 MS patients, we measured HQOL (MS Quality of Life-54) and vocational status (employed vs. disabled) and then conducted detailed clinical testing. Data were analyzed by linear and logistic regression methods. MS patients reported lower HQOL (p<0.001) and were more likely to be disabled (45% of patients vs. 0 controls). Physical HQOL was predicted by fatigue, depression, and physical disability. Mental HQOL was associated with only depression and fatigue. In contrast, vocational status was predicted by three cognitive tests, conscientiousness, and disease duration (p<0.05). Thus, for the first time, we predicted HQOL in MS while accounting for measures from these many clinical domains. We conclude that self-report HQOL indices are most strongly predicted by measures of depression, whereas vocational status is predicted primarily by objective measures of cognitive function. The findings highlight core clinical problems that merit early identification and further research regarding the development of effective treatment.

Treatment preferences and help-seeking behaviors for sleep problems among psychiatric outpatients.

PubMed

Chang, Sherilyn; Seow, Esmond; Koh, Sok Hian Doris; Verma, Swapna K; Mok, Yee Ming; Abdin, Edimansyah; Chong, Siow Ann; Subramaniam, Mythily

To understand treatment preferences and help-seeking behaviors among psychiatric patients for their sleep problems, and to examine determinants of problem recognition and help-seeking among patients with sleep difficulties. A cross-sectional survey was conducted among psychiatric outpatients in Singapore (n=400). Participants completed questionnaires that assessed their sleep quality, daytime fatigue, help-seeking behavior, treatment preferences for sleep problems, and sociodemographic information. Multiple logistic regressions were used to identify correlates of patients who recognized their sleep difficulties and of those who had sought help. Mental health professionals were the most preferred choice (60.8%) for consultation on sleep problems. Among patients with poor sleep quality (n=275), 28.4% denied having any problems and 38.9% had not sought help. Patients with chronic physical comorbidity were less likely to recognize their sleep problems (OR=0.432, p-value=0.009), while those with psychiatric comorbidity were twice as likely to perceive the problems (OR=2.094, p-value=0.021) and to seek help (OR=1.957, p-value=0.022). Daytime fatigue was associated with higher odds of problem recognition (OR=1.106, p-value=0.001) and help-seeking (OR=1.064, p-value=0.016). A considerable number of patients did not perceive their poor sleep as an issue and had not sought help for it. General sleep hygiene education is needed for psychiatric patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Cost-aware request routing in multi-geography cloud data centres using software-defined networking

NASA Astrophysics Data System (ADS)

Yuan, Haitao; Bi, Jing; Li, Bo Hu; Tan, Wei

2017-03-01

Current geographically distributed cloud data centres (CDCs) require gigantic energy and bandwidth costs to provide multiple cloud applications to users around the world. Previous studies only focus on energy cost minimisation in distributed CDCs. However, a CDC provider needs to deliver gigantic data between users and distributed CDCs through internet service providers (ISPs). Geographical diversity of bandwidth and energy costs brings a highly challenging problem of how to minimise the total cost of a CDC provider. With the recently emerging software-defined networking, we study the total cost minimisation problem for a CDC provider by exploiting geographical diversity of energy and bandwidth costs. We formulate the total cost minimisation problem as a mixed integer non-linear programming (MINLP). Then, we develop heuristic algorithms to solve the problem and to provide a cost-aware request routing for joint optimisation of the selection of ISPs and the number of servers in distributed CDCs. Besides, to tackle the dynamic workload in distributed CDCs, this article proposes a regression-based workload prediction method to obtain future incoming workload. Finally, this work evaluates the cost-aware request routing by trace-driven simulation and compares it with the existing approaches to demonstrate its effectiveness.

Involvement in the Juvenile Justice System for African American Adolescents: Examining Associations with Behavioral Health Problems

PubMed Central

Voisin, Dexter R.; Kim, Dongha; Takahashi, Lois; Morotta, Phillip; Bocanegra, Kathryn

2017-01-01

While researchers have found that African American youth experience higher levels of juvenile justice involvement at every system level (arrest, sentencing, and incarceration) relative to their other ethnic counterparts, few studies have explored how juvenile justice involvement and number of contacts might be correlated with this broad range of problems. A convenience sample of 638 African American adolescents living in predominantly low-income, urban communities participated in a survey related to juvenile justice involvement. Major findings using logistic regression models indicated that adolescents who reported juvenile justice system involvement versus no involvement were 2.3 times as likely to report mental health problems, substance abuse, and delinquent or youth offending behaviors. Additional findings documented that the higher the number of juvenile justice system contacts, the higher the rates of delinquent behaviors, alcohol and marijuana use, sex while high on drugs, and commercial sex. These findings suggest that identifying and targeting youth who have multiple juvenile justice system contacts, especially those in low-resourced communities for early intervention services, may be beneficial. Future research should examine whether peer network norms might mediate the relationships between juvenile justice involvement and youth problem behaviors. PMID:28966415

Involvement in the Juvenile Justice System for African American Adolescents: Examining Associations with Behavioral Health Problems.

PubMed

Voisin, Dexter R; Kim, Dongha; Takahashi, Lois; Morotta, Phillip; Bocanegra, Kathryn

2017-01-01

While researchers have found that African American youth experience higher levels of juvenile justice involvement at every system level (arrest, sentencing, and incarceration) relative to their other ethnic counterparts, few studies have explored how juvenile justice involvement and number of contacts might be correlated with this broad range of problems. A convenience sample of 638 African American adolescents living in predominantly low-income, urban communities participated in a survey related to juvenile justice involvement. Major findings using logistic regression models indicated that adolescents who reported juvenile justice system involvement versus no involvement were 2.3 times as likely to report mental health problems, substance abuse, and delinquent or youth offending behaviors. Additional findings documented that the higher the number of juvenile justice system contacts, the higher the rates of delinquent behaviors, alcohol and marijuana use, sex while high on drugs, and commercial sex. These findings suggest that identifying and targeting youth who have multiple juvenile justice system contacts, especially those in low-resourced communities for early intervention services, may be beneficial. Future research should examine whether peer network norms might mediate the relationships between juvenile justice involvement and youth problem behaviors.

Does coping help? A reexamination of the relation between coping and mental health.

PubMed

Aldwin, C M; Revenson, T A

1987-08-01

In a longitudinal community survey of 291 adults, we explored the relation between coping strategies and psychological symptoms. Respondents completed the revised Ways of Coping Scale (Folkman & Lazarus, 1985) for a self-named stressful episode. Factor analysis produced eight coping factors: three problem focused, four emotion focused, and one (support mobilization) that contained elements of both. Multiple regression analyses indicated bidirectionality in the relation between coping and psychological symptoms. Those in poorer mental health and under greater stress used less adaptive coping strategies, such as escapism, but coping efforts still affected mental health independent of prior symptom levels and degree of stress. We compared main versus interactive effects models of stress buffering. Main effects were confined primarily to the emotion-focused coping scales and showed little or negative impacts of coping on mental health; interactive effects, though small, were found with the problem-focused scales. The direction of the relation between problem-focused scales and symptoms may depend in part on perceived efficacy, or how the respondent thought he or she handled the problem. Implications for the measurement of adaptive coping mechanisms and their contextual appropriateness are discussed.

Weighted SGD for ℓ p Regression with Randomized Preconditioning.

PubMed

Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W

2016-01-01

In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger performance guarantees but are applicable to a narrower class of problems. We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems-e.g., ℓ 2 and ℓ 1 regression problems. We propose a hybrid algorithm named pwSGD that uses RLA techniques for preconditioning and constructing an importance sampling distribution, and then performs an SGD-like iterative process with weighted sampling on the preconditioned system.By rewriting a deterministic ℓ p regression problem as a stochastic optimization problem, we connect pwSGD to several existing ℓ p solvers including RLA methods with algorithmic leveraging (RLA for short).We prove that pwSGD inherits faster convergence rates that only depend on the lower dimension of the linear system, while maintaining low computation complexity. Such SGD convergence rates are superior to other related SGD algorithm such as the weighted randomized Kaczmarz algorithm.Particularly, when solving ℓ 1 regression with size n by d , pwSGD returns an approximate solution with ε relative error in the objective value in (log n ·nnz( A )+poly( d )/ ε 2 ) time. This complexity is uniformly better than that of RLA methods in terms of both ε and d when the problem is unconstrained. In the presence of constraints, pwSGD only has to solve a sequence of much simpler and smaller optimization problem over the same constraints. In general this is more efficient than solving the constrained subproblem required in RLA.For ℓ 2 regression, pwSGD returns an approximate solution with ε relative error in the objective value and the solution vector measured in prediction norm in (log n ·nnz( A )+poly( d ) log(1/ ε )/ ε ) time. We show that for unconstrained ℓ 2 regression, this complexity is comparable to that of RLA and is asymptotically better over several state-of-the-art solvers in the regime where the desired accuracy ε , high dimension n and low dimension d satisfy d ≥ 1/ ε and n ≥ d 2 / ε . We also provide lower bounds on the coreset complexity for more general regression problems, indicating that still new ideas will be needed to extend similar RLA preconditioning ideas to weighted SGD algorithms for more general regression problems. Finally, the effectiveness of such algorithms is illustrated numerically on both synthetic and real datasets, and the results are consistent with our theoretical findings and demonstrate that pwSGD converges to a medium-precision solution, e.g., ε = 10 -3 , more quickly.

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

PubMed

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

Managing Multiple Health Problems: Living with Multiple Health Problems

MedlinePlus

... treatments affect people with multiple health problems. Guiding Principles on Caring for Older Adults with Multiple Health ... interactions and other side effects. Each of the principles above is intended to help improve the health ...

Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

PubMed

Fisz, Jacek J

2006-12-07

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi-linear combinations of nonlinear functions, is indicated. The VP algorithm does not distinguish the weakly nonlinear parameters from the nonlinear ones and it does not apply to the model functions which are multi-linear combinations of nonlinear functions.

Ridge: a computer program for calculating ridge regression estimates

Treesearch

Donald E. Hilt; Donald W. Seegrist

1977-01-01

Least-squares coefficients for multiple-regression models may be unstable when the independent variables are highly correlated. Ridge regression is a biased estimation procedure that produces stable estimates of the coefficients. Ridge regression is discussed, and a computer program for calculating the ridge coefficients is presented.

Decision Support Model for Optimal Management of Coastal Gate

NASA Astrophysics Data System (ADS)

Ditthakit, Pakorn; Chittaladakorn, Suwatana

2010-05-01

The coastal areas are intensely settled by human beings owing to their fertility of natural resources. However, at present those areas are facing with water scarcity problems: inadequate water and poor water quality as a result of saltwater intrusion and inappropriate land-use management. To solve these problems, several measures have been exploited. The coastal gate construction is a structural measure widely performed in several countries. This manner requires the plan for suitably operating coastal gates. Coastal gate operation is a complicated task and usually concerns with the management of multiple purposes, which are generally conflicted one another. This paper delineates the methodology and used theories for developing decision support modeling for coastal gate operation scheduling. The developed model was based on coupling simulation and optimization model. The weighting optimization technique based on Differential Evolution (DE) was selected herein for solving multiple objective problems. The hydrodynamic and water quality models were repeatedly invoked during searching the optimal gate operations. In addition, two forecasting models:- Auto Regressive model (AR model) and Harmonic Analysis model (HA model) were applied for forecasting water levels and tide levels, respectively. To demonstrate the applicability of the developed model, it was applied to plan the operations for hypothetical system of Pak Phanang coastal gate system, located in Nakhon Si Thammarat province, southern part of Thailand. It was found that the proposed model could satisfyingly assist decision-makers for operating coastal gates under various environmental, ecological and hydraulic conditions.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES1

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a “Regression with Summary Statistics” (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss. PMID:29399241

Do different types of school mathematics development depend on different constellations of numerical versus general cognitive abilities?

PubMed

Fuchs, Lynn S; Geary, David C; Compton, Donald L; Fuchs, Douglas; Hamlett, Carol L; Seethaler, Pamela M; Bryant, Joan D; Schatschneider, Christopher

2010-11-01

The purpose of this study was to examine the interplay between basic numerical cognition and domain-general abilities (such as working memory) in explaining school mathematics learning. First graders (N = 280; mean age = 5.77 years) were assessed on 2 types of basic numerical cognition, 8 domain-general abilities, procedural calculations, and word problems in fall and then reassessed on procedural calculations and word problems in spring. Development was indexed by latent change scores, and the interplay between numerical and domain-general abilities was analyzed by multiple regression. Results suggest that the development of different types of formal school mathematics depends on different constellations of numerical versus general cognitive abilities. When controlling for 8 domain-general abilities, both aspects of basic numerical cognition were uniquely predictive of procedural calculations and word problems development. Yet, for procedural calculations development, the additional amount of variance explained by the set of domain-general abilities was not significant, and only counting span was uniquely predictive. By contrast, for word problems development, the set of domain-general abilities did provide additional explanatory value, accounting for about the same amount of variance as the basic numerical cognition variables. Language, attentive behavior, nonverbal problem solving, and listening span were uniquely predictive.

Solitary cannabis use in adolescence as a correlate and predictor of cannabis problems.

PubMed

Creswell, Kasey G; Chung, Tammy; Clark, Duncan B; Martin, Christopher S

2015-11-01

Most adolescent cannabis use occurs in social settings among peers. Solitary cannabis use during adolescence may represent an informative divergence from normative behavior with important implications for understanding risk for cannabis problems. This longitudinal study examined associations of adolescent solitary cannabis use with levels of cannabis use and problems in adolescence and in young adulthood. Cannabis using-adolescents aged 12-18 were recruited from clinical programs (n=354; 43.8% female; 83.3% Caucasian) and community sources (n=93; 52.7% female; 80.6% Caucasian). Participants reported on cannabis use patterns and diagnostic symptoms at baseline and multiple follow-ups into young adulthood. Compared to social-only users, adolescent solitary cannabis users were more likely to be male and reported more frequent cannabis use and more DSM-IV cannabis use disorder (CUD) symptoms. Regression analyses showed that solitary cannabis use in adolescence predicted CUD symptom counts in young adulthood (age 25) after controlling for demographic variables and the frequency of adolescent cannabis use. However, solitary adolescent cannabis use was no longer predictive of age 25 CUD symptoms after additionally controlling for adolescent CUD symptoms. Solitary cannabis use is associated with greater cannabis use and problems during adolescence, but evidence is mixed that it predicts young adult cannabis problems. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Ecological Factors Influencing Emotional/Behavioral Problems and Self-Concept in Adolescents from Low-Income Families in South Korea.

