Artificial neural networks environmental forecasting in comparison with multiple linear regression technique: From heavy metals to organic micropollutants screening in agricultural soils

NASA Astrophysics Data System (ADS)

Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea

2016-12-01

The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.

Negative Peer Relationships on Piracy Behavior: A Cross-Sectional Study of the Associations between Cyberbullying Involvement and Digital Piracy.

PubMed

Yubero, Santiago; Larrañaga, Elisa; Villora, Beatriz; Navarro, Raúl

2017-10-05

The present study examines the relationship between different roles in cyberbullying behaviors (cyberbullies, cybervictims, cyberbullies-victims, and uninvolved) and self-reported digital piracy. In a region of central Spain, 643 (49.3% females, 50.7% males) students (grades 7-10) completed a number of self-reported measures, including cyberbullying victimization and perpetration, self-reported digital piracy, ethical considerations of digital piracy, time spent on the Internet, and leisure activities related with digital content. The results of a series of hierarchical multiple regression models for the whole sample indicate that cyberbullies and cyberbullies-victims are associated with more reports of digital piracy. Subsequent hierarchical multiple regression analyses, done separately for males and females, indicate that the relationship between cyberbullying and self-reported digital piracy is sustained only for males. The ANCOVA analysis show that, after controlling for gender, self-reported digital piracy and time spent on the Internet, cyberbullies and cyberbullies-victims believe that digital piracy is a more ethically and morally acceptable behavior than victims and uninvolved adolescents believe. The results provide insight into the association between two deviant behaviors.

Association between previous spontaneous abortion and pre-eclampsia during a subsequent pregnancy.

PubMed

Sepidarkish, Mahdi; Almasi-Hashiani, Amir; Maroufizadeh, Saman; Vesali, Samira; Pirjani, Reihaneh; Samani, Reza O

2017-01-01

To determine the impact of a history of spontaneous abortion on pre-eclampsia during a subsequent pregnancy. A cross-sectional study enrolled pregnant women admitted to obstetrics and gynecology wards at 103 hospitals in Tehran, Iran for delivery between July 6 and July 21, 2015. Consenting participants were interviewed by midwives; data were collected using a five-part questionnaire and patients' medical records were retrieved. Patient data were analyzed by multiple logistic regression to identify variables associated with increased odds of pre-eclampsia. In total, 5170 patients were interviewed and 252 had experienced pre-eclampsia. The number of previous spontaneous abortions was found to be associated with pre-eclampsia, and a higher number of previous spontaneous abortions was associated with increased odds of patients having experienced pre-eclampsia (adjusted odds ratio 1.28, 95% confidence interval 1.03-1.59; P=0.025). A history of spontaneous abortion was associated with increased odds of pre-eclampsia during a subsequent pregnancy. © 2016 International Federation of Gynecology and Obstetrics.

A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance

PubMed Central

2014-01-01

Background Greater use of antibiotics during the past 50 years has exerted selective pressure on susceptible bacteria and may have favoured the survival of resistant strains. Existing information on antibiotic resistance patterns from pathogens circulating among community-based patients is substantially less than from hospitalized patients on whom guidelines are often based. We therefore chose to assess the relationship between the antibiotic resistance pattern of bacteria circulating in the community and the consumption of antibiotics in the community. Methods Both gray literature and published scientific literature in English and other European languages was examined. Multiple regression analysis was used to analyse whether studies found a positive relationship between antibiotic consumption and resistance. A subsequent meta-analysis and meta-regression was conducted for studies for which a common effect size measure (odds ratio) could be calculated. Results Electronic searches identified 974 studies but only 243 studies were considered eligible for inclusion by the two independent reviewers who extracted the data. A binomial test revealed a positive relationship between antibiotic consumption and resistance (p < .001) but multiple regression modelling did not produce any significant predictors of study outcome. The meta-analysis generated a significant pooled odds ratio of 2.3 (95% confidence interval 2.2 to 2.5) with a meta-regression producing several significant predictors (F(10,77) = 5.82, p < .01). Countries in southern Europe produced a stronger link between consumption and resistance than other regions. Conclusions Using a large set of studies we found that antibiotic consumption is associated with the development of antibiotic resistance. A subsequent meta-analysis, with a subsample of the studies, generated several significant predictors. Countries in southern Europe produced a stronger link between consumption and resistance than other regions so efforts at reducing antibiotic consumption may need to be strengthened in this area. Increased consumption of antibiotics may not only produce greater resistance at the individual patient level but may also produce greater resistance at the community, country, and regional levels, which can harm individual patients. PMID:24405683

Above-ground biomass of mangrove species. I. Analysis of models

NASA Astrophysics Data System (ADS)

Soares, Mário Luiz Gomes; Schaeffer-Novelli, Yara

2005-10-01

This study analyzes the above-ground biomass of Rhizophora mangle and Laguncularia racemosa located in the mangroves of Bertioga (SP) and Guaratiba (RJ), Southeast Brazil. Its purpose is to determine the best regression model to estimate the total above-ground biomass and compartment (leaves, reproductive parts, twigs, branches, trunk and prop roots) biomass, indirectly. To do this, we used structural measurements such as height, diameter at breast-height (DBH), and crown area. A combination of regression types with several compositions of independent variables generated 2.272 models that were later tested. Subsequent analysis of the models indicated that the biomass of reproductive parts, branches, and prop roots yielded great variability, probably because of environmental factors and seasonality (in the case of reproductive parts). It also indicated the superiority of multiple regression to estimate above-ground biomass as it allows researchers to consider several aspects that affect above-ground biomass, specially the influence of environmental factors. This fact has been attested to the models that estimated the biomass of crown compartments.

Use of factor scores for predicting body weight from linear body measurements in three South African indigenous chicken breeds.

PubMed

Malomane, Dorcus Kholofelo; Norris, David; Banga, Cuthbert B; Ngambi, Jones W

2014-02-01

Body weight and weight of body parts are of economic importance. It is difficult to directly predict body weight from highly correlated morphological traits through multiple regression. Factor analysis was carried out to examine the relationship between body weight and five linear body measurements (body length, body girth, wing length, shank thickness, and shank length) in South African Venda (VN), Naked neck (NN), and Potchefstroom koekoek (PK) indigenous chicken breeds, with a view to identify those factors that define body conformation. Multiple regression was subsequently performed to predict body weight, using orthogonal traits derived from the factor analysis. Measurements were obtained from 210 chickens, 22 weeks of age, 70 chickens per breed. High correlations were obtained between body weight and all body measurements except for wing length in PK. Two factors extracted after varimax rotation explained 91, 95, and 83% of total variation in VN, NN, and PK, respectively. Factor 1 explained 73, 90, and 64% in VN, NN, and PK, respectively, and was loaded on all body measurements except for wing length in VN and PK. In a multiple regression, these two factors accounted for 72% variation in body weight in VN, while only factor 1 accounted for 83 and 74% variation in body weight in NN and PK, respectively. The two factors could be used to define body size and conformation of these breeds. Factor 1 could predict body weight in all three breeds. Body measurements can be better selected jointly to improve body weight in these breeds.

Inhibitory saccadic dysfunction is associated with cerebellar injury in multiple sclerosis.

PubMed

Kolbe, Scott C; Kilpatrick, Trevor J; Mitchell, Peter J; White, Owen; Egan, Gary F; Fielding, Joanne

2014-05-01

Cognitive dysfunction is common in patients with multiple sclerosis (MS). Saccadic eye movement paradigms such as antisaccades (AS) can sensitively interrogate cognitive function, in particular, the executive and attentional processes of response selection and inhibition. Although we have previously demonstrated significant deficits in the generation of AS in MS patients, the neuropathological changes underlying these deficits were not elucidated. In this study, 24 patients with relapsing-remitting MS underwent testing using an AS paradigm. Rank correlation and multiple regression analyses were subsequently used to determine whether AS errors in these patients were associated with: (i) neurological and radiological abnormalities, as measured by standard clinical techniques, (ii) cognitive dysfunction, and (iii) regionally specific cerebral white and gray-matter damage. Although AS error rates in MS patients did not correlate with clinical disability (using the Expanded Disability Status Score), T2 lesion load or brain parenchymal fraction, AS error rate did correlate with performance on the Paced Auditory Serial Addition Task and the Symbol Digit Modalities Test, neuropsychological tests commonly used in MS. Further, voxel-wise regression analyses revealed associations between AS errors and reduced fractional anisotropy throughout most of the cerebellum, and increased mean diffusivity in the cerebellar vermis. Region-wise regression analyses confirmed that AS errors also correlated with gray-matter atrophy in the cerebellum right VI subregion. These results support the use of the AS paradigm as a marker for cognitive dysfunction in MS and implicate structural and microstructural changes to the cerebellum as a contributing mechanism for AS deficits in these patients. Copyright © 2013 Wiley Periodicals, Inc.

Breakfast intake among adults with type 2 diabetes: is bigger better?

PubMed Central

Jarvandi, Soghra; Schootman, Mario; Racette, Susan B.

2015-01-01

Objective To assess the association between breakfast energy and total daily energy intake among individuals with type 2 diabetes. Design Cross-sectional study. Daily energy intake was computed from a 24-h dietary recall. Multiple regression models were used to estimate the association between daily energy intake (dependent variable) and quartiles of energy intake at breakfast (independent variable) expressed as either absolute or relative (% of total daily energy intake) terms. Orthogonal polynomial contrasts were used to test for linear and quadratic trends. Models were controlled for sex, age, race/ethnicity, body mass index, physical activity and smoking. In addition, we used separate multiple regression models to test the effect of quartiles of absolute and relative breakfast energy on intake at lunch, dinner, and snacks. Setting The 1999–2004 National Health and Nutrition Examination Survey (NHANES). Subjects Participants aged ≥ 30 years with self-reported history of diabetes (N = 1,146). Results Daily energy intake increased as absolute breakfast energy intake increased (linear trend, P < 0.0001; quadratic trend, P = 0.02), but decreased as relative breakfast energy intake increased (linear trend, P < 0.0001). In addition, while higher quartiles of absolute breakfast intake had no associations with energy intake at subsequent meals, higher quartiles of relative breakfast intake were associated with lower energy intake during all subsequent meals and snacks (P < 0.05). Conclusions Consuming a breakfast that provided less energy or comprised a greater proportion of daily energy intake was associated with lower total daily energy intake in adults with type 2 diabetes. PMID:25529061

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

PubMed

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

2013-06-01

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

Factors Associated With Surgery Clerkship Performance and Subsequent USMLE Step Scores.