PubMed

Baek, Suyon; Yoo, Haewon

2017-09-01

In this study, we examined emotional/behavioral problems and self-concept in adolescents from low-income families in Korea; additionally, we identified ecological factors associated with these traits. This descriptive study employed an ecological model to analyze data from the Korean Children and Youth Panel Survey. A nationwide stratified multistage cluster sampling methodology was used. Overall, 2534 first-year middle school students were included in the survey, and the survey was conducted from 2010 to 2016. Hierarchical multiple regression models were generated. The mean score of emotional/behavioral problem has been changed from 2.20 (2011), 2.15 (2013), to 2.11 (2015) out of 4, and the mean score of self-concept has been changed from 2.73 (2012), 2.73 (2014), to 2.77 (2015) out of 4. Factors that influenced emotional/behavioral problems and self-concept among adolescents were health perception and academic achievement (only associated with self-concept) at the intrapersonal level and parenting style, peer attachment (only associated with self-concept), and relationships with teachers at the interpersonal level. These results may be used to inform the development of interventions designed to decrease emotional/behavioral problems and improve positive self-concept in adolescents from low-income families.

Adolescent gambling and impulsivity: Does employment during high school moderate the association?

PubMed

Canale, Natale; Scacchi, Luca; Griffiths, Mark D

2016-09-01

The aim of the present study was to examine the potential moderating relationships between adolescent gambling and impulsivity traits (negative urgency, positive urgency, lack of premeditation, lack of perseverance and sensation seeking) with employment status. High-school students (N=400; 69% male; mean age=18.35years; SD=1.16; past year gamblers) were surveyed to provide data on impulsivity and employment. Multiple linear regression analysis was applied to examine associations with gambling and related problems. Positive urgency was associated with stronger scores of both gambling frequency and problem gambling. Students in employment had substantially higher frequency of gambling and greater problem gambling. Moreover, the combination of having a job and low perseverance was associated with a particularly high frequency on gambling. These findings further support the importance of positive urgency and employment status in adolescent gambling. The study highlights unique moderating relationship between gambling and lack of perseverance with employment status. Youth with a low perseverance and having a job may have particular need for interventions to reduce gambling. Copyright © 2016 Elsevier Ltd. All rights reserved.

Understanding planning ability measured by the Tower of London: an evaluation of its internal structure by latent variable modeling.

PubMed

Koppenol-Gonzalez, Gabriela V; Bouwmeester, Samantha; Boonstra, A Marije

2010-12-01

The Tower of London (TOL) is a widely used instrument for assessing planning ability. Inhibition and (spatial) working memory are assumed to contribute to performance on the TOL, but findings about the relationship between these cognitive processes are often inconsistent. Moreover, the influence of specific properties of TOL problems on cognitive processes and difficulty level is often not taken into account. Furthermore, it may be expected that several planning strategies can be distinguished that cannot be extracted from the total score. In this study, a factor analysis and a latent class regression analysis were performed to address these issues. The results showed that 4 strategy groups that differed with respect to preplanning time could be distinguished. The effect of problem properties also differed for the 4 groups. Additional analyses showed that the groups differed on average planning performance but that there were no significant differences between inhibition and spatial working memory performance. Finally, it seemed that multiple factors influence performance on the TOL, the most important ones being the score measurements, the problem properties, and strategy use.

Statistical experiments using the multiple regression research for prediction of proper hardness in areas of phosphorus cast-iron brake shoes manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.

2018-01-01

Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.

A Comparative Study of Classification and Regression Algorithms for Modelling Students' Academic Performance

ERIC Educational Resources Information Center

Strecht, Pedro; Cruz, Luís; Soares, Carlos; Mendes-Moreira, João; Abreu, Rui

2015-01-01

Predicting the success or failure of a student in a course or program is a problem that has recently been addressed using data mining techniques. In this paper we evaluate some of the most popular classification and regression algorithms on this problem. We address two problems: prediction of approval/failure and prediction of grade. The former is…

Association between overuse of mobile phones on quality of sleep and general health among occupational health and safety students.

PubMed

Eyvazlou, Meysam; Zarei, Esmaeil; Rahimi, Azin; Abazari, Malek

2016-01-01

Concerns about health problems due to the increasing use of mobile phones are growing. Excessive use of mobile phones can affect the quality of sleep as one of the important issues in the health literature and general health of people. Therefore, this study investigated the relationship between the excessive use of mobile phones and general health and quality of sleep on 450 Occupational Health and Safety (OH&S) students in five universities of medical sciences in the North East of Iran in 2014. To achieve this objective, special questionnaires that included Cell Phone Overuse Scale, Pittsburgh's Sleep Quality Index (PSQI) and General Health Questionnaire (GHQ) were used, respectively. In addition to descriptive statistical methods, independent t-test, Pearson correlation, analysis of variance (ANOVA) and multiple regression tests were performed. The results revealed that half of the students had a poor level of sleep quality and most of them were considered unhealthy. The Pearson correlation co-efficient indicated a significant association between the excessive use of mobile phones and the total score of general health and the quality of sleep. In addition, the results of the multiple regression showed that the excessive use of mobile phones has a significant relationship between each of the four subscales of general health and the quality of sleep. Furthermore, the results of the multivariate regression indicated that the quality of sleep has a simultaneous effect on each of the four scales of the general health. Overall, a simultaneous study of the effects of the mobile phones on the quality of sleep and the general health could be considered as a trigger to employ some intervention programs to improve their general health status, quality of sleep and consequently educational performance.

Depression, anxiety and general psychopathology in breast cancer patients: a cross-sectional control study.

PubMed

Fafouti, M; Paparrigopoulos, T; Zervas, Y; Rabavilas, A; Malamos, N; Liappas, I; Tzavara, C

2010-01-01

A significant proportion of breast cancer patients experience psychiatric morbidity. The present study compared the psychopathological profile (depression, anxiety and general psychopathology) of Greek women with breast cancer with a group of healthy controls. Patients (n=109) were recruited from a specialized oncology breast cancer department and healthy controls (n=71) from a breast outpatient clinic. General psychopathology was assessed by the SCL-90-R. The Montgomery-Asberg Depression Rating Scale (MADRS) and the Spielberger State-Trait Anxiety Inventory (STAI) were used for assessing depression and anxiety. Demographics and clinical characteristics were also recorded. Data were modeled using multiple regression analysis. The mean age was 54.7±18.1 years for the control group and 51.2±9.5 years for the patient group (p=0.288). Mean scores on SCL-90-R, MADRS and STAI were significantly higher in the cancer group compared to controls (p<0.05). Multiple regression analysis revealed that breast cancer was independently and positively associated with all psychological measures (p<0.05). Regression coefficients ranged from 0.19 (SCL-90-R, psychotism) to 0.33 (MADRS). Lower anger/aggressiveness and anxiety were found in highly educated women; divorced/widowed women scored higher on obsessionality and MADRS compared to married women. Psychiatric treatment was associated with higher scores on somatization, depression, phobic anxiety and general psychopathology. Anxiety, depression, and overall psychopathology are more frequent in breast cancer patients compared to controls. Disease makes a larger independent contribution to all psychopathological measures than any other investigated variable. Therefore, breast cancer patients should be closely followed up in order to identify and timely treat any mental health problems that may arise.

Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.

Evolution of Space Dependent Growth in the Teleost Astyanax mexicanus

PubMed Central

Gallo, Natalya D.; Jeffery, William R.

2012-01-01

The relationship between growth rate and environmental space is an unresolved issue in teleosts. While it is known from aquaculture studies that stocking density has a negative relationship to growth, the underlying mechanisms have not been elucidated, primarily because the growth rate of populations rather than individual fish were the subject of all previous studies. Here we investigate this problem in the teleost Astyanax mexicanus, which consists of a sighted surface-dwelling form (surface fish) and several blind cave-dwelling (cavefish) forms. Surface fish and cavefish are distinguished by living in spatially contrasting environments and therefore are excellent models to study the effects of environmental size on growth. Multiple controlled growth experiments with individual fish raised in confined or unconfined spaces showed that environmental size has a major impact on growth rate in surface fish, a trait we have termed space dependent growth (SDG). In contrast, SDG has regressed to different degrees in the Pachón and Tinaja populations of cavefish. Mating experiments between surface and Pachón cavefish show that SDG is inherited as a dominant trait and is controlled by multiple genetic factors. Despite its regression in blind cavefish, SDG is not affected when sighted surface fish are raised in darkness, indicating that vision is not required to perceive and react to environmental space. Analysis of plasma cortisol levels showed that an elevation above basal levels occurred soon after surface fish were exposed to confined space. This initial cortisol peak was absent in Pachón cavefish, suggesting that the effects of confined space on growth may be mediated partly through a stress response. We conclude that Astyanax reacts to confined spaces by exhibiting SDG, which has a genetic component and shows evolutionary regression during adaptation of cavefish to confined environments. PMID:22870223

Are there specific health-related factors that can accentuate the risk of suicide among men with prostate cancer?

PubMed

Lehuluante, Abraraw; Fransson, Per

2014-06-01

The aim of this study was to explore if there were some specific factors pertinent to health-related quality of life (HRQoL) that could affect self-experienced suicide ideation in men with prostate cancer (PCa). Questionnaires containing 45 items were distributed to members of the Swedish Prostate Cancer Federation in May 2012. Out of 6,400 distributed questionnaires, 3,165 members (50 %) with PCa completed the questionnaires. Those members expressed their experienced HRQoL and experienced suicide ideation using VAS-like scales as well as multiple-choice questions. Both descriptive and analytical statistical methods were employed. A regression model was used to explore the relationship between experienced health-related quality of life and experienced suicide ideation. Generally, the respondents rated their self-experienced health-related quality of life as good. About 40 % of the participants had experienced problem with incontinence, and 23 % had obstructions during miction. About 7 % of the respondents experienced suicidal ideation, at least sometime. The regression model showed statistically significant relationships between suicide ideation, on the one hand, and lower self-rated health-related quality of life (P < 0.001), physical pain (P = 0.04), pain during miction (P = 0.03), and low-rated mental / physical energy (P = 0.03), on the other. It is quite necessary to know which specific disease and treatment-related problems can trigger suicide ideations in men with prostate cancer and to try to direct treatment, care, and psychosocial resources to alleviate these problems in time.

Latent classes of emotional and behavioural problems in epidemiological and referred samples and their relations to DSM-IV diagnoses.

PubMed

Bianchi, Valentina; Brambilla, Paolo; Garzitto, Marco; Colombo, Paola; Fornasari, Livia; Bellina, Monica; Bonivento, Carolina; Tesei, Alessandra; Piccin, Sara; Conte, Stefania; Perna, Giampaolo; Frigerio, Alessandra; Castiglioni, Isabella; Fabbro, Franco; Molteni, Massimo; Nobile, Maria

2017-05-01

Researchers' interest have recently moved toward the identification of recurrent psychopathological profiles characterized by concurrent elevations on different behavioural and emotional traits. This new strategy turned to be useful in terms of diagnosis and outcome prediction. We used a person-centred statistical approach to examine whether different groups could be identified in a referred sample and in a general-population sample of children and adolescents, and we investigated their relation to DSM-IV diagnoses. A latent class analysis (LCA) was performed on the Child Behaviour Checklist (CBCL) syndrome scales of the referred sample (N = 1225), of the general-population sample (N = 3418), and of the total sample. Models estimating 1-class through 5-class solutions were compared and agreement in the classification of subjects was evaluated. Chi square analyses, a logistic regression, and a multinomial logistic regression analysis were used to investigate the relations between classes and diagnoses. In the two samples and in the total sample, the best-fitting models were 4-class solutions. The identified classes were Internalizing Problems (15.68%), Severe Dysregulated (7.82%), Attention/Hyperactivity (10.19%), and Low Problems (66.32%). Subsequent analyses indicated a significant relationship between diagnoses and classes as well as a main association between the severe dysregulated class and comorbidity. Our data suggested the presence of four different psychopathological profiles related to different outcomes in terms of psychopathological diagnoses. In particular, our results underline the presence of a profile characterized by severe emotional and behavioural dysregulation that is mostly associated with the presence of multiple diagnosis.

Relationship between stress coping and sleep disorders among the general Japanese population: a nationwide representative survey.

PubMed

Otsuka, Yuichiro; Kaneita, Yoshitaka; Itani, Osamu; Nakagome, Sachi; Jike, Maki; Ohida, Takashi

2017-09-01

To clarify the prevalence of stress, and examine the relationship between sleep disorders and stress coping strategies among highly stressed individuals in the general Japanese population. A cross-sectional nationwide survey was undertaken in November 2007. Men and women were randomly selected from 300 districts throughout Japan. Data from 7671 (3532 men (average age 53.5 ± 17.0 years) and 4139 women (average age 53.9 ± 17.7 years)) were analyzed. Participants completed a self-reported questionnaire on stress, sleep disorders, and stress coping strategies in the previous month. Highly stressed individuals comprised 16.6% (95% confidence interval 15.8-17.5%) of the total sample, and most were aged 20-49 years. In multiple logistic regression, symptoms of insomnia (ie, difficulty initiating sleep, difficulty maintaining sleep, and early morning awakening), excessive daytime sleepiness, nightmares, daytime malfunction, and lack of rest due to sleep deprivation were more prone to occur in highly stressed individuals. In addition, logistic regression analysis controlling for other adjustment factors revealed that stress coping strategies such as 'giving up on problem-solving', 'enduring problems patiently', 'smoking' and 'drinking alcohol' were positively associated with the above-mentioned sleep disorders. On the other hand, stress coping strategies such as 'exercising', 'enjoying hobbies', and 'sharing worries' were inversely associated with the above-mentioned sleep disorders. Distraction-based stress coping (eg, hobbies, exercise, and optimistic thinking) was found to be preferable to problem-based stress coping in a highly stressed Japanese general population. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Robinson-Cimpian, Joseph P.