PubMed

Dong, Ting; Copeland, Annesley; Gangidine, Matthew; Schreiber-Gregory, Deanna; Ritter, E Matthew; Durning, Steven J

2018-03-12

We conducted an in-depth empirical investigation to achieve a better understanding of the surgery clerkship from multiple perspectives, including the influence of clerkship sequence on performance, the relationship between self-logged work hours and performance, as well as the association between surgery clerkship performance with subsequent USMLE Step exams' scores. The study cohort consisted of medical students graduating between 2015 and 2018 (n = 687). The primary measures of interest were clerkship sequence (internal medicine clerkship before or after surgery clerkship), self-logged work hours during surgery clerkship, surgery NBME subject exam score, surgery clerkship overall grade, and Step 1, Step 2 CK, and Step 3 exam scores. We reported the descriptive statistics and conducted correlation analysis, stepwise linear regression analysis, and variable selection analysis of logistic regression to answer the research questions. Students who completed internal medicine clerkship prior to surgery clerkship had better performance on surgery subject exam. The subject exam score explained an additional 28% of the variance of the Step 2 CK score, and the clerkship overall score accounted for an additional 24% of the variance after the MCAT scores and undergraduate GPA were controlled. Our finding suggests that the clerkship sequence does matter when it comes to performance on the surgery NBME subject exam. Performance on the surgery subject exam is predictive of subsequent performance on future USMLE Step exams. Copyright © 2018 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Negative Peer Relationships on Piracy Behavior: A Cross-Sectional Study of the Associations between Cyberbullying Involvement and Digital Piracy

PubMed Central

Yubero, Santiago; Larrañaga, Elisa; Villora, Beatriz

2017-01-01

The present study examines the relationship between different roles in cyberbullying behaviors (cyberbullies, cybervictims, cyberbullies-victims, and uninvolved) and self-reported digital piracy. In a region of central Spain, 643 (49.3% females, 50.7% males) students (grades 7–10) completed a number of self-reported measures, including cyberbullying victimization and perpetration, self-reported digital piracy, ethical considerations of digital piracy, time spent on the Internet, and leisure activities related with digital content. The results of a series of hierarchical multiple regression models for the whole sample indicate that cyberbullies and cyberbullies-victims are associated with more reports of digital piracy. Subsequent hierarchical multiple regression analyses, done separately for males and females, indicate that the relationship between cyberbullying and self-reported digital piracy is sustained only for males. The ANCOVA analysis show that, after controlling for gender, self-reported digital piracy and time spent on the Internet, cyberbullies and cyberbullies-victims believe that digital piracy is a more ethically and morally acceptable behavior than victims and uninvolved adolescents believe. The results provide insight into the association between two deviant behaviors. PMID:28981466

The impact of green stormwater infrastructure installation on surrounding health and safety.

PubMed

Kondo, Michelle C; Low, Sarah C; Henning, Jason; Branas, Charles C

2015-03-01

We investigated the health and safety effects of urban green stormwater infrastructure (GSI) installments. We conducted a difference-in-differences analysis of the effects of GSI installments on health (e.g., blood pressure, cholesterol and stress levels) and safety (e.g., felonies, nuisance and property crimes, narcotics crimes) outcomes from 2000 to 2012 in Philadelphia, Pennsylvania. We used mixed-effects regression models to compare differences in pre- and posttreatment measures of outcomes for treatment sites (n=52) and randomly chosen, matched control sites (n=186) within multiple geographic extents surrounding GSI sites. Regression-adjusted models showed consistent and statistically significant reductions in narcotics possession (18%-27% less) within 16th-mile, quarter-mile, half-mile (P<.001), and eighth-mile (P<.01) distances from treatment sites and at the census tract level (P<.01). Narcotics manufacture and burglaries were also significantly reduced at multiple scales. Nonsignificant reductions in homicides, assaults, thefts, public drunkenness, and narcotics sales were associated with GSI installation in at least 1 geographic extent. Health and safety considerations should be included in future assessments of GSI programs. Subsequent studies should assess mechanisms of this association.

The Impact of Green Stormwater Infrastructure Installation on Surrounding Health and Safety

PubMed Central

Low, Sarah C.; Henning, Jason; Branas, Charles C.

2015-01-01

Objectives. We investigated the health and safety effects of urban green stormwater infrastructure (GSI) installments. Methods. We conducted a difference-in-differences analysis of the effects of GSI installments on health (e.g., blood pressure, cholesterol and stress levels) and safety (e.g., felonies, nuisance and property crimes, narcotics crimes) outcomes from 2000 to 2012 in Philadelphia, Pennsylvania. We used mixed-effects regression models to compare differences in pre- and posttreatment measures of outcomes for treatment sites (n = 52) and randomly chosen, matched control sites (n = 186) within multiple geographic extents surrounding GSI sites. Results. Regression-adjusted models showed consistent and statistically significant reductions in narcotics possession (18%–27% less) within 16th-mile, quarter-mile, half-mile (P < .001), and eighth-mile (P < .01) distances from treatment sites and at the census tract level (P < .01). Narcotics manufacture and burglaries were also significantly reduced at multiple scales. Nonsignificant reductions in homicides, assaults, thefts, public drunkenness, and narcotics sales were associated with GSI installation in at least 1 geographic extent. Conclusions. Health and safety considerations should be included in future assessments of GSI programs. Subsequent studies should assess mechanisms of this association. PMID:25602887

Microbiology of Peritonitis in Peritoneal Dialysis Patients with Multiple Episodes

PubMed Central

Nessim, Sharon J.; Nisenbaum, Rosane; Bargman, Joanne M.; Jassal, Sarbjit V.

2012-01-01

♦ Background: Peritoneal dialysis (PD)–associated peritonitis clusters within patients. Patient factors contribute to peritonitis risk, but there is also entrapment of organisms within the biofilm that forms on PD catheters. It is hypothesized that this biofilm may prevent complete eradication of organisms, predisposing to multiple infections with the same organism. ♦ Methods: Using data collected in the Canadian multicenter Baxter POET (Peritonitis, Organism, Exit sites, Tunnel infections) database from 1996 to 2005, we studied incident PD patients with 2 or more peritonitis episodes. We determined the proportion of patients with 2 or more episodes caused by the same organism. In addition, using a multivariate logistic regression model, we tested whether prior peritonitis with a given organism predicted the occurrence of a subsequent episode with the same organism. ♦ Results: During their time on PD, 558 patients experienced 2 or more peritonitis episodes. Of those 558 patients, 181 (32%) had at least 2 episodes with the same organism. The organism most commonly causing repeat infection was coagulase-negative Staphylococcus (CNS), accounting for 65.7% of cases. Compared with peritonitis caused by other organisms, a first CNS peritonitis episode was associated with an increased risk of subsequent CNS peritonitis within 1 year (odds ratio: 2.1; 95% confidence interval: 1.5 to 2.8; p < 0.001). Among patients with repeat CNS peritonitis, 48% of repeat episodes occurred within 6 months of the earlier episode. ♦ Conclusions: In contrast to previous data, we did not find a high proportion of patients with multiple peritonitis episodes caused by the same organism. Coagulase-negative Staphylococcus was the organism most likely to cause peritonitis more than once in a given patient, and a prior CNS peritonitis was associated with an increased risk of CNS peritonitis within the subsequent year. PMID:22215659

Attendance rates in a workplace predict subsequent outcome of employment-based reinforcement of cocaine abstinence in methadone patients.

PubMed

Donlin, Wendy D; Knealing, Todd W; Needham, Mick; Wong, Conrad J; Silverman, Kenneth

2008-01-01

This study assessed whether attendance rates in a workplace predicted subsequent outcome of employment-based reinforcement of cocaine abstinence. Unemployed adults in Baltimore methadone programs who used cocaine (N=111) could work in a workplace for 4 hr every weekday and earn $10.00 per hour in vouchers for 26 weeks. During an induction period, participants provided urine samples but could work independent of their urinalysis results. After the induction period, participants had to provide urinalysis evidence of cocaine abstinence to work and maintain maximum pay. A multiple regression analysis showed that induction period attendance was independently associated with urinalysis evidence of cocaine abstinence under the employment-based abstinence reinforcement contingency. Induction period attendance may measure the reinforcing value of employment and could be used to guide the improvement of employment-based abstinence reinforcement.

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.

Ubiquitin-Fused and/or Multiple Early Genes from Cottontail Rabbit Papillomavirus as DNA Vaccines

PubMed Central

Leachman, Sancy A.; Shylankevich, Mark; Slade, Martin D.; Levine, Dana; K. Sundaram, Ranjini; Xiao, Wei; Bryan, Marianne; Zelterman, Daniel; Tiegelaar, Robert E.; Brandsma, Janet L.

2002-01-01

Human papillomavirus (HPV) vaccines have the potential to prevent cervical cancer by preventing HPV infection or treating premalignant disease. We previously showed that DNA vaccination with the cottontail rabbit papillomavirus (CRPV) E6 gene induced partial protection against CRPV challenge and that the vaccine's effects were greatly enhanced by priming with granulocyte-macrophage colony-stimulating factor (GM-CSF). In the present study, two additional strategies for augmenting the clinical efficacy of CRPV E6 vaccination were evaluated. The first was to fuse a ubiquitin monomer to the CRPV E6 protein to enhance antigen processing and presentation through the major histocompatibility complex class I pathway. Rabbits vaccinated with the wild-type E6 gene plus GM-CSF or with the ubiquitin-fused E6 gene formed significantly fewer papillomas than the controls. The papillomas also required a longer time to appear and grew more slowly. Finally, a significant proportion of the papillomas subsequently regressed. The ubiquitin-fused E6 vaccine was significantly more effective than the wild-type E6 vaccine plus GM-CSF priming. The second strategy was to vaccinate with multiple CRPV early genes to increase the breadth of the CRPV-specific response. DNA vaccines encoding the wild-type CRPV E1-E2, E6, or E7 protein were tested alone and in all possible combinations. All vaccines and combinations suppressed papilloma formation, slowed papilloma growth, and stimulated subsequent papilloma regression. Finally, the two strategies were merged and a combination DNA vaccine containing ubiquitin-fused versions of the CRPV E1, E2, and E7 genes was tested. This last vaccine prevented papilloma formation at all challenge sites in all rabbits, demonstrating complete protection. PMID:12097575

Analysis of phase II studies on targeted agents and subsequent phase III trials: what are the predictors for success?

PubMed

Chan, John K; Ueda, Stefanie M; Sugiyama, Valerie E; Stave, Christopher D; Shin, Jacob Y; Monk, Bradley J; Sikic, Branimir I; Osann, Kathryn; Kapp, Daniel S

2008-03-20

To identify the characteristics of phase II studies that predict for subsequent "positive" phase III trials (those that reached the proposed primary end points of study or those wherein the study drug was superior to the standard regimen investigating targeted agents in advanced tumors. We identified all phase III clinical trials of targeted therapies against advanced cancers published from 1985 to 2005. Characteristics of the preceding phase II studies were reviewed to identify predictive factors for success of the subsequent phase III trial. Data were analyzed using the chi(2) test and logistic regression models. Of 351 phase II studies, 167 (47.6%) subsequent phase III trials were positive and 184 (52.4%) negative. Phase II studies from multiple rather than single institutions were more likely to precede a successful trial (60.4% v 39.4%; P < .001). Positive phase II results were more likely to lead to a successful phase III trial (50.8% v 22.5%; P = .003). The percentage of successful trials from pharmaceutical companies was significantly higher compared with academic, cooperative groups, and research institutes (89.5% v 44.2%, 45.2%, and 46.3%, respectively; P = .002). On multivariate analysis, these factors and shorter time interval between publication of phase II results and III study publication were independent predictive factors for a positive phase III trial. In phase II studies of targeted agents, multiple- versus single-institution participation, positive phase II trial, pharmaceutical company-based trials, and shorter time period between publication of phase II to phase III trial were independent predictive factors of success in a phase III trial. Investigators should be cognizant of these factors in phase II studies before designing phase III trials.

Self-rated health is associated with subsequent functional decline among older adults in Japan.