2014-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

Development of Multiple Regression Equations To Predict Fourth Graders' Achievement in Reading and Selected Content Areas.

ERIC Educational Resources Information Center

Hafner, Lawrence E.

A study developed a multiple regression prediction equation for each of six selected achievement variables in a popular standardized test of achievement. Subjects, 42 fourth-grade pupils randomly selected across several classes in a large elementary school in a north Florida city, were administered several standardized tests to determine predictor…

Physical and Cognitive-Affective Factors Associated with Fatigue in Individuals with Fibromyalgia: A Multiple Regression Analysis

ERIC Educational Resources Information Center

Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong

2015-01-01

Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

ERIC Educational Resources Information Center

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Some Applied Research Concerns Using Multiple Linear Regression Analysis.

ERIC Educational Resources Information Center

Newman, Isadore; Fraas, John W.

The intention of this paper is to provide an overall reference on how a researcher can apply multiple linear regression in order to utilize the advantages that it has to offer. The advantages and some concerns expressed about the technique are examined. A number of practical ways by which researchers can deal with such concerns as…

A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity

ERIC Educational Resources Information Center

Martin, David

2008-01-01

This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…

Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

ERIC Educational Resources Information Center

Anderson, Joan L.

2006-01-01

Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis

ERIC Educational Resources Information Center

Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.

2006-01-01

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…

Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA

PubMed Central

Lin, Chen-Yen; Bondell, Howard; Zhang, Hao Helen; Zou, Hui

2014-01-01

Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online. PMID:24554792

CALIBRATING NON-CONVEX PENALIZED REGRESSION IN ULTRA-HIGH DIMENSION.

PubMed

Wang, Lan; Kim, Yongdai; Li, Runze

2013-10-01

We investigate high-dimensional non-convex penalized regression, where the number of covariates may grow at an exponential rate. Although recent asymptotic theory established that there exists a local minimum possessing the oracle property under general conditions, it is still largely an open problem how to identify the oracle estimator among potentially multiple local minima. There are two main obstacles: (1) due to the presence of multiple minima, the solution path is nonunique and is not guaranteed to contain the oracle estimator; (2) even if a solution path is known to contain the oracle estimator, the optimal tuning parameter depends on many unknown factors and is hard to estimate. To address these two challenging issues, we first prove that an easy-to-calculate calibrated CCCP algorithm produces a consistent solution path which contains the oracle estimator with probability approaching one. Furthermore, we propose a high-dimensional BIC criterion and show that it can be applied to the solution path to select the optimal tuning parameter which asymptotically identifies the oracle estimator. The theory for a general class of non-convex penalties in the ultra-high dimensional setup is established when the random errors follow the sub-Gaussian distribution. Monte Carlo studies confirm that the calibrated CCCP algorithm combined with the proposed high-dimensional BIC has desirable performance in identifying the underlying sparsity pattern for high-dimensional data analysis.

Scan-stratified case-control sampling for modeling blood-brain barrier integrity in multiple sclerosis.

PubMed

Pomann, Gina-Maria; Sweeney, Elizabeth M; Reich, Daniel S; Staicu, Ana-Maria; Shinohara, Russell T

2015-09-10

Multiple sclerosis (MS) is an immune-mediated neurological disease that causes morbidity and disability. In patients with MS, the accumulation of lesions in the white matter of the brain is associated with disease progression and worse clinical outcomes. Breakdown of the blood-brain barrier in newer lesions is indicative of more active disease-related processes and is a primary outcome considered in clinical trials of treatments for MS. Such abnormalities in active MS lesions are evaluated in vivo using contrast-enhanced structural MRI, during which patients receive an intravenous infusion of a costly magnetic contrast agent. In some instances, the contrast agents can have toxic effects. Recently, local image regression techniques have been shown to have modest performance for assessing the integrity of the blood-brain barrier based on imaging without contrast agents. These models have centered on the problem of cross-sectional classification in which patients are imaged at a single study visit and pre-contrast images are used to predict post-contrast imaging. In this paper, we extend these methods to incorporate historical imaging information, and we find the proposed model to exhibit improved performance. We further develop scan-stratified case-control sampling techniques that reduce the computational burden of local image regression models, while respecting the low proportion of the brain that exhibits abnormal vascular permeability. Copyright © 2015 John Wiley & Sons, Ltd.

CALIBRATING NON-CONVEX PENALIZED REGRESSION IN ULTRA-HIGH DIMENSION

PubMed Central

Wang, Lan; Kim, Yongdai; Li, Runze

2014-01-01

We investigate high-dimensional non-convex penalized regression, where the number of covariates may grow at an exponential rate. Although recent asymptotic theory established that there exists a local minimum possessing the oracle property under general conditions, it is still largely an open problem how to identify the oracle estimator among potentially multiple local minima. There are two main obstacles: (1) due to the presence of multiple minima, the solution path is nonunique and is not guaranteed to contain the oracle estimator; (2) even if a solution path is known to contain the oracle estimator, the optimal tuning parameter depends on many unknown factors and is hard to estimate. To address these two challenging issues, we first prove that an easy-to-calculate calibrated CCCP algorithm produces a consistent solution path which contains the oracle estimator with probability approaching one. Furthermore, we propose a high-dimensional BIC criterion and show that it can be applied to the solution path to select the optimal tuning parameter which asymptotically identifies the oracle estimator. The theory for a general class of non-convex penalties in the ultra-high dimensional setup is established when the random errors follow the sub-Gaussian distribution. Monte Carlo studies confirm that the calibrated CCCP algorithm combined with the proposed high-dimensional BIC has desirable performance in identifying the underlying sparsity pattern for high-dimensional data analysis. PMID:24948843

Artificial Neural Networks: A Novel Approach to Analysing the Nutritional Ecology of a Blowfly Species, Chrysomya megacephala

PubMed Central

Bianconi, André; Zuben, Cláudio J. Von; Serapião, Adriane B. de S.; Govone, José S.

2010-01-01

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies. PMID:20569135

Regression Models for the Analysis of Longitudinal Gaussian Data from Multiple Sources

PubMed Central

O’Brien, Liam M.; Fitzmaurice, Garrett M.

2006-01-01

We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry. PMID:15726666

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Computer/gaming station use in youth: Correlations among use, addiction and functional impairment

PubMed Central

Baer, Susan; Saran, Kelly; Green, David A

2012-01-01

OBJECTIVE: Computer/gaming station use is ubiquitous in the lives of youth today. Overuse is a concern, but it remains unclear whether problems arise from addictive patterns of use or simply excessive time spent on use. The goal of the present study was to evaluate computer/gaming station use in youth and to examine the relationship between amounts of use, addictive features of use and functional impairment. METHOD: A total of 110 subjects (11 to 17 years of age) from local schools participated. Time spent on television, video gaming and non-gaming recreational computer activities was measured. Addictive features of computer/gaming station use were ascertained, along with emotional/behavioural functioning. Multiple linear regressions were used to understand how youth functioning varied with time of use and addictive features of use. RESULTS: Mean (± SD) total screen time was 4.5±2.4 h/day. Addictive features of use were consistently correlated with functional impairment across multiple measures and informants, whereas time of use, after controlling for addiction, was not. CONCLUSIONS: Youth are spending many hours each day in front of screens. In the absence of addictive features of computer/gaming station use, time spent is not correlated with problems; however, youth with addictive features of use show evidence of poor emotional/ behavioural functioning. PMID:24082802

Individual strategy ratings improve the control for task difficulty effects in arithmetic problem solving paradigms.

PubMed

Tschentscher, Nadja; Hauk, Olaf

2015-01-01

Mental arithmetic is a powerful paradigm to study problem solving using neuroimaging methods. However, the evaluation of task complexity varies significantly across neuroimaging studies. Most studies have parameterized task complexity by objective features such as the number size. Only a few studies used subjective rating procedures. In fMRI, we provided evidence that strategy self-reports control better for task complexity across arithmetic conditions than objective features (Tschentscher and Hauk, 2014). Here, we analyzed the relative predictive value of self-reported strategies and objective features for performance in addition and multiplication tasks, by using a paradigm designed for neuroimaging research. We found a superiority of strategy ratings as predictor of performance above objective features. In a Principal Component Analysis on reaction times, the first component explained over 90 percent of variance and factor loadings reflected percentages of self-reported strategies well. In multiple regression analyses on reaction times, self-reported strategies performed equally well or better than objective features, depending on the operation type. A Receiver Operating Characteristic (ROC) analysis confirmed this result. Reaction times classified task complexity better when defined by individual ratings. This suggests that participants' strategy ratings are reliable predictors of arithmetic complexity and should be taken into account in neuroimaging research.

Individual strategy ratings improve the control for task difficulty effects in arithmetic problem solving paradigms

PubMed Central

Tschentscher, Nadja; Hauk, Olaf

2015-01-01

Mental arithmetic is a powerful paradigm to study problem solving using neuroimaging methods. However, the evaluation of task complexity varies significantly across neuroimaging studies. Most studies have parameterized task complexity by objective features such as the number size. Only a few studies used subjective rating procedures. In fMRI, we provided evidence that strategy self-reports control better for task complexity across arithmetic conditions than objective features (Tschentscher and Hauk, 2014). Here, we analyzed the relative predictive value of self-reported strategies and objective features for performance in addition and multiplication tasks, by using a paradigm designed for neuroimaging research. We found a superiority of strategy ratings as predictor of performance above objective features. In a Principal Component Analysis on reaction times, the first component explained over 90 percent of variance and factor loadings reflected percentages of self-reported strategies well. In multiple regression analyses on reaction times, self-reported strategies performed equally well or better than objective features, depending on the operation type. A Receiver Operating Characteristic (ROC) analysis confirmed this result. Reaction times classified task complexity better when defined by individual ratings. This suggests that participants’ strategy ratings are reliable predictors of arithmetic complexity and should be taken into account in neuroimaging research. PMID:26321997

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

Illustrates some of the basic concepts and procedures for using regression analysis in experimental design, analysis of variance, analysis of covariance, and curvilinear regression. Applications to evaluation of instruction and vocational education programs are illustrated. (GR)

The Influence of Self-Efficacy Beliefs and Metacognitive Prompting on Genetics Problem Solving Ability among High School Students in Kenya

NASA Astrophysics Data System (ADS)

Aurah, Catherine Muhonja

Within the framework of social cognitive theory, the influence of self-efficacy beliefs and metacognitive prompting on genetics problem solving ability among high school students in Kenya was examined through a mixed methods research design. A quasi-experimental study, supplemented by focus group interviews, was conducted to investigate both the outcomes and the processes of students' genetics problem-solving ability. Focus group interviews substantiated and supported findings from the quantitative instruments. The study was conducted in 17 high schools in Western Province, Kenya. A total of 2,138 high school students were purposively sampled. A sub-sample of 48 students participated in focus group interviews to understand their perspectives and experiences during the study so as to corroborate the quantitative data. Quantitative data were analyzed through descriptive statistics, zero-order correlations, 2 x 2 factorial ANOVA,, and sequential hierarchical multiple regressions. Qualitative data were transcribed, coded, and reported thematically. Results revealed metacognitive prompts had significant positive effects on student problem-solving ability independent of gender. Self-efficacy and metacognitive prompting significantly predicted genetics problem-solving ability. Gender differences were revealed, with girls outperforming boys on the genetics problem-solving test. Furthermore, self-efficacy moderated the relationship between metacognitive prompting and genetics problem-solving ability. This study established a foundation for instructional methods for biology teachers and recommendations are made for implementing metacognitive prompting in a problem-based learning environment in high schools and science teacher education programs in Kenya.

Sleep problems and daytime tiredness in Finnish preschool-aged children-a community survey.

PubMed

Simola, P; Niskakangas, M; Liukkonen, K; Virkkula, P; Pitkäranta, A; Kirjavainen, T; Aronen, E T

2010-11-01

Sleep is important to the well-being and development of children. Specially, small children are vulnerable to the effects of inadequate sleep. However, not much is known about the frequency of all types of sleep problems and daytime tiredness in preschool-aged children. To evaluate the prevalence of a wide spectrum of sleep problems, daytime tiredness and associations between these in 3- to 6-year-old Finnish children. A population-based study where parents of 3- to 6-year-old children (n= 904) living in Helsinki filled in the Sleep Disturbance Scale for Children (SDSC). Of the children, 45% had at least one sleep-related problem occurring at least three times a week: 14.1% were unwilling to go to bed, 10.2% had difficulties in falling asleep, 10.2% had bruxism, 6.4% sleep talking, 2.1% sleep terrors, 8.2% had sleep-related breathing problem, 11.2% had excessive sweating while falling asleep and 12.9% excessive sweating during sleep. Age and gender were related to phenotype of the sleeping problems. In multiple regression analysis, the difficulties in initiating and maintaining sleep were most strongly associated with tiredness in the morning and during the day. Different types of sleep problems are frequent in preschool-aged children. Poor sleep quality is associated with morning and daytime tiredness. In screening for sleep problems in children, attention should be paid not only to sleep amount but also to sleep quality. © 2010 Blackwell Publishing Ltd.

Weighted SGD for ℓp Regression with Randomized Preconditioning*

PubMed Central

Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W.

2018-01-01

In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger performance guarantees but are applicable to a narrower class of problems. We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems—e.g., ℓ2 and ℓ1 regression problems. We propose a hybrid algorithm named pwSGD that uses RLA techniques for preconditioning and constructing an importance sampling distribution, and then performs an SGD-like iterative process with weighted sampling on the preconditioned system.By rewriting a deterministic ℓp regression problem as a stochastic optimization problem, we connect pwSGD to several existing ℓp solvers including RLA methods with algorithmic leveraging (RLA for short).We prove that pwSGD inherits faster convergence rates that only depend on the lower dimension of the linear system, while maintaining low computation complexity. Such SGD convergence rates are superior to other related SGD algorithm such as the weighted randomized Kaczmarz algorithm.Particularly, when solving ℓ1 regression with size n by d, pwSGD returns an approximate solution with ε relative error in the objective value in 𝒪(log n·nnz(A)+poly(d)/ε2) time. This complexity is uniformly better than that of RLA methods in terms of both ε and d when the problem is unconstrained. In the presence of constraints, pwSGD only has to solve a sequence of much simpler and smaller optimization problem over the same constraints. In general this is more efficient than solving the constrained subproblem required in RLA.For ℓ2 regression, pwSGD returns an approximate solution with ε relative error in the objective value and the solution vector measured in prediction norm in 𝒪(log n·nnz(A)+poly(d) log(1/ε)/ε) time. We show that for unconstrained ℓ2 regression, this complexity is comparable to that of RLA and is asymptotically better over several state-of-the-art solvers in the regime where the desired accuracy ε, high dimension n and low dimension d satisfy d ≥ 1/ε and n ≥ d2/ε. We also provide lower bounds on the coreset complexity for more general regression problems, indicating that still new ideas will be needed to extend similar RLA preconditioning ideas to weighted SGD algorithms for more general regression problems. Finally, the effectiveness of such algorithms is illustrated numerically on both synthetic and real datasets, and the results are consistent with our theoretical findings and demonstrate that pwSGD converges to a medium-precision solution, e.g., ε = 10−3, more quickly. PMID:29782626

Battered police: risk factors for violence against law enforcement officers.

PubMed

Covington, Michele W; Huff-Corzine, Lin; Corzine, Jay

2014-01-01

Although we hear more about violence committed by the police, violence against police officers is also a major problem in the United States. Using data collected from the Orlando, Florida Police Department files, this study examines situational variables, offender characteristics, and officer demographics that may correlate with violence directed at law enforcement officers. Logistic regression results indicate that battery against one or more police officers is significantly more likely when multiple officers are involved, when offenders are women, when offenders are larger than average as measured by body mass index (BMI), and when offenders are known to have recently consumed alcohol. We close with a discussion of policy implications and directions for future research.

Electric kettles as a source of human lead exposure.

PubMed

Wigle, D T; Charlebois, E J

1978-01-01

Five hundred and seventy-four households in Ottawa were surveyed to evaluate water boiled in electric kettles as a source of lead exposure. Samples of boiled water exceeded the World Health Organization mandatory limit for drinking water (50 microgram/l) in 42.5% of the households. Excessive lead concentrations were observed in 62.8% of water samples from kettles more than 5 years old. Multiple regression analysis indicated that age, sex, and cigarette smoking habits, but not lead concentration in boiled water, nor weekly consumption of boiled water were significantly associated with blood-lead concentration. Lead exposure from electric kettles may be a significant problem only in infants receiving formula prepared with boiled water.

The Associations Between Forced Sex and Severe Mental Health, Substance Use, and HIV Risk Behaviors Among Asian American Women

PubMed Central

Hahm, Hyeouk Chris; Augsberger, Astraea; Feranil, Mario; Jang, Jisun; Tagerman, Michelle

2017-01-01

We examined the association between forced sex history and mental health, sexual health, and substance use among Asian American women (n = 720); 14.3% of our sample (n = 103) reported forced sex experiences. Multiple logistic regression analyses revealed that participants with forced sex histories were 2-8 times more likely to have higher rates of mental health problems, HIV risk behavior, and substance use. Qualitative analysis was used to supplement the quantitative results and give depth to our findings. Our results suggest that interventions for Asian American women who experienced forced sex should integrate mental health, substance use, and sexual health treatments. PMID:27230614

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Stress moderates the relationships between problem-gambling severity and specific psychopathologies.

PubMed

Ronzitti, Silvia; Kraus, Shane W; Hoff, Rani A; Potenza, Marc N

2018-01-01

The purpose of this study was to examine the extent to which stress moderated the relationships between problem-gambling severity and psychopathologies. We analyzed Wave-1 data from 41,869 participants of the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC). Logistic regression showed that as compared to a non-gambling (NG) group, individuals at-risk gambling (ARG) and problem gambling (PPG) demonstrated higher odds of multiple Axis-I and Axis-II disorders in both high- and low-stress groups. Interactions odds ratios were statistically significant for stress moderating the relationships between at-risk gambling (versus non-gambling) and Any Axis-I and Any Axis-II disorder, with substance-use and Cluster-A and Cluster-B disorders contributing significantly. Some similar patterns were observed for pathological gambling (versus non-gambling), with stress moderating relationships with Cluster-B disorders. In all cases, a stronger relationship was observed between problem-gambling severity and psychopathology in the low-stress versus high-stress groups. The findings suggest that perceived stress accounts for some of the variance in the relationship between problem-gambling severity and specific forms of psychopathology, particularly with respect to lower intensity, subsyndromal levels of gambling. Findings suggest that stress may be particularly important to consider in the relationships between problem-gambling severity and substance use and Cluster-B disorders. Published by Elsevier B.V.

Social anxiety and cannabis-related impairment: The synergistic influences of peer and parent descriptive and injunctive normative perceptions

PubMed Central

Foster, Dawn W.; Garey, Lorra; Buckner, Julia D.; Zvolensky, Michael J.

2016-01-01

Objectives Cannabis users, especially socially anxious cannabis users, are influenced by perceptions of other’s use. The present study tested whether social anxiety interacted with perceptions about peer and parent beliefs to predict cannabis-related problems. Methods Participants were 148 (36.5% female, 60.1% non-Hispanic Caucasian) current cannabis users aged 18–36 (M = 21.01, SD = 3.09) who completed measures of perceived descriptive and injunctive norms, social anxiety, and cannabis use behaviors. Hierarchical multiple regressions were employed to investigate the predictive value of the social anxiety × parent injunctive norms × peer norms interaction terms on cannabis use behaviors. Results Higher social anxiety was associated with more cannabis problems. A three-way interaction emerged between social anxiety, parent injunctive norms, and peer descriptive norms, with respect to cannabis problems. Social anxiety was positively related to more cannabis problems when parent injunctive norms were high (i.e., perceived approval) and peer descriptive norms were low. Results further showed that social anxiety was positively related to more cannabis problems regardless of parent injunctive norms. Conclusions The present work suggest that it may be important to account for parent influences when addressing normative perceptions among young adult cannabis users. Additional research is needed to determine whether interventions incorporating feedback regarding parent norms impacts cannabis use frequency and problems. PMID:27144526

Multi-Axis Identifiability Using Single-Surface Parameter Estimation Maneuvers on the X-48B Blended Wing Body

NASA Technical Reports Server (NTRS)

Ratnayake, Nalin A.; Koshimoto, Ed T.; Taylor, Brian R.

2011-01-01

The problem of parameter estimation on hybrid-wing-body type aircraft is complicated by the fact that many design candidates for such aircraft involve a large number of aero- dynamic control effectors that act in coplanar motion. This fact adds to the complexity already present in the parameter estimation problem for any aircraft with a closed-loop control system. Decorrelation of system inputs must be performed in order to ascertain individual surface derivatives with any sort of mathematical confidence. Non-standard control surface configurations, such as clamshell surfaces and drag-rudder modes, further complicate the modeling task. In this paper, asymmetric, single-surface maneuvers are used to excite multiple axes of aircraft motion simultaneously. Time history reconstructions of the moment coefficients computed by the solved regression models are then compared to each other in order to assess relative model accuracy. The reduced flight-test time required for inner surface parameter estimation using multi-axis methods was found to come at the cost of slightly reduced accuracy and statistical confidence for linear regression methods. Since the multi-axis maneuvers captured parameter estimates similar to both longitudinal and lateral-directional maneuvers combined, the number of test points required for the inner, aileron-like surfaces could in theory have been reduced by 50%. While trends were similar, however, individual parameters as estimated by a multi-axis model were typically different by an average absolute difference of roughly 15-20%, with decreased statistical significance, than those estimated by a single-axis model. The multi-axis model exhibited an increase in overall fit error of roughly 1-5% for the linear regression estimates with respect to the single-axis model, when applied to flight data designed for each, respectively.

Approach to addressing missing data for electronic medical records and pharmacy claims data research.

PubMed

Bounthavong, Mark; Watanabe, Jonathan H; Sullivan, Kevin M

2015-04-01

The complete capture of all values for each variable of interest in pharmacy research studies remains aspirational. The absence of these possibly influential values is a common problem for pharmacist investigators. Failure to account for missing data may translate to biased study findings and conclusions. Our goal in this analysis was to apply validated statistical methods for missing data to a previously analyzed data set and compare results when missing data methods were implemented versus standard analytics that ignore missing data effects. Using data from a retrospective cohort study, the statistical method of multiple imputation was used to provide regression-based estimates of the missing values to improve available data usable for study outcomes measurement. These findings were then contrasted with a complete-case analysis that restricted estimation to subjects in the cohort that had no missing values. Odds ratios were compared to assess differences in findings of the analyses. A nonadjusted regression analysis ("crude analysis") was also performed as a reference for potential bias. Veterans Integrated Systems Network that includes VA facilities in the Southern California and Nevada regions. New statin users between November 30, 2006, and December 2, 2007, with a diagnosis of dyslipidemia. We compared the odds ratios (ORs) and 95% confidence intervals (CIs) for the crude, complete-case, and multiple imputation analyses for the end points of a 25% or greater reduction in atherogenic lipids. Data were missing for 21.5% of identified patients (1665 subjects of 7739). Regression model results were similar for the crude, complete-case, and multiple imputation analyses with overlap of 95% confidence limits at each end point. The crude, complete-case, and multiple imputation ORs (95% CIs) for a 25% or greater reduction in low-density lipoprotein cholesterol were 3.5 (95% CI 3.1-3.9), 4.3 (95% CI 3.8-4.9), and 4.1 (95% CI 3.7-4.6), respectively. The crude, complete-case, and multiple imputation ORs (95% CIs) for a 25% or greater reduction in non-high-density lipoprotein cholesterol were 3.5 (95% CI 3.1-3.9), 4.5 (95% CI 4.0-5.2), and 4.4 (95% CI 3.9-4.9), respectively. The crude, complete-case, and multiple imputation ORs (95% CIs) for 25% or greater reduction in TGs were 3.1 (95% CI 2.8-3.6), 4.0 (95% CI 3.5-4.6), and 4.1 (95% CI 3.6-4.6), respectively. The use of the multiple imputation method to account for missing data did not alter conclusions based on a complete-case analysis. Given the frequency of missing data in research using electronic health records and pharmacy claims data, multiple imputation may play an important role in the validation of study findings. © 2015 Pharmacotherapy Publications, Inc.

A regressive methodology for estimating missing data in rainfall daily time series

NASA Astrophysics Data System (ADS)

Barca, E.; Passarella, G.

2009-04-01

The "presence" of gaps in environmental data time series represents a very common, but extremely critical problem, since it can produce biased results (Rubin, 1976). Missing data plagues almost all surveys. The problem is how to deal with missing data once it has been deemed impossible to recover the actual missing values. Apart from the amount of missing data, another issue which plays an important role in the choice of any recovery approach is the evaluation of "missingness" mechanisms. When data missing is conditioned by some other variable observed in the data set (Schafer, 1997) the mechanism is called MAR (Missing at Random). Otherwise, when the missingness mechanism depends on the actual value of the missing data, it is called NCAR (Not Missing at Random). This last is the most difficult condition to model. In the last decade interest arose in the estimation of missing data by using regression (single imputation). More recently multiple imputation has become also available, which returns a distribution of estimated values (Scheffer, 2002). In this paper an automatic methodology for estimating missing data is presented. In practice, given a gauging station affected by missing data (target station), the methodology checks the randomness of the missing data and classifies the "similarity" between the target station and the other gauging stations spread over the study area. Among different methods useful for defining the similarity degree, whose effectiveness strongly depends on the data distribution, the Spearman correlation coefficient was chosen. Once defined the similarity matrix, a suitable, nonparametric, univariate, and regressive method was applied in order to estimate missing data in the target station: the Theil method (Theil, 1950). Even though the methodology revealed to be rather reliable an improvement of the missing data estimation can be achieved by a generalization. A first possible improvement consists in extending the univariate technique to the multivariate approach. Another approach follows the paradigm of the "multiple imputation" (Rubin, 1987; Rubin, 1988), which consists in using a set of "similar stations" instead than the most similar. This way, a sort of estimation range can be determined allowing the introduction of uncertainty. Finally, time series can be grouped on the basis of monthly rainfall rates defining classes of wetness (i.e.: dry, moderately rainy and rainy), in order to achieve the estimation using homogeneous data subsets. We expect that integrating the methodology with these enhancements will certainly improve its reliability. The methodology was applied to the daily rainfall time series data registered in the Candelaro River Basin (Apulia - South Italy) from 1970 to 2001. REFERENCES D.B., Rubin, 1976. Inference and Missing Data. Biometrika 63 581-592 D.B. Rubin, 1987. Multiple Imputation for Nonresponce in Surveys, New York: John Wiley & Sons, Inc. D.B. Rubin, 1988. An overview of multiple imputation. In Survey Research Section, pp. 79-84, American Statistical Association, 1988. J.L., Schafer, 1997. Analysis of Incomplete Multivariate Data, Chapman & Hall. J., Scheffer, 2002. Dealing with Missing Data. Res. Lett. Inf. Math. Sci. 3, 153-160. Available online at http://www.massey.ac.nz/~wwiims/research/letters/ H. Theil, 1950. A rank-invariant method of linear and polynomial regression analysis. Indicationes Mathematicae, 12, pp.85-91.

Relationships Between Problem-Gambling Severity and Psychopathology as Moderated by Income

PubMed Central

Sanacora, Rachel L.; Whiting, Seth W.; Pilver, Corey E.; Hoff, Rani A.; Potenza, Marc N.