PubMed

Hirosaki, Mayumi; Okumiya, Kiyohito; Wada, Taizo; Ishine, Masayuki; Sakamoto, Ryota; Ishimoto, Yasuko; Kasahara, Yoriko; Kimura, Yumi; Fukutomi, Eriko; Chen, Wen Ling; Nakatsuka, Masahiro; Fujisawa, Michiko; Otsuka, Kuniaki; Matsubayashi, Kozo

2017-09-01

Previous studies have reported that self-rated health (SRH) predicts subsequent mortality. However, less is known about the association between SRH and functional ability. The aim of this study was to examine whether SRH predicts decline in basic activities of daily living (ADL), even after adjustment for depression, among community-dwelling older adults in Japan. A three-year prospective cohort study was conducted among 654 residents aged 65 years and older without disability in performing basic ADL at baseline. SRH was assessed using a visual analogue scale (range; 0-100), and dichotomized into low and high groups. Information on functional ability, sociodemographic factors, depressive symptoms, and medical conditions were obtained using a self-administered questionnaire. Logistic regression analysis was used to examine the association between baseline SRH and functional decline three years later. One hundred and eight (16.5%) participants reported a decline in basic ADL at the three-year follow-up. Multiple logistic regression analysis showed that the low SRH group had a higher risk for functional decline compared to the high SRH group, even after controlling for potential confounding factors (odds ratio (OR) = 2.4; 95% confidence interval (CI) = 1.3-4.4). Furthermore, a 10-point difference in SRH score was associated with subsequent functional decline (OR = 1.37; 95% CI = 1.16-1.61). SRH was an independent predictor of functional decline. SRH could be a simple assessment tool for predicting the loss or maintenance of functional ability in community-dwelling older adults. Positive self-evaluation might be useful to maintain an active lifestyle and stay healthy.

Catatonic Stupor in Schizophrenic Disorders and Subsequent Medical Complications and Mortality.

PubMed

Funayama, Michitaka; Takata, Taketo; Koreki, Akihiro; Ogino, Satoyuki; Mimura, Masaru

2018-05-01

Although catatonia can occur secondary to a general medical condition, catatonia itself has been known to lead to various medical compolications. Although case reports on the association of catatonia with subsequent medical complications have been documented, no comprehensive large-scale study has been performed. To investigate specific medical complications after catatonia, we conducted a retrospective cohort study of specific medical complications of schizophrenia patients with catatonia. The 1719 schizophrenia inpatients in our study were categorized into two groups: the catatonia group, i.e., those who exhibited catatonic stupor while they were hospitalized, and the noncatatonia group, i.e., those who never exhibited catatonic stupor. Differences between the two groups in the occurrence of subsequent medical complications were examined using linear and logistic regression analyses, and models were adjusted for potentially confounding factors. The catatonia group had an increased risk for mortality (odds ratio = 4.8, 95% confidence interval = 2.0-10.6, p < .01) and certain specific medical complications, i.e., pneumonia, urinary tract infection, sepsis, disseminated intravascular coagulation, rhabdomyolysis, dehydration, deep venous thrombosis, pulmonary embolism, urinary retention, decubitus, arrhythmia, renal failure, neuroleptic malignant syndrome, hypernatremia, and liver dysfunction (all p values < .01, except for deep venous thrombosis, p = .04 in the multiple linear regression analysis). Catatonic stupor in schizophrenia substantially raises the risk for specific medical complications and mortality. Hyperactivity of the sympathetic nervous system, dehydration, and immobility, which are frequently involved in catatonia, might contribute to these specific medical complications. In catatonia, meticulous care for both mental and medical conditions should be taken to reduce the risk of adverse medical consequences.

Do somatic complaints predict subsequent symptoms of depression?

PubMed

Terre, Lisa; Poston, Walker S Carlos; Foreyt, John; St Jeor, Sachiko T

2003-01-01

Evidence suggests substantial comorbidity between symptoms of somatization and depression in clinical as well as nonclinical populations. However, as most existing research has been retrospective or cross-sectional in design, very little is known about the specific nature of this relationship. In particular, it is unclear whether somatic complaints may heighten the risk for the subsequent development of depressive symptoms. We report findings on the link between symptoms of somatization (assessed using the SCL-90-R) and depression 5 years later (assessed using the CES-D) in an initially healthy cohort of community adults, based on prospective data from the RENO Diet-Heart Study. Gender-stratified multiple regression analyses revealed that baseline CES-D scores were the best predictors of subsequent depressive symptoms for men and women. Baseline scores on the SCL-90-R somatization subscale significantly predicted subsequent self-reported symptoms of depressed mood 5 years later, but only in women. However, somatic complaints were a somewhat less powerful predictor than income and age. Our findings suggest that somatic complaints may represent one, but not necessarily the most important, risk factor for the subsequent development of depressive symptoms in women in nonclinical populations. The results also highlight the importance of including social variables in studies on women's depression as well as conducting additional research to further examine predictors of depressive symptoms in men. Copyright 2003 S. Karger AG, Basel

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 Detection and Interpretation of Interaction Effects between Continuous Variables in Multiple Regression.

ERIC Educational Resources Information Center

Jaccard, James; And Others

1990-01-01

Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)

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.

Association between the Type of Workplace and Lung Function in Copper Miners

PubMed Central

Gruszczyński, Leszek; Wojakowska, Anna; Ścieszka, Marek; Turczyn, Barbara; Schmidt, Edward

2016-01-01

The aim of the analysis was to retrospectively assess changes in lung function in copper miners depending on the type of workplace. In the groups of 225 operators, 188 welders, and 475 representatives of other jobs, spirometry was performed at the start of employment and subsequently after 10, 20, and 25 years of work. Spirometry Longitudinal Data Analysis software was used to estimate changes in group means for FEV1 and FVC. Multiple linear regression analysis was used to assess an association between workplace and lung function. Lung function assessed on the basis of calculation of longitudinal FEV1 (FVC) decline was similar in all studied groups. However, multiple linear regression model used in cross-sectional analysis revealed an association between workplace and lung function. In the group of welders, FEF75 was lower in comparison to operators and other miners as early as after 10 years of work. Simultaneously, in smoking welders, the FEV1/FVC ratio was lower than in nonsmokers (p < 0,05). The interactions between type of workplace and smoking (p < 0,05) in their effect on FVC, FEV1, PEF, and FEF50 were shown. Among underground working copper miners, the group of smoking welders is especially threatened by impairment of lung ventilatory function. PMID:27274987

Delayed, but not immediate, feedback after multiple-choice questions increases performance on a subsequent short-answer, but not multiple-choice, exam: evidence for the dual-process theory of memory.

PubMed

Sinha, Neha; Glass, Arnold Lewis

2015-01-01

Three experiments, two performed in the laboratory and one embedded in a college psychology lecture course, investigated the effects of immediate versus delayed feedback following a multiple-choice exam on subsequent short answer and multiple-choice exams. Performance on the subsequent multiple-choice exam was not affected by the timing of the feedback on the prior exam; however, performance on the subsequent short answer exam was better following delayed than following immediate feedback. This was true regardless of the order in which immediate versus delayed feedback was given. Furthermore, delayed feedback only had a greater effect than immediate feedback on subsequent short answer performance following correct, confident responses on the prior exam. These results indicate that delayed feedback cues a student's prior response and increases subsequent recollection of that response. The practical implication is that delayed feedback is better than immediate feedback during academic testing.

Applications of statistics to medical science, III. Correlation and regression.

PubMed

Watanabe, Hiroshi

2012-01-01

In this third part of a series surveying medical statistics, the concepts of correlation and regression are reviewed. In particular, methods of linear regression and logistic regression are discussed. Arguments related to survival analysis will be made in a subsequent paper.

Development of a theoretical screening tool to assess caries risk in Nevada youth.

PubMed

Ditmyer, Marcia M; Mobley, Connie; Draper, Quinn; Demopoulos, Christina; Smith, E Steven

2008-01-01

One objective of this study was to determine the prevalence and severity of caries among Nevada youth, subsequently comparing these data with national statistics. A second objective was to identify the risk factors associated with caries prevalence and severity in order to develop and tailor a theoretical screening tool for this cohort for future validation. Researchers computed the prevalence rates of dental caries (D-score) and severity rates of decayed, missing, and filled teeth indices in a cohort of 9202 students, 13 to 18 years of age, attending public/private schools in the 2005/2006 academic year. Multiple regression established which of the 13 variables significantly contributed to caries risk, subsequently using logistic regression to ascertain the weight of contribution and odds ratios of significant variables. Living in counties with no municipal water fluoridation, increased exposure to environmental smoke, minority race, living in rural communities, and increasing age were the largest significant contributors (respectively). Exposure to tobacco, being female, lack of dental insurance, increased body mass index risk, and lack of dental sealants were also significant, but to a lesser extent. Nonsignificant factors included socioeconomic status, ethnicity, and family history of diabetes. This study confirmed high caries prevalence and severity and identified significant risk factors for inclusion in a theoretical risk screening tool for future validation and translation for use in the early detection of caries risk in Nevada youth.

Predictors of type 2 diabetes among Taiwanese women with prior gestational diabetes mellitus.

PubMed

Lin, Pei-Chao; Hung, Chich-Hsiu; Huang, Ruei-Dian; Chan, Te-Fu

2016-01-01

The aims of this study were to determine the blood glucose screening rate of Taiwanese post-partum women with gestational diabetes (GDM) and to identify the predictors of type 2 diabetes among Taiwanese women with GDM. The medical records of 130 women with GDM, who were delivered at a hospital in southern Taiwan between 1997 and 2010, were retrospectively reviewed. The GDM diagnosis was performed according to the National Diabetes Data Group and Expert Committee Criteria. The 2010 American Diabetes Association diabetes diagnosis criteria were used to determine whether post-partum women subsequently developed type 2 diabetes. In total, 71 records (54.6%) included blood glucose testing after childbirth between the first month and the ninth year, and 29 records (22.3%) documented subsequent type 2 diabetes. In a multiple logistic regression analysis, the patients' pre-pregnancy body mass indices and insulin use during pregnancy were independently associated with subsequent type 2 diabetes. In this study, documentation during pregnancy, which could have provided beneficial insights, was limited. Healthcare professionals should develop a program to improve the post-partum follow-up of women diagnosed with GDM. © 2015 The Authors. Japan Journal of Nursing Science © 2015 Japan Academy of Nursing Science.

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.

Attitude Ambivalence, Friend Norms, and Adolescent Drug Use

PubMed Central

Hohman, Zachary P.; Crano, William D.; Siegel, Jason T.; Alvaro, Eusebio M.

2013-01-01

This study assessed the moderating effects of attitudinal ambivalence on adolescent marijuana use in the context of the theory of planned behavior (TPB). With data from the National Survey of Parents and Youth (N=1,604), two hierarchical multiple regression models were developed to examine the association of ambivalent attitudes, intentions, and later marijuana use. The first model explored the moderating effect of ambivalence on intentions to use marijuana; the second tested the moderation of ambivalence on actual marijuana use 1 year later. Results across both analyses suggest that ambivalence moderated the association of friend norms and subsequent adolescent marijuana use: friend norms were better predictors of marijuana intentions (β=0.151, t=2.29, p=0.02) and subsequent use when adolescents were attitudinally ambivalent about marijuana use (β=0.071, t=2.76, p= 0.006). These results suggest that preventive programs that affect the certainty with which adolescents holds pro- or antimarijuana attitudes may influence the likelihood of their resistance to, initiation, or continuance of marijuana use. PMID:23404670

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.

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

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...

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…

Catatonic Stupor in Schizophrenic Disorders and Subsequent Medical Complications and Mortality

PubMed Central

Funayama, Michitaka; Takata, Taketo; Koreki, Akihiro; Ogino, Satoyuki; Mimura, Masaru

2018-01-01

ABSTRACT Objective Although catatonia can occur secondary to a general medical condition, catatonia itself has been known to lead to various medical compolications. Although case reports on the association of catatonia with subsequent medical complications have been documented, no comprehensive large-scale study has been performed. To investigate specific medical complications after catatonia, we conducted a retrospective cohort study of specific medical complications of schizophrenia patients with catatonia. Methods The 1719 schizophrenia inpatients in our study were categorized into two groups: the catatonia group, i.e., those who exhibited catatonic stupor while they were hospitalized, and the noncatatonia group, i.e., those who never exhibited catatonic stupor. Differences between the two groups in the occurrence of subsequent medical complications were examined using linear and logistic regression analyses, and models were adjusted for potentially confounding factors. Results The catatonia group had an increased risk for mortality (odds ratio = 4.8, 95% confidence interval = 2.0–10.6, p < .01) and certain specific medical complications, i.e., pneumonia, urinary tract infection, sepsis, disseminated intravascular coagulation, rhabdomyolysis, dehydration, deep venous thrombosis, pulmonary embolism, urinary retention, decubitus, arrhythmia, renal failure, neuroleptic malignant syndrome, hypernatremia, and liver dysfunction (all p values < .01, except for deep venous thrombosis, p = .04 in the multiple linear regression analysis). Conclusions Catatonic stupor in schizophrenia substantially raises the risk for specific medical complications and mortality. Hyperactivity of the sympathetic nervous system, dehydration, and immobility, which are frequently involved in catatonia, might contribute to these specific medical complications. In catatonia, meticulous care for both mental and medical conditions should be taken to reduce the risk of adverse medical consequences. PMID:29521882

Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

ERIC Educational Resources Information Center

Smith, Kent W.; Sasaki, M. S.

1979-01-01

A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

The co-occurrence of AIDS and homelessness: results from the integration of administrative databases for AIDS surveillance and public shelter utilisation in Philadelphia.

PubMed

Culhane, D P; Gollub, E; Kuhn, R; Shpaner, M

2001-07-01

Administrative databases from the City of Philadelphia that track public shelter utilisation (n=44 337) and AIDS case reporting (n=7749) were merged to identify rates and risk factors for co-occurring homelessness and AIDS. Multiple decrement life tables analyses were conducted, and logistic regression analyses used to identify risk factors associated with AIDS among the homeless, and homelessness among people with AIDS. City of Philadelphia, Pennsylvania, USA. People admitted to public shelters had a three year rate of subsequent AIDS diagnosis of 1.8 per 100 person years; nine times the rate for the general population of Philadelphia. Logistic regression results show that substance abuse history (OR = 3.14), male gender (OR = 2.05), and a history of serious mental disorder (OR = 1.62) were significantly related to the risk for AIDS diagnosis among shelter users. Among people with AIDS, results show a three year rate of subsequent shelter admission of 6.9 per 100 person years, and a three year rate of prior shelter admission of 9%, three times the three year rate of shelter admission for the general population. Logistic regression results show that intravenous drug user history (OR = 3.14); no private insurance (OR = 2.93); black race (OR = 2.82); pulmonary or extra-pulmonary TB (OR = 1.43); and pneumocystis pneumonia (OR = 0.56) were all related to the risk for shelter admission. Homelessness prevention programmes should target people with HIV risk factors, and HIV prevention programmes should be targeted to homeless persons, as these populations have significant intersection. Reasons and implications for this intersection are discussed.

Multiple-Instance Regression with Structured Data

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

2008-01-01

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

Association of Periodontitis and Subsequent Depression: A Nationwide Population-Based Study.

PubMed

Hsu, Chih-Chao; Hsu, Yi-Chao; Chen, Hsuan-Ju; Lin, Che-Chen; Chang, Kuang-Hsi; Lee, Chang-Yin; Chong, Lee-Won; Kao, Chia-Hung

2015-12-01

Periodontitis is a systemic and chronic inflammatory disease associated with multiple physical conditions. Distress and depression are other problems affecting the progression of periodontitis. However, the causal relationship between depression and periodontitis has not been adequately investigated. This aim of this study was to determine the association between periodontitis and the subsequent development of depression.We identified 12,708 patients with newly diagnosed periodontitis from 2000 to 2005 and 50,832 frequency-matched individuals without periodontitis. Both groups were followed until diagnosed with depression, withdrawal from the National Health Insurance program, or the end of 2011. The association between periodontitis and depressio was analyzed using Cox proportional hazard regression models.The incidence density rate of depression was higher in the periodontitis group than in the nonperiodontitis group, with an adjusted hazard ratio of 1.73 (95% confidence interval 1.58-1.89) when adjusting for sex, age, and comorbidity. Cox models revealed that periodontitis was an independent risk factor for depression in patients, except for comorbidities of diabetes mellitus (DM), alcohol abuse, and cancer.Periodontitis may increase the risk of subsequent depression and was suggested an independent risk factor regardless of sex, age, and most comorbidities. However, DM, alcohol abuse, and cancer may prevent the development of subsequent depression because of DM treatment, the paradoxical effect of alcohol, and emotional distress to cancer, respectively. Prospective studies on the relationship between periodontitis and depression are warranted.

Association of Periodontitis and Subsequent Depression

PubMed Central

Hsu, Chih-Chao; Hsu, Yi-Chao; Chen, Hsuan-Ju; Lin, Che-Chen; Chang, Kuang-Hsi; Lee, Chang-Yin; Chong, Lee-Won; Kao, Chia-Hung

2015-01-01

Abstract Periodontitis is a systemic and chronic inflammatory disease associated with multiple physical conditions. Distress and depression are other problems affecting the progression of periodontitis. However, the causal relationship between depression and periodontitis has not been adequately investigated. This aim of this study was to determine the association between periodontitis and the subsequent development of depression. We identified 12,708 patients with newly diagnosed periodontitis from 2000 to 2005 and 50,832 frequency-matched individuals without periodontitis. Both groups were followed until diagnosed with depression, withdrawal from the National Health Insurance program, or the end of 2011. The association between periodontitis and depressio was analyzed using Cox proportional hazard regression models. The incidence density rate of depression was higher in the periodontitis group than in the nonperiodontitis group, with an adjusted hazard ratio of 1.73 (95% confidence interval 1.58–1.89) when adjusting for sex, age, and comorbidity. Cox models revealed that periodontitis was an independent risk factor for depression in patients, except for comorbidities of diabetes mellitus (DM), alcohol abuse, and cancer. Periodontitis may increase the risk of subsequent depression and was suggested an independent risk factor regardless of sex, age, and most comorbidities. However, DM, alcohol abuse, and cancer may prevent the development of subsequent depression because of DM treatment, the paradoxical effect of alcohol, and emotional distress to cancer, respectively. Prospective studies on the relationship between periodontitis and depression are warranted. PMID:26705230

Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine MR sequences.

PubMed

Tan, Li Kuo; Liew, Yih Miin; Lim, Einly; McLaughlin, Robert A

2017-07-01

Automated left ventricular (LV) segmentation is crucial for efficient quantification of cardiac function and morphology to aid subsequent management of cardiac pathologies. In this paper, we parameterize the complete (all short axis slices and phases) LV segmentation task in terms of the radial distances between the LV centerpoint and the endo- and epicardial contours in polar space. We then utilize convolutional neural network regression to infer these parameters. Utilizing parameter regression, as opposed to conventional pixel classification, allows the network to inherently reflect domain-specific physical constraints. We have benchmarked our approach primarily against the publicly-available left ventricle segmentation challenge (LVSC) dataset, which consists of 100 training and 100 validation cardiac MRI cases representing a heterogeneous mix of cardiac pathologies and imaging parameters across multiple centers. Our approach attained a .77 Jaccard index, which is the highest published overall result in comparison to other automated algorithms. To test general applicability, we also evaluated against the Kaggle Second Annual Data Science Bowl, where the evaluation metric was the indirect clinical measures of LV volume rather than direct myocardial contours. Our approach attained a Continuous Ranked Probability Score (CRPS) of .0124, which would have ranked tenth in the original challenge. With this we demonstrate the effectiveness of convolutional neural network regression paired with domain-specific features in clinical segmentation. Copyright © 2017 Elsevier B.V. All rights reserved.

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

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

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.

Psychological effects of disaster relief activities on Japan ground self-defense force personnel following the 2011 great east Japan earthquake.

PubMed

Dobashi, Kosuke; Nagamine, Masanori; Shigemura, Jun; Tsunoda, Tomoya; Shimizu, Kunio; Yoshino, Aihide; Nomura, Soichiro

2014-01-01

Disaster relief workers are potentially exposed to severe stressors on the job, resulting in a variety of psychological responses. This study aims to clarify the psychological effects of disaster relief activities on Japan Ground Self-Defense Force (JGSDF) personnel following the 2011 Great East Japan Earthquake. A self-report questionnaire was administered to 606 JGSDF personnel one month after completing the disaster relief mission. Posttraumatic stress responses and general psychological distress were assessed using the Impact of Event Scale-Revised (IES-R) and the K10 scales. Associations between outcome variables and independent variables (age, gender, military rank, length of deployment, and exposure to dead bodies) were measured with univariate analyses and subsequent multiple logistic regression analyses. The mean (± SD) IES-R score was 6.2 (± 8.1), and the mean K10 score was 12.8 (± 4.4). In the univariate analyses, exposure to dead bodies and age were identified as significant factors for IES-R and K10 scores, (p < 0.01). However, the multiple logistic regression analyses did not reveal any significant factors although body handlers' exposure approached significance for IES-R. The subjects reported very low psychological responses despite the severe nature of their disaster relief activities. Several factors may account for the low levels of psychological distress and posttraumatic symptoms observed in this study.

The role of enamel thickness and refractive index on human tooth colour.

PubMed

Oguro, Rena; Nakajima, Masatoshi; Seki, Naoko; Sadr, Alireza; Tagami, Junji; Sumi, Yasunori

2016-08-01

To investigate the role of enamel thickness and refractive index (n) on tooth colour. The colour and enamel thickness of fifteen extracted human central incisors were determined according to CIELab colour scale using spectrophotometer (Crystaleye) and swept-source optical coherence tomography (SS-OCT), respectively. Subsequently, labial enamel was trimmed by approximately 100μm, and the colour and remaining enamel thickness were investigated again. This cycle was repeated until dentin appeared. Enamel blocks were prepared from the same teeth and their n were obtained using SS-OCT. Multiple regression analysis was performed to reveal any effects of enamel thickness and n on colour difference (ΔE00) and differences in colour parameters with CIELCh and CIELab colour scales. Multiple regression analysis revealed that enamel thickness (p=0.02) and n of enamel (p<0.001) were statistically significant predictors of ΔE00 after complete enamel trimming. The n was also a significant predictor of ΔH' (p=0.01). Enamel thickness and n were not statistically significant predictors of ΔL', ΔC', Δa* and Δb*. Enamel affected tooth colour, in which n was a statistically significant predictor for tooth colour change. Understanding the role of enamel in tooth colour could contribute to development of aesthetic restorative materials that mimic the colour of natural tooth with minimal reduction of the existing enamel. Copyright © 2016 Elsevier Ltd. All rights reserved.