2016-01-01

Background and aims Problem and pathological gambling have been associated with elevated rates of both Axis-I and Axis-II psychiatric disorders. Although both problem gambling and psychiatric disorders have been reported as being more prevalent among lower income vs. middle/higher income groups, how income might moderate the relationship between problem-gambling severity and psychopathology is incompletely understood. To examine the associations between problem-gambling severity and psychopathology in lower income and middle/higher income groups. Methods Data from the first wave of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) (n = 43,093) were analyzed in adjusted logistic regression models to investigate the relationships between problem-gambling severity and psychiatric disorders within and across income groups. Results Greater problem-gambling severity was associated with increased odds of multiple psychiatric disorders for both lower income and middle/higher income groups. Income moderated the association between problem/pathological gambling and alcohol abuse/dependence, with a stronger association seen among middle/higher income respondents than among lower income respondents. Discussion and conclusions The findings that problem-gambling severity is related to psychopathology across income groups suggest a need for public health initiatives across social strata to reduce the impact that problem/pathological gambling may have in relation to psychopathology. Middle/higher income populations, perhaps owing to the availability of more “disposable income,” may be at greater risk for co-occurring gambling and alcohol-use psychopathology and may benefit preferentially from interventions targeting both gambling and alcohol use. PMID:27440475

Associations between family and clinician ratings of child mental health: A study of UK CAMHS assessments and outcomes.

PubMed

Terrelonge, Dion N; Fugard, Andrew Jb

2017-10-01

The rated severity of child mental health problems depends on who is doing the rating, whether child, carer or clinician. It is important to know how these ratings relate to each other. To investigate to what extent clinicians' views are associated with carers' and young people's views in routine care in the United Kingdom. Ratings of clinician and parent/child viewpoints from a large Child and Adolescent Mental Health Services (CAMHS) sample ( ns 1773-47,299), as measured by the Children's Global Assessment Scale (CGAS) and Strengths and Difficulties Questionnaire (SDQ) respectively, were analysed. The parent SDQ added value score (AVS), which adjusts for regression to the mean and other non-treatment change, was also included in the analyses. Small-to-medium correlations were found between family and clinician ratings; however, ratings diverged for the lowest-function CGAS bands. Regression analyses showed that pro-social ratings from both child and parent contributed to clinician ratings. Knowing child-reported emotional problem severity made parent ratings of emotions irrelevant to clinician judgements. There was a positive association between SDQ AVS and CGAS; as hypothesised, CGAS showed more change than the SDQ AVS, suggesting that clinicians over-estimate change. This study shows the importance of multi-informant data gathering and the integration of multiple views by clinicians when monitoring outcomes.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study and an Application

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Cimpian, Joseph R.

2017-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

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

ERIC Educational Resources Information Center

Woolley, Kristin K.

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

Death Anxiety as a Predictor of Posttraumatic Stress Levels among Individuals with Spinal Cord Injuries

ERIC Educational Resources Information Center

Martz, Erin

2004-01-01

Because the onset of a spinal cord injury may involve a brush with death and because serious injury and disability can act as a reminder of death, death anxiety was examined as a predictor of posttraumatic stress levels among individuals with disabilities. This cross-sectional study used multiple regression and multivariate multiple regression to…

Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016).

PubMed

McClelland, Gary H; Irwin, Julie R; Disatnik, David; Sivan, Liron

2017-02-01

Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.

Problem-Solving After Traumatic Brain Injury in Adolescence: Associations With Functional Outcomes

PubMed Central

Wade, Shari L.; Cassedy, Amy E.; Fulks, Lauren E.; Taylor, H. Gerry; Stancin, Terry; Kirkwood, Michael W.; Yeates, Keith O.; Kurowski, Brad G.

2017-01-01

Objective To examine the association of problem-solving with functioning in youth with traumatic brain injury (TBI). Design Cross-sectional evaluation of pretreatment data from a randomized controlled trial. Setting Four children’s hospitals and 1 general hospital, with level 1 trauma units. Participants Youth, ages 11 to 18 years, who sustained moderate or severe TBI in the last 18 months (N=153). Main Outcome Measures Problem-solving skills were assessed using the Social Problem-Solving Inventory (SPSI) and the Dodge Social Information Processing Short Stories. Everyday functioning was assessed based on a structured clinical interview using the Child and Adolescent Functional Assessment Scale (CAFAS) and via adolescent ratings on the Youth Self Report (YSR). Correlations and multiple regression analyses were used to examine associations among measures. Results The TBI group endorsed lower levels of maladaptive problem-solving (negative problem orientation, careless/impulsive responding, and avoidant style) and lower levels of rational problem-solving, resulting in higher total problem-solving scores for the TBI group compared with a normative sample (P<.001). Dodge Social Information Processing Short Stories dimensions were correlated (r=.23–.37) with SPSI subscales in the anticipated direction. Although both maladaptive (P<.001) and adaptive (P=.006) problem-solving composites were associated with overall functioning on the CAFAS, only maladaptive problem-solving (P<.001) was related to the YSR total when outcomes were continuous. For the both CAFAS and YSR logistic models, maladaptive style was significantly associated with greater risk of impairment (P=.001). Conclusions Problem-solving after TBI differs from normative samples and is associated with functional impairments. The relation of problem-solving deficits after TBI with global functioning merits further investigation, with consideration of the potential effects of problem-solving interventions on functional outcomes. PMID:28389109

Problem-Solving After Traumatic Brain Injury in Adolescence: Associations With Functional Outcomes.

PubMed

Wade, Shari L; Cassedy, Amy E; Fulks, Lauren E; Taylor, H Gerry; Stancin, Terry; Kirkwood, Michael W; Yeates, Keith O; Kurowski, Brad G

2017-08-01

To examine the association of problem-solving with functioning in youth with traumatic brain injury (TBI). Cross-sectional evaluation of pretreatment data from a randomized controlled trial. Four children's hospitals and 1 general hospital, with level 1 trauma units. Youth, ages 11 to 18 years, who sustained moderate or severe TBI in the last 18 months (N=153). Problem-solving skills were assessed using the Social Problem-Solving Inventory (SPSI) and the Dodge Social Information Processing Short Stories. Everyday functioning was assessed based on a structured clinical interview using the Child and Adolescent Functional Assessment Scale (CAFAS) and via adolescent ratings on the Youth Self Report (YSR). Correlations and multiple regression analyses were used to examine associations among measures. The TBI group endorsed lower levels of maladaptive problem-solving (negative problem orientation, careless/impulsive responding, and avoidant style) and lower levels of rational problem-solving, resulting in higher total problem-solving scores for the TBI group compared with a normative sample (P<.001). Dodge Social Information Processing Short Stories dimensions were correlated (r=.23-.37) with SPSI subscales in the anticipated direction. Although both maladaptive (P<.001) and adaptive (P=.006) problem-solving composites were associated with overall functioning on the CAFAS, only maladaptive problem-solving (P<.001) was related to the YSR total when outcomes were continuous. For the both CAFAS and YSR logistic models, maladaptive style was significantly associated with greater risk of impairment (P=.001). Problem-solving after TBI differs from normative samples and is associated with functional impairments. The relation of problem-solving deficits after TBI with global functioning merits further investigation, with consideration of the potential effects of problem-solving interventions on functional outcomes. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

[Establishment of multiple regression model for virulence factors of Saccharomyces albicans by random amplified polymorphic DNA bands].

PubMed

Liu, Qi; Wu, Youcong; Yuan, Youhua; Bai, Li; Niu, Kun

2011-12-01

To research the relationship between the virulence factors of Saccharomyces albicans (S. albicans) and the random amplified polymorphic DNA (RAPD) bands of them, and establish the regression model by multiple regression analysis. Extracellular phospholipase, secreted proteinase, ability to generate germ tubes and adhere to oral mucosal cells of 92 strains of S. albicans were measured in vitro; RAPD-polymerase chain reaction (RAPD-PCR) was used to get their bands. Multiple regression for virulence factors of S. albicans and RAPD-PCR bands was established. The extracellular phospholipase activity was associated with 4 RAPD bands: 350, 450, 650 and 1 300 bp (P < 0.05); secreted proteinase activity of S. albicans was associated with 2 bands: 350 and 1 200 bp (P < 0.05); the ability of germ tube produce was associated with 2 bands: 400 and 550 bp (P < 0.05). Some RAPD bands will reflect the virulence factors of S. albicans indirectly. These bands would contain some important messages for regulation of S. albicans virulence factors.

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

Step by Step: Biology Undergraduates' Problem-Solving Procedures during Multiple-Choice Assessment

ERIC Educational Resources Information Center

Prevost, Luanna B.; Lemons, Paula P.

2016-01-01

This study uses the theoretical framework of domain-specific problem solving to explore the procedures students use to solve multiple-choice problems about biology concepts. We designed several multiple-choice problems and administered them on four exams. We trained students to produce written descriptions of how they solved the problem, and this…

Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings.

PubMed

Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

2017-08-01

Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. A 'Rich Picture' was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further consideration. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Using ridge regression in systematic pointing error corrections

NASA Technical Reports Server (NTRS)

Guiar, C. N.

1988-01-01

A pointing error model is used in the antenna calibration process. Data from spacecraft or radio star observations are used to determine the parameters in the model. However, the regression variables are not truly independent, displaying a condition known as multicollinearity. Ridge regression, a biased estimation technique, is used to combat the multicollinearity problem. Two data sets pertaining to Voyager 1 spacecraft tracking (days 105 and 106 of 1987) were analyzed using both linear least squares and ridge regression methods. The advantages and limitations of employing the technique are presented. The problem is not yet fully resolved.

Biopsychosocial Predictors of Fall Events among Older African Americans

PubMed Central

Nicklett, Emily Joy; Taylor, Robert Joseph; Rostant, Ola; Johnson, Kimson E.; Evans, Linnea

2016-01-01

This study identifies risk and protective factors for falls among older, community-dwelling African Americans. Drawing upon the biopsychosocial perspective (Engel, 1997), we conducted a series of sex- and age-adjusted multinomial logistic regression analyses to identify the correlates of fall events among older African Americans. Our sample consisted of 1,442 community-dwelling African Americans aged 65 and older, participating in the 2010-12 rounds of the Health and Retirement Study. Biophysical characteristics associated with greater relative risk of experiencing single and/or multiple falls included greater functional limitations, poorer self-rated health, poorer self-rated vision, chronic illnesses (high blood pressure, diabetes, cancer, lung disease, heart problems, stroke, and arthritis), greater chronic illness comorbidity, older age, and female sex. Physical activity was negatively associated with recurrent falls. Among the examined psychosocial characteristics, greater depressive symptoms were associated with greater relative risk of experiencing single and multiple fall events. Implications for clinicians and future studies are discussed. PMID:28285579

Cooperativeness and bully/victim problems among Australian schoolchildren.

PubMed

Rigby, K; Cox, I; Black, G

1997-06-01

The relationship was examined between the self-reported cooperativeness of Australian secondary-school students and their involvement in peer abuse at school, both as bullies and as victims. An 18-item Likert-type measure, the Cooperativeness Scale, was developed, and its reliability and concurrent validity were supported by the results of its application to two samples of Australian students (N = 176 and N = 763, respectively) attending different coeducational secondary schools, the first in a predominantly middle-class area and the second in a lower class socioeconomic area. At both schools, girls scored higher in cooperativeness than boys. Students at the second school also anonymously completed multiple measures of the extent of their involvement during the current year in bullying, victimization, or both. As predicted, correlations and multiple regression analyses supported the hypothesis that relatively low levels of cooperativeness were characteristic, not only of both boys and girls who engaged in bullying, but also, to a lesser extent, of those who were frequently victimized by their peers at school.

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

PubMed

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

2015-01-01

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

Data-driven discovery of partial differential equations

PubMed Central

Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan

2017-01-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg–de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable. PMID:28508044

Squeezing observational data for better causal inference: Methods and examples for prevention research.

PubMed

Garcia-Huidobro, Diego; Michael Oakes, J

2017-04-01

Randomised controlled trials (RCTs) are typically viewed as the gold standard for causal inference. This is because effects of interest can be identified with the fewest assumptions, especially imbalance in background characteristics. Yet because conducting RCTs are expensive, time consuming and sometimes unethical, observational studies are frequently used to study causal associations. In these studies, imbalance, or confounding, is usually controlled with multiple regression, which entails strong assumptions. The purpose of this manuscript is to describe strengths and weaknesses of several methods to control for confounding in observational studies, and to demonstrate their use in cross-sectional dataset that use patient registration data from the Juan Pablo II Primary Care Clinic in La Pintana-Chile. The dataset contains responses from 5855 families who provided complete information on family socio-demographics, family functioning and health problems among their family members. We employ regression adjustment, stratification, restriction, matching, propensity score matching, standardisation and inverse probability weighting to illustrate the approaches to better causal inference in non-experimental data and compare results. By applying study design and data analysis techniques that control for confounding in different ways than regression adjustment, researchers may strengthen the scientific relevance of observational studies. © 2016 International Union of Psychological Science.

Hyperspectral analysis of soil organic matter in coal mining regions using wavelets, correlations, and partial least squares regression.

PubMed

Lin, Lixin; Wang, Yunjia; Teng, Jiyao; Wang, Xuchen

2016-02-01

Hyperspectral estimation of soil organic matter (SOM) in coal mining regions is an important tool for enhancing fertilization in soil restoration programs. The correlation--partial least squares regression (PLSR) method effectively solves the information loss problem of correlation--multiple linear stepwise regression, but results of the correlation analysis must be optimized to improve precision. This study considers the relationship between spectral reflectance and SOM based on spectral reflectance curves of soil samples collected from coal mining regions. Based on the major absorption troughs in the 400-1006 nm spectral range, PLSR analysis was performed using 289 independent bands of the second derivative (SDR) with three levels and measured SOM values. A wavelet-correlation-PLSR (W-C-PLSR) model was then constructed. By amplifying useful information that was previously obscured by noise, the W-C-PLSR model was optimal for estimating SOM content, with smaller prediction errors in both calibration (R(2) = 0.970, root mean square error (RMSEC) = 3.10, and mean relative error (MREC) = 8.75) and validation (RMSEV = 5.85 and MREV = 14.32) analyses, as compared with other models. Results indicate that W-C-PLSR has great potential to estimate SOM in coal mining regions.