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…

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

PubMed

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

2010-01-01

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

Refractive Status at Birth: Its Relation to Newborn Physical Parameters at Birth and Gestational Age

PubMed Central

Varghese, Raji Mathew; Sreenivas, Vishnubhatla; Puliyel, Jacob Mammen; Varughese, Sara

2009-01-01

Background Refractive status at birth is related to gestational age. Preterm babies have myopia which decreases as gestational age increases and term babies are known to be hypermetropic. This study looked at the correlation of refractive status with birth weight in term and preterm babies, and with physical indicators of intra-uterine growth such as the head circumference and length of the baby at birth. Methods All babies delivered at St. Stephens Hospital and admitted in the nursery were eligible for the study. Refraction was performed within the first week of life. 0.8% tropicamide with 0.5% phenylephrine was used to achieve cycloplegia and paralysis of accommodation. 599 newborn babies participated in the study. Data pertaining to the right eye is utilized for all the analyses except that for anisometropia where the two eyes were compared. Growth parameters were measured soon after birth. Simple linear regression analysis was performed to see the association of refractive status, (mean spherical equivalent (MSE), astigmatism and anisometropia) with each of the study variables, namely gestation, length, weight and head circumference. Subsequently, multiple linear regression was carried out to identify the independent predictors for each of the outcome parameters. Results Simple linear regression showed a significant relation between all 4 study variables and refractive error but in multiple regression only gestational age and weight were related to refractive error. The partial correlation of weight with MSE adjusted for gestation was 0.28 and that of gestation with MSE adjusted for weight was 0.10. Birth weight had a higher correlation to MSE than gestational age. Conclusion This is the first study to look at refractive error against all these growth parameters, in preterm and term babies at birth. It would appear from this study that birth weight rather than gestation should be used as criteria for screening for refractive error, especially in developing countries where the incidence of intrauterine malnutrition is higher. PMID:19214228

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…

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

From clinical to tissue-based dual TIA: Validation and refinement of ABCD3-I score.

PubMed

Dai, Qiliang; Sun, Wen; Xiong, Yunyun; Hankey, Graeme J; Xiao, Lulu; Zhu, Wusheng; Ma, Minmin; Liu, Wenhua; Liu, Dezhi; Cai, Qiankun; Han, Yunfei; Duan, Lihui; Chen, Xiangliang; Xu, Gelin; Liu, Xinfeng

2015-04-07

To investigate whether dual tissue-defined ischemic attacks, defined as multiple diffusion-weighted imaging lesions of different age and/or arterial territory (dual DWI), are an independent and stronger predictor of 90-day stroke than dual clinical TIAs (dual TIA). Consecutive patients with clinically defined TIA were enrolled and assessed clinically and by MRI within 3 days. The predictive ability of the ABCD clinical factors, dual TIA, and dual DWI was evaluated by means of multivariate logistic regression. Among 658 patients who were included in the study and completed 90 days of follow-up, a total of 70 patients (10.6%) experienced subsequent stroke by 90 days. Multivariate logistic regression indicated that dual DWI was an independent predictor for subsequent stroke (odds ratio 4.64, 95% confidence interval 2.15-10.01), while dual TIA was not (odds ratio 1.18, 95% confidence interval 0.69-2.01). C statistics was higher when the item of dual TIA in ABCD3-I score was replaced by dual DWI (0.759 vs 0.729, p = 0.035). The net reclassification value for 90-day stroke risk was also improved (continuous net reclassification improvement 0.301, p = 0.017). Dual DWI independently predicted future stroke in patients with TIA. A new ABCD3-I score with dual DWI instead of dual clinical TIA may improve risk stratification for early stroke risk after TIA. © 2015 American Academy of Neurology.

Cryotherapy-Induced Persistent Vasoconstriction After Cutaneous Cooling: Hysteresis Between Skin Temperature and Blood Perfusion.

PubMed

Khoshnevis, Sepideh; Craik, Natalie K; Matthew Brothers, R; Diller, Kenneth R

2016-03-01

The goal of this study was to investigate the persistence of cold-induced vasoconstriction following cessation of active skin-surface cooling. This study demonstrates a hysteresis effect that develops between skin temperature and blood perfusion during the cooling and subsequent rewarming period. An Arctic Ice cryotherapy unit (CTU) was applied to the knee region of six healthy subjects for 60 min of active cooling followed by 120 min of passive rewarming. Multiple laser Doppler flowmetry perfusion probes were used to measure skin blood flow (expressed as cutaneous vascular conductance (CVC)). Skin surface cooling produced a significant reduction in CVC (P < 0.001) that persisted throughout the duration of the rewarming period. In addition, there was a hysteresis effect between CVC and skin temperature during the cooling and subsequent rewarming cycle (P < 0.01). Mixed model regression (MMR) showed a significant difference in the slopes of the CVC-skin temperature curves during cooling and rewarming (P < 0.001). Piecewise regression was used to investigate the temperature thresholds for acceleration of CVC during the cooling and rewarming periods. The two thresholds were shown to be significantly different (P = 0.003). The results show that localized cooling causes significant vasoconstriction that continues beyond the active cooling period despite skin temperatures returning toward baseline values. The significant and persistent reduction in skin perfusion may contribute to nonfreezing cold injury (NFCI) associated with cryotherapy.

Cryotherapy-Induced Persistent Vasoconstriction After Cutaneous Cooling: Hysteresis Between Skin Temperature and Blood Perfusion

PubMed Central

Khoshnevis, Sepideh; Craik, Natalie K.; Matthew Brothers, R.; Diller, Kenneth R.

2016-01-01

The goal of this study was to investigate the persistence of cold-induced vasoconstriction following cessation of active skin-surface cooling. This study demonstrates a hysteresis effect that develops between skin temperature and blood perfusion during the cooling and subsequent rewarming period. An Arctic Ice cryotherapy unit (CTU) was applied to the knee region of six healthy subjects for 60 min of active cooling followed by 120 min of passive rewarming. Multiple laser Doppler flowmetry perfusion probes were used to measure skin blood flow (expressed as cutaneous vascular conductance (CVC)). Skin surface cooling produced a significant reduction in CVC (P < 0.001) that persisted throughout the duration of the rewarming period. In addition, there was a hysteresis effect between CVC and skin temperature during the cooling and subsequent rewarming cycle (P < 0.01). Mixed model regression (MMR) showed a significant difference in the slopes of the CVC–skin temperature curves during cooling and rewarming (P < 0.001). Piecewise regression was used to investigate the temperature thresholds for acceleration of CVC during the cooling and rewarming periods. The two thresholds were shown to be significantly different (P = 0.003). The results show that localized cooling causes significant vasoconstriction that continues beyond the active cooling period despite skin temperatures returning toward baseline values. The significant and persistent reduction in skin perfusion may contribute to nonfreezing cold injury (NFCI) associated with cryotherapy. PMID:26632263

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.

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.

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 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.

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…

Multiple traumatic brain injury and concussive symptoms among deployed military personnel.

PubMed

Bryan, Craig J

2013-01-01

To identify if concussive symptoms occur with greater frequency among military personnel with multiple lifetime TBIs and if a history of TBI increases risk for subsequent TBI. One hundred and sixty-one military personnel referred to a TBI clinic for evaluation and treatment of suspected head injury at a military clinic in Iraq. Military patients completed standardized self-report measures of concussion, depression and post-traumatic stress symptoms; clinical interview; and physical examination. Group comparisons were made according to number of lifetime TBIs and logistic regression was utilized to determine the association of past TBIs on current TBI. Patients with one or more previous TBIs were more likely to report concussion symptoms immediately following a recent injury and during the evaluation. Although differences between single and multiple TBI groups were observed, these did not reach the level of statistical significance. A history of any TBI increased the likelihood of current TBI diagnosis, but this relationship was no longer significant when adjusting for injury mechanism, depression and post-traumatic stress symptoms. Among deployed military personnel, the relationship of previous TBI with recent TBI and concussive symptoms may be largely explained by the presence of psychological symptoms.

Late-Life Depressive Symptoms and Lifetime History of Major Depression: Cognitive Deficits are Largely Due to Incipient Dementia rather than Depression.

PubMed

Heser, Kathrin; Bleckwenn, Markus; Wiese, Birgitt; Mamone, Silke; Riedel-Heller, Steffi G; Stein, Janine; Lühmann, Dagmar; Posselt, Tina; Fuchs, Angela; Pentzek, Michael; Weyerer, Siegfried; Werle, Jochen; Weeg, Dagmar; Bickel, Horst; Brettschneider, Christian; König, Hans-Helmut; Maier, Wolfgang; Scherer, Martin; Wagner, Michael

2016-08-01

Late-life depression is frequently accompanied by cognitive impairments. Whether these impairments indicate a prodromal state of dementia, or are a symptomatic expression of depression per se is not well-studied. In a cohort of very old initially non-demented primary care patients (n = 2,709, mean age = 81.1 y), cognitive performance was compared between groups of participants with or without elevated depressive symptoms and with or without subsequent dementia using ANCOVA (adjusted for age, sex, and education). Logistic regression analyses were computed to predict subsequent dementia over up to six years of follow-up. The same analytical approach was performed for lifetime major depression. Participants with elevated depressive symptoms without subsequent dementia showed only small to medium cognitive deficits. In contrast, participants with depressive symptoms with subsequent dementia showed medium to very large cognitive deficits. In adjusted logistic regression models, learning and memory deficits predicted the risk for subsequent dementia in participants with depressive symptoms. Participants with a lifetime history of major depression without subsequent dementia showed no cognitive deficits. However, in adjusted logistic regression models, learning and orientation deficits predicted the risk for subsequent dementia also in participants with lifetime major depression. Marked cognitive impairments in old age depression should not be dismissed as "depressive pseudodementia", but require clinical attention as a possible sign of incipient dementia. Non-depressed elderly with a lifetime history of major depression, who remained free of dementia during follow-up, had largely normal cognitive performance.

Severe sunburn and subsequent risk of primary cutaneous malignant melanoma in scotland.

PubMed Central

MacKie, R. M.; Aitchison, T.

1982-01-01

A case-control study of occupational and recreational sun exposure, Mediterranean and other sun-exposed holidays, tanning history and history of isolated episodes of severe sunburn has been carried out on 113 patients with cutaneous malignant melanoma and 113 age- and sex-matched controls. Social class and skin type were also considered in the analysis of the data which involved the use of conditional multiple logistic regression. A highly significant increase in the history of severe sunburn was recorded in melanoma patients of both sexes in the 5-year period preceding presentation with their tumour. Higher social class and negative history of recreational sun exposure were also significantly increased in patients by comparison with controls. In the male group severe sunburn, lack of occupational sun exposure and higher social class were significant factors while in the female group only severe sunburn was significantly increased in the melanoma patients. This study thus provides evidence to suggest that short intense episodes of UV exposure resulting in burning may be one of the aetiological factors involved in subsequent development of melanoma. PMID:7150488

Attitude ambivalence, friend norms, and adolescent drug use.

PubMed

Hohman, Zachary P; Crano, William D; Siegel, Jason T; Alvaro, Eusebio M

2014-02-01

This study assessed the moderating effects of attitudinal ambivalence on adolescent marijuana use in the context of the theory of planned behavior (TPB). With data from the National Survey of Parents and Youth (N = 1,604), two hierarchical multiple regression models were developed to examine the association of ambivalent attitudes, intentions, and later marijuana use. The first model explored the moderating effect of ambivalence on intentions to use marijuana; the second tested the moderation of ambivalence on actual marijuana use 1 year later. Results across both analyses suggest that ambivalence moderated the association of friend norms and subsequent adolescent marijuana use: friend norms were better predictors of marijuana intentions (β = 0.151, t = 2.29, p = 0.02) and subsequent use when adolescents were attitudinally ambivalent about marijuana use (β = 0.071, t = 2.76, p = 0.006). These results suggest that preventive programs that affect the certainty with which adolescents holds pro- or antimarijuana attitudes may influence the likelihood of their resistance to, initiation, or continuance of marijuana use.

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)

Incremental Net Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, Michael

2005-01-01

A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…

Floating Data and the Problem with Illustrating Multiple Regression.

ERIC Educational Resources Information Center

Sachau, Daniel A.

2000-01-01

Discusses how to introduce basic concepts of multiple regression by creating a large-scale, three-dimensional regression model using the classroom walls and floor. Addresses teaching points that should be covered and reveals student reaction to the model. Finds that the greatest benefit of the model is the low fear, walk-through, nonmathematical…

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

Additive effects prevail: The response of biota to multiple stressors in an intensively monitored watershed.

PubMed

Gieswein, Alexander; Hering, Daniel; Feld, Christian K

2017-09-01

Freshwater ecosystems are impacted by a range of stressors arising from diverse human-caused land and water uses. Identifying the relative importance of single stressors and understanding how multiple stressors interact and jointly affect biology is crucial for River Basin Management. This study addressed multiple human-induced stressors and their effects on the aquatic flora and fauna based on data from standard WFD monitoring schemes. For altogether 1095 sites within a mountainous catchment, we used 12 stressor variables covering three different stressor groups: riparian land use, physical habitat quality and nutrient enrichment. Twenty-one biological metrics calculated from taxa lists of three organism groups (fish, benthic invertebrates and aquatic macrophytes) served as response variables. Stressor and response variables were subjected to Boosted Regression Tree (BRT) analysis to identify stressor hierarchy and stressor interactions and subsequently to Generalised Linear Regression Modelling (GLM) to quantify the stressors standardised effect size. Our results show that riverine habitat degradation was the dominant stressor group for the river fauna, notably the bed physical habitat structure. Overall, the explained variation in benthic invertebrate metrics was higher than it was in fish and macrophyte metrics. In particular, general integrative (aggregate) metrics such as % Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa performed better than ecological traits (e.g. % feeding types). Overall, additive stressor effects dominated, while significant and meaningful stressor interactions were generally rare and weak. We concluded that given the type of stressor and ecological response variables addressed in this study, river basin managers do not need to bother much about complex stressor interactions, but can focus on the prevailing stressors according to the hierarchy identified. Copyright © 2017 Elsevier B.V. All rights reserved.

Post-processing ECMWF precipitation and temperature ensemble reforecasts for operational hydrologic forecasting at various spatial scales

NASA Astrophysics Data System (ADS)

Verkade, J. S.; Brown, J. D.; Reggiani, P.; Weerts, A. H.

2013-09-01

The ECMWF temperature and precipitation ensemble reforecasts are evaluated for biases in the mean, spread and forecast probabilities, and how these biases propagate to streamflow ensemble forecasts. The forcing ensembles are subsequently post-processed to reduce bias and increase skill, and to investigate whether this leads to improved streamflow ensemble forecasts. Multiple post-processing techniques are used: quantile-to-quantile transform, linear regression with an assumption of bivariate normality and logistic regression. Both the raw and post-processed ensembles are run through a hydrologic model of the river Rhine to create streamflow ensembles. The results are compared using multiple verification metrics and skill scores: relative mean error, Brier skill score and its decompositions, mean continuous ranked probability skill score and its decomposition, and the ROC score. Verification of the streamflow ensembles is performed at multiple spatial scales: relatively small headwater basins, large tributaries and the Rhine outlet at Lobith. The streamflow ensembles are verified against simulated streamflow, in order to isolate the effects of biases in the forcing ensembles and any improvements therein. The results indicate that the forcing ensembles contain significant biases, and that these cascade to the streamflow ensembles. Some of the bias in the forcing ensembles is unconditional in nature; this was resolved by a simple quantile-to-quantile transform. Improvements in conditional bias and skill of the forcing ensembles vary with forecast lead time, amount, and spatial scale, but are generally moderate. The translation to streamflow forecast skill is further muted, and several explanations are considered, including limitations in the modelling of the space-time covariability of the forcing ensembles and the presence of storages.

SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes.

PubMed

Mägi, Reedik; Suleimanov, Yury V; Clarke, Geraldine M; Kaakinen, Marika; Fischer, Krista; Prokopenko, Inga; Morris, Andrew P

2017-01-11

Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements "reverse regression" methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10 -8 ), which has not been reported in previous large-scale GWAS of lipid traits. The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.

The natural history of endometrial polyps.

PubMed

Wong, M; Crnobrnja, B; Liberale, V; Dharmarajah, K; Widschwendter, M; Jurkovic, D

2017-02-01

What is the natural history of endometrial polyps in women who are managed expectantly? The growth rates of expectantly managed polyps vary considerably and cannot be accurately predicted. The majority of polyps detected on ultrasound are treated surgically, and therefore little is known about their natural history. Some polyps have been reported to regress spontaneously without the need for treatment; however, the factors predictive of regression are unknown. This was a retrospective cohort study conducted at the Department of Gynaecology, University College London Hospitals. We searched our ultrasound clinic database between July 1997 and September 2015, to identify women aged 18 years or older with endometrial polyps that were managed expectantly for ≥6 months. All women attended for a minimum of two ultrasound scans. A single expert operator performed all ultrasound scans. Those with <6-month follow-up and those who were taking hormonal contraception, HRT or tamoxifen were excluded from the study. The mean diameter of each polyp was calculated from the measurements in three perpendicular planes. The polyp growth rate was expressed as annual percentage change in the mean diameter. Non-parametric tests and the Fisher's exact test were used to compare differences in polyp mean diameters and growth rates between women of different demographic characteristics. To correct for multiple significance testing, we used the Bonferroni method, giving the level of probability at which findings were considered significant as P < 0.0029 (as 17 tests were undertaken). We included 112 women with endometrial polyps, which were expectantly managed over a median period of 22.5 months (range, 6-136). The annual endometrial polyp growth rate varied with a median of 1.0% (interquartile range, -6.5 to 14.3). There was no association between women's demographic characteristics or polyps' morphology and their growth rates. Eleven out of 75 (15% (95% CI, 6.9%-23.1%)) women who initially did not have abnormal uterine bleeding subsequently developed abnormal bleeding during the follow-up period. Polyp growth rate was not associated with the subsequent development of abnormal uterine bleeding (P = 0.397). Seven out of 112 (6.3% (95% CI, 1.8%-10.8%)) women had complete regression of their polyps without treatment during a median follow-up period of 28 months (range, 9-56). Spontaneous regression appeared to occur more frequently in premenopausal women (P = 0.016) and in those who presented with abnormal uterine bleeding at diagnosis (P = 0.004); however, the differences did not reach statistical significance after correction for multiple comparisons. This study was retrospective and therefore may be prone to selection and information biases. The lack of histological confirmation on all ultrasound diagnoses may also be considered as a limitation. Women should be advised that the growth pattern of an individual polyp cannot be accurately predicted; however, a small proportion of polyps do regress spontaneously. There was no correlation between polyps' growth rate and the subsequent development of abnormal uterine bleeding. In view of that, routine monitoring of asymptomatic polyps by ultrasound is not helpful and encouraging women to report clinical symptoms is more useful in deciding whether treatment is required. In contrast to previous studies, we found that polyps may regress more frequently in premenopausal women and in those who presented with abnormal uterine bleeding; a larger sample size would give us greater power to detect a difference in these subgroups of women. No study funding was received and no competing interests are present. N/A. © The Author 2016. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

PubMed Central

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses. PMID:22457655

Tools to support interpreting multiple regression in the face of multicollinearity.

PubMed

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

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.

Coffee and Green Tea Consumption and Subsequent Risk of Malignant Lymphoma and Multiple Myeloma in Japan: The Japan Public Health Center-based Prospective Study.

PubMed

Ugai, Tomotaka; Matsuo, Keitaro; Sawada, Norie; Iwasaki, Motoki; Yamaji, Taiki; Shimazu, Taichi; Sasazuki, Shizuka; Inoue, Manami; Kanda, Yoshinobu; Tsugane, Shoichiro

2017-08-01

Background: The aim of this study was to investigate the association of coffee and green tea consumption and the risk of malignant lymphoma and multiple myeloma in a large-scale population-based cohort study in Japan. Methods: In this analysis, a total of 95,807 Japanese subjects (45,937 men and 49,870 women; ages 40-69 years at baseline) of the Japan Public Health Center-based Prospective Study who completed a questionnaire about their coffee and green tea consumption were followed up until December 31, 2012, for an average of 18 years. HRs and 95% confidence intervals were estimated using a Cox regression model adjusted for potential confounders as a measure of association between the risk of malignant lymphoma and multiple myeloma associated with coffee and green tea consumption at baseline. Results: During the follow-up period, a total of 411 malignant lymphoma cases and 138 multiple myeloma cases were identified. Overall, our findings showed no significant association between coffee or green tea consumption and the risk of malignant lymphoma or multiple myeloma for both sexes. Conclusions: In this study, we observed no significant association between coffee or green tea consumption and the risk of malignant lymphoma or multiple myeloma. Impact: Our results do not support an association between coffee or green tea consumption and the risk of malignant lymphoma or multiple myeloma. Cancer Epidemiol Biomarkers Prev; 26(8); 1352-6. ©2017 AACR . ©2017 American Association for Cancer Research.

Neuroinflammation and its relationship to changes in brain volume and white matter lesions in multiple sclerosis.