Modelling space of spread Dengue Hemorrhagic Fever (DHF) in Central Java use spatial durbin model

NASA Astrophysics Data System (ADS)

Ispriyanti, Dwi; Prahutama, Alan; Taryono, Arkadina PN

2018-05-01

Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial Durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. By using queen contiguity and rook contiguity, the best model produced is the SDM model with queen contiguity because it has the smallest AIC value of 494,12. Factors that generally affect the spread of DHF in Central Java Province are the number of population and the average length of school.

Cognitive, emotive, and cognitive-behavioral correlates of suicidal ideation among Chinese adolescents in Hong Kong.

PubMed

Kwok, Sylvia Lai Yuk Ching; Shek, Daniel Tan Lei

2010-03-05

Utilizing Daniel Goleman's theory of emotional competence, Beck's cognitive theory, and Rudd's cognitive-behavioral theory of suicidality, the relationships between hopelessness (cognitive component), social problem solving (cognitive-behavioral component), emotional competence (emotive component), and adolescent suicidal ideation were examined. Based on the responses of 5,557 Secondary 1 to Secondary 4 students from 42 secondary schools in Hong Kong, results showed that suicidal ideation was positively related to adolescent hopelessness, but negatively related to emotional competence and social problem solving. While standard regression analyses showed that all the above variables were significant predictors of suicidal ideation, hierarchical regression analyses showed that hopelessness was the most important predictor of suicidal ideation, followed by social problem solving and emotional competence. Further regression analyses found that all four subscales of emotional competence, i.e., empathy, social skills, self-management of emotions, and utilization of emotions, were important predictors of male adolescent suicidal ideation. However, the subscale of social skills was not a significant predictor of female adolescent suicidal ideation. Standard regression analysis also revealed that all three subscales of social problem solving, i.e., negative problem orientation, rational problem solving, and impulsiveness/carelessness style, were important predictors of suicidal ideation. Theoretical and practice implications of the findings are discussed.

Short sleep duration and irregular bedtime are associated with increased behavioral problems among Japanese preschool-age children.

PubMed

Komada, Yoko; Abe, Takashi; Okajima, Isa; Asaoka, Shoichi; Matsuura, Noriko; Usui, Akira; Shirakawa, Shuichiro; Inoue, Yuichi

2011-06-01

Sleep problems are known to be risk factors for subsequent emotional and behavioral difficulties in childhood and adolescence. To date, there has been no study investigating the relationships between sleep habits and behavioral problems in a large nonclinical sample of preschool age children. The aim of this study was to examine these relationships and factors associated with the sleep habits of preschool age (2 to 5 year old) children. Their mothers (n = 1,746) completed a multiple-choice questionnaire about the sleep habits and behavior problems of their children, as well as their own sleep habits and working hours at Tokyo metropolitan public nursery schools. The short sleep duration group showed significantly higher aggressive scores than the long sleep duration group among 2- to 3-year-old children, and the irregular bedtime group showed significantly higher aggressive and attention problem scores than the regular bedtime group among 4- to 5-year-old children. Univariate and multivariate logistic regression analyses revealed that children's late bedtime was associated with their mother's late waking-up time, and late schedule of both the mother's leaving and returning home. This study recognized an association between behavioral problems and poor sleep habits among preschool-age children. It is important for children to sleep regularly and adequately in order to decrease their behavior problems. In conclusion, appropriate management of children's sleep by their mothers is necessary for promoting sleep-related health of children.

Problem solving performance and learning strategies of undergraduate students who solved microbiology problems using IMMEX educational software

NASA Astrophysics Data System (ADS)

Ebomoyi, Josephine Itota

The objectives of this study were as follows: (1) Determine the relationship between learning strategies and performance in problem solving, (2) Explore the role of a student's declared major on performance in problem solving, (3) Understand the decision making process of high and low achievers during problem solving. Participants (N = 65) solved problems using the Interactive multimedia exercise (IMMEX) software. All participants not only solved "Microquest," which focuses on cellular processes and mode of action of antibiotics, but also "Creeping Crud," which focuses on the cause, origin and transmission of diseases. Participants also responded to the "Motivated Strategy Learning Questionnaire" (MSLQ). Hierarchical multiple regression was used for analysis with GPA (Gracie point average) as a control. There were 49 (78.6%) that successfully solved "Microquest" while 52 (82.5%) successfully solved "Creeping Crud". Metacognitive self regulation strategy was significantly (p < .10) related to ability to solve "Creeping Crud". Peer learning strategy showed a positive significant (p < .10) relationship with scores obtained from solving "Creeping Crud". Students' declared major made a significant (p < .05) difference on the ability to solve "Microquest". A subset (18) volunteered for a think aloud method to determine decision-making process. High achievers used fewer steps, and had more focused approach than low achievers. Common strategies and attributes included metacognitive skills, writing to keep track, using prior knowledge. Others included elements of frustration/confusion and self-esteem problems. The implications for educational and relevance to real life situations are discussed.

Are Survivors Who Report Cancer-Related Financial Problems More Likely to Forgo or Delay Medical Care?

PubMed Central

Kent, Erin E.; Forsythe, Laura P.; Yabroff, K. Robin; Weaver, Kathryn E.; de Moor, Janet S.; Rodriguez, Juan L.; Rowland, Julia H.

2015-01-01

BACKGROUND Financial problems caused by cancer and its treatment can substantially affect survivors and their families and create barriers to seeking health care. METHODS The authors identified cancer survivors diagnosed as adults (n = 1556) from the nationally representative 2010 National Health Interview Survey. Using multivariable logistic regression analyses, the authors report sociodemographic, clinical, and treatment-related factors associated with perceived cancer-related financial problems and the association between financial problems and forgoing or delaying health care because of cost. Adjusted percentages using the predictive marginals method are presented. RESULTS Cancer-related financial problems were reported by 31.8% (95% confidence interval, 29.3%–34.5%) of survivors. Factors found to be significantly associated with cancer-related financial problems in survivors included younger age at diagnosis, minority race/ethnicity, history of chemotherapy or radiation treatment, recurrence or multiple cancers, and shorter time from diagnosis. After adjustment for covariates, respondents who reported financial problems were more likely to report delaying (18.3% vs 7.4%) or forgoing overall medical care (13.8% vs 5.0%), prescription medications (14.2% vs 7.6%), dental care (19.8% vs 8.3%), eyeglasses (13.9% vs 5.8%), and mental health care (3.9% vs 1.6%) than their counterparts without financial problems (all P<.05). CONCLUSIONS Cancer-related financial problems are not only disproportionately represented in survivors who are younger, members of a minority group, and have a higher treatment burden, but may also contribute to survivors forgoing or delaying medical care after cancer. PMID:23907958

Are survivors who report cancer-related financial problems more likely to forgo or delay medical care?

PubMed

Kent, Erin E; Forsythe, Laura P; Yabroff, K Robin; Weaver, Kathryn E; de Moor, Janet S; Rodriguez, Juan L; Rowland, Julia H

2013-10-15

Financial problems caused by cancer and its treatment can substantially affect survivors and their families and create barriers to seeking health care. The authors identified cancer survivors diagnosed as adults (n=1556) from the nationally representative 2010 National Health Interview Survey. Using multivariable logistic regression analyses, the authors report sociodemographic, clinical, and treatment-related factors associated with perceived cancer-related financial problems and the association between financial problems and forgoing or delaying health care because of cost. Adjusted percentages using the predictive marginals method are presented. Cancer-related financial problems were reported by 31.8% (95% confidence interval, 29.3%-34.5%) of survivors. Factors found to be significantly associated with cancer-related financial problems in survivors included younger age at diagnosis, minority race/ethnicity, history of chemotherapy or radiation treatment, recurrence or multiple cancers, and shorter time from diagnosis. After adjustment for covariates, respondents who reported financial problems were more likely to report delaying (18.3% vs 7.4%) or forgoing overall medical care (13.8% vs 5.0%), prescription medications (14.2% vs 7.6%), dental care (19.8% vs 8.3%), eyeglasses (13.9% vs 5.8%), and mental health care (3.9% vs 1.6%) than their counterparts without financial problems (all P<.05). Cancer-related financial problems are not only disproportionately represented in survivors who are younger, members of a minority group, and have a higher treatment burden, but may also contribute to survivors forgoing or delaying medical care after cancer. Copyright © 2013 American Cancer Society.

Forecasting USAF JP-8 Fuel Needs

DTIC Science & Technology

2009-03-01

versus complex ones. When we consider long -term forecasts, 5-years in this case, multiple regression outperforms ANN modeling within the specified...with more simple and easy-to-implement methods, versus complex ones. When we consider long -term 5-year forecasts, our multiple regression model...effort. The insight and experience was certainly appreciated. Special thanks to my Turkish peers for their continuous support and help during this long

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

ERIC Educational Resources Information Center

Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Using multiple calibration sets to improve the quantitative accuracy of partial least squares (PLS) regression on open-path fourier transform infrared (OP/FT-IR) spectra of ammonia over wide concentration ranges

USDA-ARS?s Scientific Manuscript database

A technique of using multiple calibration sets in partial least squares regression (PLS) was proposed to improve the quantitative determination of ammonia from open-path Fourier transform infrared spectra. The spectra were measured near animal farms, and the path-integrated concentration of ammonia...

The relationship between intention to leave the hospital and coping methods of emergency nurses after workplace violence.

PubMed

Jeong, In-Young; Kim, Ji-Soo

2018-04-01

To identify the relationship between emergency nurses' intention to leave the hospital and their coping methods following workplace violence. Emergency departments report a high prevalence of workplace violence, with nurses being at particular risk of violence from patients and patients' relatives. Violence negatively influences nurses' personal and professional lives and increases their turnover. This is a cross-sectional, descriptive survey study. Participants were nurses (n = 214) with over one year of experience of working in an emergency department. We measured workplace violence, coping after workplace violence experiences and job satisfaction using scales validated through a preliminary survey. Questionnaires were distributed to all nurses who signed informed consent forms. Multiple logistic regression analysis was used to identify the relationships between nurses' intention to leave the hospital and their coping methods after workplace violence. Verbal abuse was the most frequent violence experience and more often originated from patients' relatives than from patients. Of the nurses who experienced violence, 61.0% considered leaving the hospital. As for coping, nurses who employed problem-focused coping most frequently sought to identify the problems that cause violence, while nurses who employed emotion-focused coping primarily attempted to endure the situation. The multiple logistic regression analysis revealed that female sex, emotion-focused coping and job satisfaction were significantly related to emergency nurses' intention to leave. Emotion-focused coping seems to have a stronger effect on intention to leave after experiencing violence than does job satisfaction. Nurse managers should begin providing emergency nurses with useful information to guide their management of violence experiences. Nurse managers should also encourage nurses to report violent experiences to the administrative department rather than resorting to emotion-focused coping. Nurses should be provided with the opportunity to communicate their feelings to their colleagues. © 2017 John Wiley & Sons Ltd.

Relationship between the non-motor items of the MDS-UPDRS and Quality of Life in patients with Parkinson's disease.

PubMed

Skorvanek, Matej; Rosenberger, Jaroslav; Minar, Michal; Grofik, Milan; Han, Vladimir; Groothoff, Johan W; Valkovic, Peter; Gdovinova, Zuzana; van Dijk, Jitse P

2015-01-01

The Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is a newly developed comprehensive tool to assess Parkinson's disease (PD), which covers a wider range of non-motor PD manifestations than the original UPDRS scale. The aim of this study was to assess the relationship between the MDS-UPDRS and Quality of Life (QoL) and to analyze the relationship between individual MDS-UPDRS non-motor items and QoL. A total of 291 PD patients were examined in a multicenter Slovak study. Patients were assessed by the MDS-UPDRS, HY scale and PDQ39. Data were analyzed using the multiple regression analyses. The mean participant age was 68.0 ± 9.0 years, 53.5% were men, mean disease duration was 8.3 ± 5.3 years and mean HY was 2.7 ± 1.0. In a multiple regression analysis model the PDQ39 summary index was related to MDS-UPDRS parts II, I and IV respectively, but not to part III. Individual MDS-UPDRS non-motor items related to the PDQ39 summary index in the summary group and in the non-fluctuating patients subgroup were pain, fatigue and features of dopamine dysregulation syndrome (DDS). In the fluctuating PD patient subgroup, PDQ39 was related to pain and Depressed mood items. Other MDS-UPDRS non-motor items e.g. Anxious mood, Apathy, Cognitive impairment, Hallucinations and psychosis, Sleep problems, Daytime sleepiness and Urinary problems were related to some PDQ39 domains. The overall burden of NMS in PD is more important in terms of QoL than motor symptoms. Individual MDS-UPDRS non-motor items related to worse QoL are especially pain and other sensations, fatigue and features of DDS. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

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

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Evaluation of the comprehensive palatability of Japanese sake paired with dishes by multiple regression analysis based on subdomains.

PubMed

Nakamura, Ryo; Nakano, Kumiko; Tamura, Hiroyasu; Mizunuma, Masaki; Fushiki, Tohru; Hirata, Dai

2017-08-01

Many factors contribute to palatability. In order to evaluate the palatability of Japanese alcohol sake paired with certain dishes by integrating multiple factors, here we applied an evaluation method previously reported for palatability of cheese by multiple regression analysis based on 3 subdomain factors (rewarding, cultural, and informational). We asked 94 Japanese participants/subjects to evaluate the palatability of sake (1st evaluation/E1 for the first cup, 2nd/E2 and 3rd/E3 for the palatability with aftertaste/afterglow of certain dishes) and to respond to a questionnaire related to 3 subdomains. In E1, 3 factors were extracted by a factor analysis, and the subsequent multiple regression analyses indicated that the palatability of sake was interpreted by mainly the rewarding. Further, the results of attribution-dissections in E1 indicated that 2 factors (rewarding and informational) contributed to the palatability. Finally, our results indicated that the palatability of sake was influenced by the dish eaten just before drinking.

Association between severe eczema in children and multiple comorbid conditions and increased healthcare utilization

PubMed Central

Silverberg, Jonathan I.; Simpson, Eric L.

2015-01-01

Background Atopic dermatitis (AD) is associated with multiple comorbid conditions, such as asthma and food allergy. We sought to determine the impact of eczema severity on the development of these disorders and other non-atopic comorbidities in AD. Methods We used the 2007 National Survey of Children's Health, a prospective questionnaire-based study of a nationally representative sample of 91,642 children age 0-17 years. Prevalence and severity of eczema, asthma, hay fever and food allergy, sleep impairment, healthcare utilization, recurrent ear infections, visual and dental problems were determined. Results In general, more severe eczema correlated with poorer overall health, impaired sleep and increased healthcare utilization, including seeing a specialist, compared to children with mild or moderate disease (Rao-Scott Chi-square, P<0.0001). Severe eczema was associated with higher prevalence of comorbid chronic health disorders, including asthma, hay fever and food allergies (P<0.0001). In addition, the severity of eczema was directly related to the severity of the comorbidities. These associations remained significant in multivariate logistic regression models that included age, sex and race/ethnicity. Severe eczema was also associated with recent dental problems, including bleeding gums (P<0.0001), toothache (P=0.0004), but not broken teeth (P=0.04) or tooth decay (P=0.13). Conclusions These data indicate that severe eczema is associated with multiple comorbid chronic health disorders, impaired overall health and increased healthcare utilization. Further, these data suggest that children with eczema are at risk for decreased oral health. Future studies are warranted to verify this novel association. PMID:23773154

Learning style and concept acquisition of community college students in introductory biology

NASA Astrophysics Data System (ADS)

Bobick, Sandra Burin

This study investigated the influence of learning style on concept acquisition within a sample of community college students in a general biology course. There are two subproblems within the larger problem: (1) the influence of demographic variables (age, gender, number of college credits, prior exposure to scientific information) on learning style, and (2) the correlations between prior scientific knowledge, learning style and student understanding of the concept of the gene. The sample included all students enrolled in an introductory general biology course during two consecutive semesters at an urban community college. Initial data was gathered during the first week of the semester, at which time students filled in a short questionnaire (age, gender, number of college credits, prior exposure to science information either through reading/visual sources or a prior biology course). Subjects were then given the Inventory of Learning Processes-Revised (ILP-R) which measures general preferences in five learning styles; Deep Learning; Elaborative Learning, Agentic Learning, Methodical Learning and Literal Memorization. Subjects were then given the Gene Conceptual Knowledge pretest: a 15 question objective section and an essay section. Subjects were exposed to specific concepts during lecture and laboratory exercises. At the last lab, students were given the Genetics Conceptual Knowledge Posttest. Pretest/posttest gains were correlated with demographic variables and learning styles were analyzed for significant correlations. Learning styles, as the independent variable in a simultaneous multiple regression, were significant predictors of results on the gene assessment tests, including pretest, posttest and gain. Of the learning styles, Deep Learning accounted for the greatest positive predictive value of pretest essay and pretest objective results. Literal Memorization was a significant negative predictor for posttest essay, essay gain and objective gain. Simultaneous multiple regression indicated that demographic variables were significant positive predictors for Methodical, Deep and Elaborative Learning Styles. Stepwise multiple regression resulted in number of credits, Read Science and gender (female) as significant predictors of learning styles. The findings of this study emphasize the importance of learning styles in conceptual understanding of the gene and the correlation of nonformal exposure to science information with learning style and conceptual understanding.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

A methodology for the design of experiments in computational intelligence with multiple regression models.

PubMed

Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

A methodology for the design of experiments in computational intelligence with multiple regression models

PubMed Central

Gestal, Marcos; Munteanu, Cristian R.; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable. PMID:27920952

Multi-model ensemble combinations of the water budget in the East/Japan Sea

NASA Astrophysics Data System (ADS)

HAN, S.; Hirose, N.; Usui, N.; Miyazawa, Y.

2016-02-01

The water balance of East/Japan Sea is determined mainly by inflow and outflow through the Korea/Tsushima, Tsugaru and Soya/La Perouse Straits. However, the volume transports measured at three straits remain quantitatively unbalanced. This study examined the seasonal variation of the volume transport using the multiple linear regression and ridge regression of multi-model ensemble (MME) methods to estimate physically consistent circulation in East/Japan Sea by using four different data assimilation models. The MME outperformed all of the single models by reducing uncertainties, especially the multicollinearity problem with the ridge regression. However, the regression constants turned out to be inconsistent with each other if the MME was applied separately for each strait. The MME for a connected system was thus performed to find common constants for these straits. The estimation of this MME was found to be similar to the MME result of sea level difference (SLD). The estimated mean transport (2.42 Sv) was smaller than the measurement data at the Korea/Tsushima Strait, but the calibrated transport of the Tsugaru Strait (1.63 Sv) was larger than the observed data. The MME results of transport and SLD also suggested that the standard deviation (STD) of the Korea/Tsushima Strait is larger than the STD of the observation, whereas the estimated results were almost identical to that observed for the Tsugaru and Soya/La Perouse Straits. The similarity between MME results enhances the reliability of the present MME estimation.

Multi-model ensemble estimation of volume transport through the straits of the East/Japan Sea

NASA Astrophysics Data System (ADS)

Han, Sooyeon; Hirose, Naoki; Usui, Norihisa; Miyazawa, Yasumasa

2016-01-01

The volume transports measured at the Korea/Tsushima, Tsugaru, and Soya/La Perouse Straits remain quantitatively inconsistent. However, data assimilation models at least provide a self-consistent budget despite subtle differences among the models. This study examined the seasonal variation of the volume transport using the multiple linear regression and ridge regression of multi-model ensemble (MME) methods to estimate more accurately transport at these straits by using four different data assimilation models. The MME outperformed all of the single models by reducing uncertainties, especially the multicollinearity problem with the ridge regression. However, the regression constants turned out to be inconsistent with each other if the MME was applied separately for each strait. The MME for a connected system was thus performed to find common constants for these straits. The estimation of this MME was found to be similar to the MME result of sea level difference (SLD). The estimated mean transport (2.43 Sv) was smaller than the measurement data at the Korea/Tsushima Strait, but the calibrated transport of the Tsugaru Strait (1.63 Sv) was larger than the observed data. The MME results of transport and SLD also suggested that the standard deviation (STD) of the Korea/Tsushima Strait is larger than the STD of the observation, whereas the estimated results were almost identical to that observed for the Tsugaru and Soya/La Perouse Straits. The similarity between MME results enhances the reliability of the present MME estimation.

Basic deprivation and involvement in risky sexual behaviour among out-of-school young people in a Lagos slum.

PubMed

Kunnuji, Michael

2014-01-01

Research has shown that in countries such as Nigeria many urban dwellers live in a state of squalour and lack the basic necessities of food, clothing and shelter. The present study set out to examine the association between forms of basic deprivation--such as food deprivation, high occupancy ratio as a form of shelter deprivation, and inadequate clothing--and two sexual outcomes--timing of onset of penetrative sex and involvement in multiple sexual partnerships. The study used survey data from a sample of 480 girls resident in Iwaya community. A survival analysis of the timing of onset of sex and a regression model for involvement in multiple sexual partnerships reveal that among the forms of deprivation explored, food deprivation is the only significant predictor of the timing of onset of sex and involvement in multiple sexual partnerships. The study concludes that the sexual activities of poor out-of-school girls are partly explained by their desire to overcome food deprivation and recommends that government and non-governmental-organisation programmes working with young people should address the problem of basic deprivation among adolescent girls.

Executive Functions Underlying Multiplicative Reasoning: Problem Type Matters

ERIC Educational Resources Information Center

Agostino, Alba; Johnson, Janice; Pascual-Leone, Juan

2010-01-01

We investigated the extent to which inhibition, updating, shifting, and mental-attentional capacity ("M"-capacity) contribute to children's ability to solve multiplication word problems. A total of 155 children in Grades 3-6 (8- to 13-year-olds) completed a set of multiplication word problems at two levels of difficulty: one-step and multiple-step…

The representation of multiplication and division facts in memory.

PubMed

De Brauwer, Jolien; Fias, Wim

2011-01-01

Recently, using a training paradigm, Campbell and Agnew (2009) observed cross-operation response time savings with nonidentical elements (e.g., practice 3 + 2, test 5 - 2) for addition and subtraction, showing that a single memory representation underlies addition and subtraction performance. Evidence for cross-operation savings between multiplication and division have been described frequently (e.g., Campbell, Fuchs-Lacelle, & Phenix, 2006) but they have always been attributed to a mediation strategy (reformulating a division problem as a multiplication problem, e.g., Campbell et al., 2006). Campbell and Agnew (2009) therefore concluded that there exists a fundamental difference between addition and subtraction on the one hand and multiplication and division on the other hand. However, our results suggest that retrieval savings between inverse multiplication and division problems can be observed. Even for small problems (solved by direct retrieval) practicing a division problem facilitated the corresponding multiplication problem and vice versa. These findings indicate that shared memory representations underlie multiplication and division retrieval. Hence, memory and learning processes do not seem to differ fundamentally between addition-subtraction and multiplication-division.

L2-norm multiple kernel learning and its application to biomedical data fusion

PubMed Central

2010-01-01

Background This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields different extensions of multiple kernel learning (MKL) such as L∞, L1, and L2 MKL. In particular, L2 MKL is a novel method that leads to non-sparse optimal kernel coefficients, which is different from the sparse kernel coefficients optimized by the existing L∞ MKL method. In real biomedical applications, L2 MKL may have more advantages over sparse integration method for thoroughly combining complementary information in heterogeneous data sources. Results We provide a theoretical analysis of the relationship between the L2 optimization of kernels in the dual problem with the L2 coefficient regularization in the primal problem. Understanding the dual L2 problem grants a unified view on MKL and enables us to extend the L2 method to a wide range of machine learning problems. We implement L2 MKL for ranking and classification problems and compare its performance with the sparse L∞ and the averaging L1 MKL methods. The experiments are carried out on six real biomedical data sets and two large scale UCI data sets. L2 MKL yields better performance on most of the benchmark data sets. In particular, we propose a novel L2 MKL least squares support vector machine (LSSVM) algorithm, which is shown to be an efficient and promising classifier for large scale data sets processing. Conclusions This paper extends the statistical framework of genomic data fusion based on MKL. Allowing non-sparse weights on the data sources is an attractive option in settings where we believe most data sources to be relevant to the problem at hand and want to avoid a "winner-takes-all" effect seen in L∞ MKL, which can be detrimental to the performance in prospective studies. The notion of optimizing L2 kernels can be straightforwardly extended to ranking, classification, regression, and clustering algorithms. To tackle the computational burden of MKL, this paper proposes several novel LSSVM based MKL algorithms. Systematic comparison on real data sets shows that LSSVM MKL has comparable performance as the conventional SVM MKL algorithms. Moreover, large scale numerical experiments indicate that when cast as semi-infinite programming, LSSVM MKL can be solved more efficiently than SVM MKL. Availability The MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/l2lssvm.html. PMID:20529363

An efficient method for generalized linear multiplicative programming problem with multiplicative constraints.

PubMed

Zhao, Yingfeng; Liu, Sanyang

2016-01-01

We present a practical branch and bound algorithm for globally solving generalized linear multiplicative programming problem with multiplicative constraints. To solve the problem, a relaxation programming problem which is equivalent to a linear programming is proposed by utilizing a new two-phase relaxation technique. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems. Global convergence has been proved and results of some sample examples and a small random experiment show that the proposed algorithm is feasible and efficient.

Determinants of quality of life in children with psychiatric disorders.

PubMed

Bastiaansen, Dennis; Koot, Hans M; Ferdinand, Robert F

2005-08-01

To assess factors that, in addition to childhood psychopathology, are associated with Quality of Life (QoL) in children with psychiatric problems. In a referred sample of 252 8 to 18-year-olds, information concerning QoL, psychopathology and a broad range of child, parent, and family/ social network factors was obtained from children, parents, teachers and clinicians. Poor child, parent, and clinician reported QoL was associated with child psychopathology, but given the presence of psychopathology, also with child factors, such as low self-esteem, and poor social skills, and family/social network factors, such as poor family functioning, and poor social support. In multiple linear regression analyses the importance of parent factors, such as parenting stress, was almost negligible. To increase QoL of children with psychiatric problems, treatment of symptoms is important, but outcome might improve if treatment is also focussed on other factors that may affect QoL. Results are discussed in relation to current treatment programs.

Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering.

PubMed

Gao, Shan; Guo, Guibing; Li, Runzhi; Wang, Zongmin

2017-01-01

Nowadays, providing high-quality recommendation services to users is an essential component in web applications, including shopping, making friends, and healthcare. This can be regarded either as a problem of estimating users' preference by exploiting explicit feedbacks (numerical ratings), or as a problem of collaborative ranking with implicit feedback (e.g., purchases, views, and clicks). Previous works for solving this issue include pointwise regression methods and pairwise ranking methods. The emerging healthcare websites and online medical databases impose a new challenge for medical service recommendation. In this paper, we develop a model, MBPR (Medical Bayesian Personalized Ranking over multiple users' actions), based on the simple observation that users tend to assign higher ranks to some kind of healthcare services that are meanwhile preferred in users' other actions. Experimental results on the real-world datasets demonstrate that MBPR achieves more accurate recommendations than several state-of-the-art methods and shows its generality and scalability via experiments on the datasets from one mobile shopping app.

Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering

PubMed Central

2017-01-01

Nowadays, providing high-quality recommendation services to users is an essential component in web applications, including shopping, making friends, and healthcare. This can be regarded either as a problem of estimating users' preference by exploiting explicit feedbacks (numerical ratings), or as a problem of collaborative ranking with implicit feedback (e.g., purchases, views, and clicks). Previous works for solving this issue include pointwise regression methods and pairwise ranking methods. The emerging healthcare websites and online medical databases impose a new challenge for medical service recommendation. In this paper, we develop a model, MBPR (Medical Bayesian Personalized Ranking over multiple users' actions), based on the simple observation that users tend to assign higher ranks to some kind of healthcare services that are meanwhile preferred in users' other actions. Experimental results on the real-world datasets demonstrate that MBPR achieves more accurate recommendations than several state-of-the-art methods and shows its generality and scalability via experiments on the datasets from one mobile shopping app. PMID:29118963

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836

Quantum annealing versus classical machine learning applied to a simplified computational biology problem

NASA Astrophysics Data System (ADS)

Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

2018-03-01

Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

Early risk factors for depressive symptoms among Korean adolescents: a 6-to-8 year follow-up study.