PubMed

Datta, Gourab; Colasanti, Alessandro; Rabiner, Eugenii A; Gunn, Roger N; Malik, Omar; Ciccarelli, Olga; Nicholas, Richard; Van Vlierberghe, Eline; Van Hecke, Wim; Searle, Graham; Santos-Ribeiro, Andre; Matthews, Paul M

2017-11-01

Brain magnetic resonance imaging is an important tool in the diagnosis and monitoring of multiple sclerosis patients. However, magnetic resonance imaging alone provides limited information for predicting an individual patient's disability progression. In part, this is because magnetic resonance imaging lacks sensitivity and specificity for detecting chronic diffuse and multi-focal inflammation mediated by activated microglia/macrophages. The aim of this study was to test for an association between 18 kDa translocator protein brain positron emission tomography signal, which arises largely from microglial activation, and measures of subsequent disease progression in multiple sclerosis patients. Twenty-one patients with multiple sclerosis (seven with secondary progressive disease and 14 with a relapsing remitting disease course) underwent T1- and T2-weighted and magnetization transfer magnetic resonance imaging at baseline and after 1 year. Positron emission tomography scanning with the translocator protein radioligand 11C-PBR28 was performed at baseline. Brain tissue and lesion volumes were segmented from the T1- and T2-weighted magnetic resonance imaging and relative 11C-PBR28 uptake in the normal-appearing white matter was estimated as a distribution volume ratio with respect to a caudate pseudo-reference region. Normal-appearing white matter distribution volume ratio at baseline was correlated with enlarging T2-hyperintense lesion volumes over the subsequent year (ρ = 0.59, P = 0.01). A post hoc analysis showed that this association reflected behaviour in the subgroup of relapsing remitting patients (ρ = 0.74, P = 0.008). By contrast, in the subgroup of secondary progressive patients, microglial activation at baseline was correlated with later progression of brain atrophy (ρ = 0.86, P = 0.04). A regression model including the baseline normal-appearing white matter distribution volume ratio, T2 lesion volume and normal-appearing white matter magnetization transfer ratio for all of the patients combined explained over 90% of the variance in enlarging lesion volume over the subsequent 1 year. Glial activation in white matter assessed by translocator protein PET significantly improves predictions of white matter lesion enlargement in relapsing remitting patients and is associated with greater brain atrophy in secondary progressive disease over a period of short term follow-up. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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…

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.

Impaired executive function can predict recurrent falls in Parkinson's disease.

PubMed

Mak, Margaret K; Wong, Adrian; Pang, Marco Y

2014-12-01

To examine whether impairment in executive function independently predicts recurrent falls in people with Parkinson's disease (PD). Prospective cohort study. University motor control research laboratory. A convenience sample of community-dwelling people with PD (N=144) was recruited from a patient self-help group and movement disorders clinics. Not applicable. Executive function was assessed with the Mattis Dementia Rating Scale Initiation/Perseveration (MDRS-IP) subtest, and fear of falling (FoF) with the Activities-specific Balance Confidence (ABC) Scale. All participants were followed up for 12 months to record the number of monthly fall events. Forty-two people with PD had at least 2 falls during the follow-up period and were classified as recurrent fallers. After accounting for demographic variables and fall history (P=.001), multiple logistic regression analysis showed that the ABC scores (P=.014) and MDRS-IP scores (P=.006) were significantly associated with future recurrent falls among people with PD. The overall accuracy of the prediction was 85.9%. With the use of the significant predictors identified in multiple logistic regression analysis, a prediction model determined by the logistic function was generated: Z = 1.544 + .378 (fall history) - .045 (ABC) - .145 (MDRS-IP). Impaired executive function is a significant predictor of future recurrent falls in people with PD. Participants with executive dysfunction and greater FoF at baseline had a significantly greater risk of sustaining a recurrent fall within the subsequent 12 months. Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

The Relationship Between Preoperative and Primary Care Blood Pressure Among Veterans Presenting from Home for Surgery. Is There Evidence for Anesthesiologist-Initiated Blood Pressure Referral?

PubMed Central

Schonberger, Robert B.; Burg, Matthew M.; Holt, Natalie; Lukens, Carrie L.; Dai, Feng; Brandt, Cynthia

2011-01-01

Background American College of Cardiology/American Heart Association guidelines describe the perioperative evaluation as “a unique opportunity to identify patients with hypertension,” however factors such as anticipatory stress or medication noncompliance may induce a bias toward higher blood pressure, leaving clinicians unsure about how to interpret preoperative hypertension. Information describing the relationship between preoperative intake blood pressure and primary care measurements could help anesthesiologists make primary care referrals for improved blood pressure control in an evidence-based fashion. We hypothesized that the preoperative examination provides a useful basis for initiating primary care blood pressure referral. Methods We analyzed retrospective data on 2807 patients who arrived from home for surgery and who were subsequently evaluated within 6 months after surgery in the primary care center of the same institution. After descriptive analysis, we conducted multiple linear regression analysis to identify day-of-surgery (DOS) factors associated with subsequent primary care blood pressure. We calculated the sensitivity, specificity, and positive and negative predictive value of different blood pressure referral thresholds using both a single-measurement and a two-stage screen incorporating recent preoperative and DOS measurements for identifying patients with subsequently elevated primary care blood pressure. Results DOS systolic blood pressure (SBP) was higher than subsequent primary care SBP by a mean bias of 5.5mmHg (95% limits of agreement +43.8 to −32.8). DOS diastolic blood pressure (DBP) was higher than subsequent primary care DBP by a mean bias of 1.5mmHg (95% limits of agreement +13.0 to −10.0). Linear regression of DOS factors explained 19% of the variability in primary care SBP and 29% of the variability in DBP. Accounting for the observed bias, a two-stage SBP referral screen requiring preoperative clinic SBP≥140mmHg and DOS SBP≥146mmHg had 95.9% estimated specificity (95% CI 94.4 to 97.0) for identifying subsequent primary care SBP≥140mmHg and estimated sensitivity of 26.8% (95% CI 22.0 to 32.0). A similarly high specificity using a single DOS SBP required a threshold SBP≥160mmHg, for which estimated specificity was 95.2% (95% CI 94.2 to 96.1). For DBP, a presenting DOS DBP≥92mmHg had 95.7% specificity (95% CI 94.8 to 96.4) for subsequent primary care DBP≥90mmHg with a sensitivity of 18.8% (95% CI 14.4 to 24.0). Conclusion A small bias toward higher DOS blood pressures relative to subsequent primary care measurements was observed. DOS factors predicted only a small proportion of the observed variation. Accounting for the observed bias, a two-stage SBP threshold and a single-reading DBP threshold were highly specific though insensitive for identifying subsequent primary care blood pressure elevation. PMID:22075017

Are High-Lethality Suicide Attempters With Bipolar Disorder a Distinct Phenotype?

PubMed Central

Oquendo, Maria A.; Carballo, Juan Jose; Rajouria, Namita; Currier, Dianne; Tin, Adrienne; Merville, Jessica; Galfalvy, Hanga C.; Sher, Leo; Grunebaum, Michael F.; Burke, Ainsley K.; Mann, J. John

2013-01-01

Because Bipolar Disorder (BD) individuals making highly lethal suicide attempts have greater injury burden and risk for suicide, early identification is critical. BD patients were classified as high- or low-lethality attempters. High-lethality attempts required inpatient medical treatment. Mixed effects logistic regression models and permutation analyses examined correlations between lethality, number, and order of attempts. High-lethality attempters reported greater suicidal intent and more previous attempts. Multiple attempters showed no pattern of incremental lethality increase with subsequent attempts, but individuals with early high-lethality attempts more often made high-lethality attempts later. A subset of high-lethality attempters make only high-lethality attempts. However, presence of previous low-lethality attempts does not indicate that risk for more lethal, possibly successful, attempts is reduced. PMID:19590998

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…

Predictive value of age of walking for later motor performance in children with mental retardation.

PubMed

Kokubun, M; Haishi, K; Okuzumi, H; Hosobuchi, T; Koike, T

1996-12-01

The purpose of the present study was to clarify the predictive value of age of walking for later motor performance in children with mental retardation. While paying due attention to other factors, our investigation focused on the relationship between a subject's age of walking, and his or her subsequent beam-walking performance. The subjects were 85 children with mental retardation with an average age of 13 years and 3 months. Beam-walking performance was measured by a procedure developed by the authors. Five low beams (5 cm) which varied in width (12.5, 10, 7.5, 5 and 2.5 cm) were employed. The performance of subjects was scored from zero to five points according to the width of the beam that they were able to walk without falling off. From the results of multiple regression analysis, three independent variables were found to be significantly related to beam-walking performance. The age of walking was the most basic variable: partial correlation coefficient (PCC) = -45; standardized partial regression coefficient (SPRC) = -0.41. The next variable in importance was walking duration (PCC = 0.38; SPRC = 0.31). The autism variable also contributed significantly (PCC = 0.28; SPRC = 0.22). Therefore, within the age range used in the present study, the age of walking in children with mental retardation was thought to have sufficient predictive value, even when the variables which might have possibly affected their subsequent performance were taken into consideration; the earlier the age of walking, the better the beam-walking performance.

The impact of substance use on brain structure in people at high risk of developing schizophrenia.

PubMed

Welch, Killian A; McIntosh, Andrew M; Job, Dominic E; Whalley, Heather C; Moorhead, Thomas W; Hall, Jeremy; Owens, David G C; Lawrie, Stephen M; Johnstone, Eve C

2011-09-01

Ventricular enlargement and reduced prefrontal volume are consistent findings in schizophrenia. Both are present in first episode subjects and may be detectable before the onset of clinical disorder. Substance misuse is more common in people with schizophrenia and is associated with similar brain abnormalities. We employ a prospective cohort study with nested case control comparison design to investigate the association between substance misuse, brain abnormality, and subsequent schizophrenia. Substance misuse history, imaging data, and clinical information were collected on 147 subjects at high risk of schizophrenia and 36 controls. Regions exhibiting a significant relationship between level of use of alcohol, cannabis or tobacco, and structure volume were identified. Multivariate regression then elucidated the relationship between level of substance use and structure volumes while accounting for correlations between these variables and correcting for potential confounders. Finally, we established whether substance misuse was associated with later risk of schizophrenia. Increased ventricular volume was associated with alcohol and cannabis use in a dose-dependent manner. Alcohol consumption was associated with reduced frontal lobe volume. Multiple regression analyses found both alcohol and cannabis were significant predictors of these abnormalities when simultaneously entered into the statistical model. Alcohol and cannabis misuse were associated with an increased subsequent risk of schizophrenia. We provide prospective evidence that use of cannabis or alcohol by people at high genetic risk of schizophrenia is associated with brain abnormalities and later risk of psychosis. A family history of schizophrenia may render the brain particularly sensitive to the risk-modifying effects of these substances.

Polychlorinated Biphenyls, Glycaemia and Diabetes in a Population Living in a Highly Polychlorinated Biphenyls-Polluted Area in Northern Italy: a Cross-sectional and Cohort Study

PubMed Central

Zani, Claudia; Donato, Francesco; Magoni, Michele; Feretti, Donatella; Covolo, Loredana; Vassallo, Francesco; Speziani, Fabrizio; Scarcella, Carmelo; Bergonzi, Roberto; Apostoli, Pietro

2013-01-01

Conflicts of interests: the authors declare no potential conflict of interests. Background Polychlorinated biphenyls (PCBs) have been found to be associated with diabetes in some, but not all, studies performed so far. The aim of this study was to assess the association between PCB serum levels and glycaemia and diabetes in people living in Brescia, a highly industrialised PCB-polluted town in Northern Italy. Design and Methods 527 subjects were enrolled in a cross-sectional population-based study: they were interviewed face-to-face in 2003 and also provided a blood sample under fasting conditions. The concentration of 24 PCB congeners was determined using gas-chromatography (GC/MS). Subsequently, all subjects were included in a follow-up (cohort) study. According to the Local Health Authority health-care database, subjects were considered to be diabetic if they had diabetes at interview time (prevalent cases) or during a 7-year follow-up (incident cases). Results A total of 53 subjects (10.0%) were diabetics: 28 had diabetes at enrolment and other 25 developed the disease subsequently. Diabetes frequency increased according to the serum concentrations of total PCBs and single PCB congeners, but no association was found when estimates were adjusted for education, body mass index, age and gender by logistic regression analysis. Accordingly, glycaemia increased with PCB serum levels, but no association was observed when multiple regression analysis, including confounding factors, was performed. Conclusions This study does not support the hypothesis that PCB environmental exposure is strictly associated with diabetes or glycaemia. PMID:25170473

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.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

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.)