PubMed

Shin, Kyoung Min; Cho, Sun-Mi; Shin, Yun Mi; Park, Kyung Soon

2013-11-01

Depression during adolescence is critical to the individual's own development. Hence, identifying individuals with high-risk depression at an early stage is necessary. This study aimed to identify childhood emotional and behavioral risk factors related to depressive symptoms in Korean adolescents through a longitudinal study. The first survey took place from 1998 to 2000, and a follow-up assessment conducted in 2006, as the original participants reached 13-15 yr of age. The first assessment used the Korean version of Child Behavior Checklist and a general questionnaire on family structure, parental education, and economic status to evaluate the participants. The follow-up assessment administered the Korean Children's Depression Inventory. Multiple regression analysis revealed that childhood attention problems predicted depressive symptoms during adolescence for both boys and girls. For boys, family structure also predicted adolescent depressive symptoms. This study suggests that adolescents with attention problems during childhood are more likely to experience depressive symptoms.

Prevention of Incontinence Associated Skin Damage in Nursing Homes: Disparities and Predictors

PubMed Central

Bliss, Donna Z.; Gurvich, Olga V.; Mathiason, Michelle A.; Eberly, Lynn E.; Savik, Kay; Harms, Susan; Mueller, Christine; Wyman, Jean F.; Virnig, Beth

2016-01-01

Racial/ethnic disparities in preventing health problems have been reported in nursing homes. Incontinence is common among nursing home residents and can result in inflammatory-type skin damage, referred to as incontinence associated skin damage (IASD). Little is known about the prevention of IASD and whether there are racial/ethnic disparities in its prevention. This study assessed the proportion of older nursing home residents receiving IASD prevention after developing incontinence after admission (n=10,713) and whether there were racial/ethnic disparities in IASD prevention. Predictors of preventing IASD were also examined. Four national datasets provided potential predictors at multiple levels. Disparities were analyzed using the Peters-Belson method; predictors of preventing IASD were assessed using hierarchical logistic regression. Prevention of IASD was received by 0.12 of residents and no racial/ethnic disparities were found. Predictors of preventing IASD were primarily resident level factors including limitations in activities of daily living, poor nutrition, and more oxygenation problems. PMID:27586441

An application of Six Sigma methodology to turnover intentions in health care.

PubMed

Taner, Mehmet

2009-01-01

The purpose of this study is to show how the principles of Six Sigma can be applied to the high turnover problem of doctors in medical emergency services and paramedic backup. Six Sigma's define-measure-analyse-improve-control (DMAIC) is applied for reducing the turnover rate of doctors in an organisation operating in emergency services. Variables of the model are determined. Explanatory factor analysis, multiple regression, analysis of variance (ANOVA) and Gage R&R are employed for the analysis. Personal burnout/stress and dissatisfaction from salary were found to be the "vital few" variables. The organisation took a new approach by improving its initiatives to doctors' working conditions. Sigma level of the process is increased. New policy and process changes have been found to effectively decrease the incidence of turnover intentions. The improved process is gained, standardised and institutionalised. This study is one of the few papers in the literature that elaborates the turnover problem of doctors working in the emergency and paramedic backup services.

"Please Don't Make Me Ask for Help": Implicit Social Support and Mental Health in Chinese Individuals Living with HIV.

PubMed

Yang, Joyce P; Leu, Janxin; Simoni, Jane M; Chen, Wei Ti; Shiu, Cheng-Shi; Zhao, Hongxin

2015-08-01

China faces a growing HIV epidemic; psychosocial needs of HIV-positive individuals remain largely unaddressed. Research is needed to consider the gap between need for mental healthcare and lack of sufficiently trained professionals, in a culturally acceptable manner. This study assessed explicit and implicit forms of social support and mental health symptoms in 120 HIV-positive Chinese. Explicit social support refers to interactions involving active disclosure and discussion of problems and request for assistance, whereas implicit social support refers to the emotional comfort one obtains from social networks without disclosing problems. We hypothesized and found using multiple linear regression, that after controlling for demographics, only implicit, but not explicit social support positively predicted mental health. Future research is warranted on the effects of utilizing implicit social support to bolster mental health, which has the potential to circumvent the issues of both high stigma and low professional resources in this population.

Alcohol Availability and Intimate Partner Violence Among US Couples

PubMed Central

McKinney, Christy M.; Caetano, Raul; Harris, Theodore Robert; Ebama, Malembe S.

2008-01-01

Objectives We examined the relation between alcohol outlet density (the number of alcohol outlets per capita by zip code) and male-to-female partner violence (MFPV) or female-to-male partner violence (FMPV). We also investigated whether binge drinking or the presence of alcohol-related problems altered the relationship between alcohol outlet density and MFPV or FMPV. Methods We linked individual and couple sociodemographic and behavioral data from a 1995 national population-based sample of 1,597 couples to alcohol outlet data and 1990 US Census sociodemographic information. We used logistic regression for survey data to estimate unadjusted and adjusted odds ratios between alcohol outlet density and MFPV or FMPV along with 95% confidence intervals (CIs) and p-values. We used a design-based Wald test to derive a p-value for multiplicative interaction to assess the role of binge drinking and alcohol-related problems. Results In adjusted analysis, an increase of one alcohol outlet per 10,000 persons was associated with a 1.03-fold increased risk of MFPV (p-value for linear trend = 0.01) and a 1.011-fold increased risk of FMPV (p-value for linear trend = 0.48). An increase of 10 alcohol outlets per 10,000 persons was associated with 34% and 12% increased risk of MFPV and FMPV respectively, though the CI for the association with FMPV was compatible with no increased risk. The relationship between alcohol outlet density and MFPV was stronger among couples reporting alcohol-related problems than those reporting no problems (p-value for multiplicative interaction = 0.01). Conclusions We found that as alcohol outlet density increases so does the risk of MFPV and that this relationship may differ for couples who do and do not report alcohol-related problems. Given that MFPV accounts for the majority of injuries related to intimate partner violence, policy makers may wish to carefully consider the potential benefit of limiting alcohol outlet density to reduce MFPV and its adverse consequences. PMID:18976345

Alcohol availability and intimate partner violence among US couples.

PubMed

McKinney, Christy M; Caetano, Raul; Harris, Theodore Robert; Ebama, Malembe S

2009-01-01

We examined the relation between alcohol outlet density (the number of alcohol outlets per capita by zip code) and male-to-female partner violence (MFPV) or female-to-male partner violence (FMPV). We also investigated whether binge drinking or the presence of alcohol-related problems altered the relationship between alcohol outlet density and MFPV or FMPV. We linked individual and couple sociodemographic and behavioral data from a 1995 national population-based sample of 1,597 couples to alcohol outlet data and 1990 US Census sociodemographic information. We used logistic regression for survey data to estimate unadjusted and adjusted odds ratios between alcohol outlet density and MFPV or FMPV along with 95% confidence intervals (CIs) and p-values. We used a design-based Wald test to derive a p-value for multiplicative interaction to assess the role of binge drinking and alcohol-related problems. In adjusted analysis, an increase of one alcohol outlet per 10,000 persons was associated with a 1.03-fold increased risk of MFPV (p-value for linear trend = 0.01) and a 1.011-fold increased risk of FMPV (p-value for linear trend = 0.48). An increase of 10 alcohol outlets per 10,000 persons was associated with 34% and 12% increased risk of MFPV and FMPV respectively, though the CI for the association with FMPV was compatible with no increased risk. The relationship between alcohol outlet density and MFPV was stronger among couples reporting alcohol-related problems than those reporting no problems (p-value for multiplicative interaction = 0.01). We found that as alcohol outlet density increases so does the risk of MFPV and that this relationship may differ for couples who do and do not report alcohol-related problems. Given that MFPV accounts for the majority of injuries related to intimate partner violence, policy makers may wish to carefully consider the potential benefit of limiting alcohol outlet density to reduce MFPV and its adverse consequences.

Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

2011-11-01

SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

Alcohol-related problems and life satisfaction predict motivation to change among mandated college students.

PubMed

Diulio, Andrea R; Cero, Ian; Witte, Tracy K; Correia, Christopher J

2014-04-01

The present study investigated the role specific types of alcohol-related problems and life satisfaction play in predicting motivation to change alcohol use. Participants were 548 college students mandated to complete a brief intervention following an alcohol-related policy violation. Using hierarchical multiple regression, we tested for the presence of interaction and quadratic effects on baseline data collected prior to the intervention. A significant interaction indicated that the relationship between a respondent's personal consequences and his/her motivation to change differs depending upon the level of concurrent social consequences. Additionally quadratic effects for abuse/dependence symptoms and life satisfaction were found. The quadratic probes suggest that abuse/dependence symptoms and poor life satisfaction are both positively associated with motivation to change for a majority of the sample; however, the nature of these relationships changes for participants with more extreme scores. Results support the utility of using a multidimensional measure of alcohol related problems and assessing non-linear relationships when assessing predictors of motivation to change. The results also suggest that the best strategies for increasing motivation may vary depending on the types of alcohol-related problems and level of life satisfaction the student is experiencing and highlight potential directions for future research. Copyright © 2014. Published by Elsevier Ltd.

Psychometric properties of the Revised Chen Internet Addiction Scale (CIAS-R) in Chinese adolescents.

PubMed

Mak, Kwok-Kei; Lai, Ching-Man; Ko, Chih-Hung; Chou, Chien; Kim, Dong-Il; Watanabe, Hiroko; Ho, Roger C M

2014-10-01

The Revised Chen Internet Addiction Scale (CIAS-R) was developed to assess Internet addiction in Chinese populations, but its psychometric properties in adolescents have not been examined. This study aimed to evaluate the factor structure and psychometric properties of CIAS-R in Hong Kong Chinese adolescents. 860 Grade 7 to 13 students (38 % boys) completed the CIAS-R, the Young's Internet Addiction Test (IAT), and the Health of the Nation Outcome Scales for Children and Adolescents (HoNOSCA) in a survey. The prevalence of Internet addiction as assessed by CIAS-R was 18 %. High internal consistency and inter-item correlations were reported for the CIAS-R. Results from the confirmatory factor analysis suggested a four-factor structure of Compulsive Use and Withdrawal, Tolerance, Interpersonal and Health-related Problems, and Time Management Problems. Moreover, results of hierarchical multiple regression supported the incremental validity of the CIAS-R to predict mental health outcomes beyond the effects of demographic differences and self-reported time spent online. The CIAS is a reliable and valid measure of internet addiction problems in Hong Kong adolescents. Future study is warranted to validate the cutoffs of the CIAS-R for identification of adolescents with Internet use problems who may have mental health needs.

Emotional distress and burden among caregivers of children with oncological/hematological disorders.

PubMed

Edmond, Sara N; Graves, Patricia E; Whiting, Sara E; Karlson, Cynthia W

2016-06-01

Caring for children with oncological and hematological disorders may lead to caregiver emotional distress and caregiver burden; however, little work has examined the relationship between children's symptoms and caregiver's distress and burden. This study used self-report survey data from caregivers (N = 96) and a cross-sectional design to examine correlates of caregiver emotional distress and burden. Data collected included caregiver and child demographic data, child symptoms (i.e., sleep problems, pain, and emotional/behavioral problems), caregiver emotional distress, and caregiver burden. Multiple linear regression found that parent reported financial difficulty (β = 0.29, t = 3.13, p = .003), greater child sleep problems (β = 0.29 t = 2.81, p = .007), greater child pain (β = 0.33 t = 3.48, p = .001), and greater child emotional/behavioral problems (β = 0.27, t = 2.71, p = .009) were all related to higher levels of caregiver emotional distress. Only financial difficulties (β = -0.35, t = -2.03, p = .04) and child pain (β = -0.30, t = -2.33, p = .02) were related to caregiver burden. Child symptoms may play an important role in the development of caregiver distress and caregiver burden; future research should utilize longitudinal designs to examine temporal and casual relationships. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Feedback mechanisms of change: How problem alerts reported by youth clients and their caregivers impact clinician-reported session content

PubMed Central

Douglas, Susan R.; Jonghyuk, Bae; de Andrade, Ana Regina Vides; Tomlinson, M. Michele; Hargraves, Ryan Pamela; Bickman, Leonard

2015-01-01

Objective This study explored how clinician-reported content addressed in treatment sessions was predicted by clinician feedback group and multi-informant cumulative problem alerts that appeared in computerized feedback reports for 299 clients aged 11 to 18 years receiving home-based community mental health treatment. Method Measures included a clinician-report of content addressed in sessions and additional measures of treatment progress and process (e.g., therapeutic alliance) completed by clinicians, clients, and their caregivers. Item responses in the top 25th percentile in severity from these measures appeared as ‘problem alerts’ on corresponding computerized feedback reports. Clinicians randomized to the feedback group received feedback weekly while the control group did not. Analyses were conducted using the Cox proportional hazards regression for recurrent events. Results For all content domains, the results of the survival analyses indicated a robust effect of the feedback group on addressing specific content in sessions, with feedback associated with shorter duration to first occurrence and increased likelihood of addressing or focusing on a topic compared to the non-feedback group. Conclusion There appears to be an important relationship between feedback and cumulative problem alerts reported by multiple informants as they influence session content. PMID:26337327

Weather Impact on Airport Arrival Meter Fix Throughput

NASA Technical Reports Server (NTRS)

Wang, Yao

2017-01-01

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Practical Guidance for Conducting Mediation Analysis With Multiple Mediators Using Inverse Odds Ratio Weighting

PubMed Central

Nguyen, Quynh C.; Osypuk, Theresa L.; Schmidt, Nicole M.; Glymour, M. Maria; Tchetgen Tchetgen, Eric J.

2015-01-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994–2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. PMID:25693776