Sport activities differentiating match-play improvement in elite youth footballers - a 2-year longitudinal study.

PubMed

Güllich, Arne; Kovar, Peter; Zart, Sebastian; Reimann, Ansgar

2017-02-01

This study examined contributions of different types of sport activities to the development of elite youth soccer performance. Match-play performance of 44 German male players was assessed by expert coaches twice, 24 months apart (age 11.1-13.1 years), based on videotaped 5v5 matches. Player pairs were matched by identical age and initial performance at t 1 . Each player was assigned to a group of either "Strong" or "Weak Responders" based on a higher or lower subsequent performance improvement at t 2 within each pair (mean Δperformance 29% vs. 7%). A questionnaire recorded current and earlier amounts of organised practice/training and non-organised sporting play, in soccer and other sports, respectively. Group comparison revealed that "Strong Responders" accumulated more non-organised soccer play and organised practice/training in other sports, but not more organised soccer practice/training. Subsequent multivariate analyses (multiple linear regression analyses (MLR)) highlighted that higher resultant match-play performance at t 2 was accounted for R 2 adj  = 0.65 by performance at t 1 , together with more non-organised soccer play and organised engagement in other sports, respectively, and greater current, but less earlier volume of organised soccer. The findings suggest that variable early sporting experience facilitates subsequent soccer performance development in German elite youth footballers.

GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature.

PubMed

Ye, Ning; Yin, Hengfu; Liu, Jingjing; Dai, Xiaogang; Yin, Tongming

2015-01-01

The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

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.

Bone mineral density and correlation factor analysis in normal Taiwanese children.

PubMed

Shu, San-Ging

2007-01-01

Our aim was to establish reference data and linear regression equations for lumbar bone mineral density (BMD) in normal Taiwanese children. Several influencing factors of lumbar BMD were investigated. Two hundred fifty-seven healthy children were recruited from schools, 136 boys and 121 girls, aged 4-18 years were enrolled on a voluntary basis with written consent. Their height, weight, blood pressure, puberty stage, bone age and lumbar BMD (L2-4) by dual energy x-ray absorptiometry (DEXA) were measured. Data were analyzed using Pearson correlation and stepwise regression tests. All measurements increased with age. Prior to age 8, there was no gender difference. Parameters such as height, weight, and bone age (BA) in girls surpassed boys between ages 8-13 without statistical significance (p> or =0.05). This was reversed subsequently after age 14 in height (p<0.05). BMD difference had the same trend but was not statistically significant either. The influencing power of puberty stage and bone age over BMD was almost equal to or higher than that of height and weight. All the other factors correlated with BMD to variable powers. Multiple linear regression equations for boys and girls were formulated. BMD reference data is provided and can be used to monitor childhood pathological conditions. However, BMD in those with abnormal bone age or pubertal development could need modifications to ensure accuracy.

Semiparametric temporal process regression of survival-out-of-hospital.

PubMed

Zhan, Tianyu; Schaubel, Douglas E

2018-05-23

The recurrent/terminal event data structure has undergone considerable methodological development in the last 10-15 years. An example of the data structure that has arisen with increasing frequency involves the recurrent event being hospitalization and the terminal event being death. We consider the response Survival-Out-of-Hospital, defined as a temporal process (indicator function) taking the value 1 when the subject is currently alive and not hospitalized, and 0 otherwise. Survival-Out-of-Hospital is a useful alternative strategy for the analysis of hospitalization/survival in the chronic disease setting, with the response variate representing a refinement to survival time through the incorporation of an objective quality-of-life component. The semiparametric model we consider assumes multiplicative covariate effects and leaves unspecified the baseline probability of being alive-and-out-of-hospital. Using zero-mean estimating equations, the proposed regression parameter estimator can be computed without estimating the unspecified baseline probability process, although baseline probabilities can subsequently be estimated for any time point within the support of the censoring distribution. We demonstrate that the regression parameter estimator is asymptotically normal, and that the baseline probability function estimator converges to a Gaussian process. Simulation studies are performed to show that our estimating procedures have satisfactory finite sample performances. The proposed methods are applied to the Dialysis Outcomes and Practice Patterns Study (DOPPS), an international end-stage renal disease study.

An Estimate of Avian Mortality at Communication Towers in the United States and Canada

PubMed Central

Longcore, Travis; Rich, Catherine; Mineau, Pierre; MacDonald, Beau; Bert, Daniel G.; Sullivan, Lauren M.; Mutrie, Erin; Gauthreaux, Sidney A.; Avery, Michael L.; Crawford, Robert L.; Manville, Albert M.; Travis, Emilie R.; Drake, David

2012-01-01

Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action. PMID:22558082

An estimate of avian mortality at communication towers in the United States and Canada.

PubMed

Longcore, Travis; Rich, Catherine; Mineau, Pierre; MacDonald, Beau; Bert, Daniel G; Sullivan, Lauren M; Mutrie, Erin; Gauthreaux, Sidney A; Avery, Michael L; Crawford, Robert L; Manville, Albert M; Travis, Emilie R; Drake, David

2012-01-01

Avian mortality at communication towers in the continental United States and Canada is an issue of pressing conservation concern. Previous estimates of this mortality have been based on limited data and have not included Canada. We compiled a database of communication towers in the continental United States and Canada and estimated avian mortality by tower with a regression relating avian mortality to tower height. This equation was derived from 38 tower studies for which mortality data were available and corrected for sampling effort, search efficiency, and scavenging where appropriate. Although most studies document mortality at guyed towers with steady-burning lights, we accounted for lower mortality at towers without guy wires or steady-burning lights by adjusting estimates based on published studies. The resulting estimate of mortality at towers is 6.8 million birds per year in the United States and Canada. Bootstrapped subsampling indicated that the regression was robust to the choice of studies included and a comparison of multiple regression models showed that incorporating sampling, scavenging, and search efficiency adjustments improved model fit. Estimating total avian mortality is only a first step in developing an assessment of the biological significance of mortality at communication towers for individual species or groups of species. Nevertheless, our estimate can be used to evaluate this source of mortality, develop subsequent per-species mortality estimates, and motivate policy action.

The M Word: Multicollinearity in Multiple Regression.

ERIC Educational Resources Information Center

Morrow-Howell, Nancy

1994-01-01

Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…

[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.

Use of nicotine replacement therapy to reduce or delay smoking but not to quit: prevalence and association with subsequent cessation efforts.

PubMed

Levy, Douglas E; Thorndike, Anne N; Biener, Lois; Rigotti, Nancy A

2007-12-01

To assess the prevalence of nicotine replacement therapy (NRT) use for purposes other than quitting smoking and examine the relation of this non-standard NRT use (NSNRT) with subsequent smoking cessation efforts. A population based cohort study of adult smokers who were interviewed by telephone at baseline (2001-2) and at two year follow-up. The association between NSNRT use to cut down on smoking or to delay smoking before baseline and cessation attempts and smoking outcomes at two year follow-up was assessed using logistic regression to adjust for multiple potential confounding factors. Massachusetts, USA. 1712 adult smokers in Massachusetts who were selected using a random digit dial telephone survey. Quit attempt in 12 months before follow-up, NRT use at quit attempt in 12 months before follow-up, smoking cessation by follow-up, or 50% reduction in cigarettes smoked per day between baseline and follow-up. 18.7% of respondents reported ever having used NSNRT. In a multiple logistic regression analysis, there was no statistically significant association between past NSNRT use and quit attempts (OR(cut down) = 0.89, 95% CI 0.59 to 1.33; OR(delay) = 1.29, 95% CI 0.73 to 2.29), smoking cessation (OR(cut down) = 0.74, 95% CI 0.43 to 1.24; OR(delay) = 1.22, 95% CI 0.60 to 2.50) or 50% reduction in cigarettes smoked per day (OR(cut down) = 0.93, 95% CI 0.62 to 1.38; OR(delay) = 0.80, 95% CI 0.43 to 1.49) at follow-up. Past use of NRT to cut down on cigarettes was associated with use of NRT at a follow-up quit attempt (OR(cut down) = 2.28, 95% CI 1.50 to 3.47) but past use of NRT to delay smoking was not (OR(delay) = 1.25, 95% CI 0.67 to 2.34). Use of NRT for reasons other than quitting smoking may be more common than was previously estimated. This population based survey finds no strong evidence that NRT use for purposes other than quitting smoking is either harmful or helpful.

A matching framework to improve causal inference in interrupted time-series analysis.

PubMed

Linden, Ariel

2018-04-01

Interrupted time-series analysis (ITSA) is a popular evaluation methodology in which a single treatment unit's outcome is studied over time and the intervention is expected to "interrupt" the level and/or trend of the outcome, subsequent to its introduction. When ITSA is implemented without a comparison group, the internal validity may be quite poor. Therefore, adding a comparable control group to serve as the counterfactual is always preferred. This paper introduces a novel matching framework, ITSAMATCH, to create a comparable control group by matching directly on covariates and then use these matches in the outcomes model. We evaluate the effect of California's Proposition 99 (passed in 1988) for reducing cigarette sales, by comparing California to other states not exposed to smoking reduction initiatives. We compare ITSAMATCH results to 2 commonly used matching approaches, synthetic controls (SYNTH), and regression adjustment; SYNTH reweights nontreated units to make them comparable to the treated unit, and regression adjusts covariates directly. Methods are compared by assessing covariate balance and treatment effects. Both ITSAMATCH and SYNTH achieved covariate balance and estimated similar treatment effects. The regression model found no treatment effect and produced inconsistent covariate adjustment. While the matching framework achieved results comparable to SYNTH, it has the advantage of being technically less complicated, while producing statistical estimates that are straightforward to interpret. Conversely, regression adjustment may "adjust away" a treatment effect. Given its advantages, ITSAMATCH should be considered as a primary approach for evaluating treatment effects in multiple-group time-series analysis. © 2017 John Wiley & Sons, Ltd.

Sequence analysis to assess labour market participation following vocational rehabilitation: an observational study among patients sick-listed with low back pain from a randomised clinical trial in Denmark

PubMed Central

Lindholdt, Louise; Labriola, Merete; Nielsen, Claus Vinther; Horsbøl, Trine Allerslev; Lund, Thomas

2017-01-01

Introduction The return-to-work (RTW) process after long-term sickness absence is often complex and long and implies multiple shifts between different labour market states for the absentee. Standard methods for examining RTW research typically rely on the analysis of one outcome measure at a time, which will not capture the many possible states and transitions the absentee can go through. The purpose of this study was to explore the potential added value of sequence analysis in supplement to standard regression analysis of a multidisciplinary RTW intervention among patients with low back pain (LBP). Methods The study population consisted of 160 patients randomly allocated to either a hospital-based brief or a multidisciplinary intervention. Data on labour market participation following intervention were obtained from a national register and analysed in two ways: as a binary outcome expressed as active or passive relief at a 1-year follow-up and as four different categories for labour market participation. Logistic regression and sequence analysis were performed. Results The logistic regression analysis showed no difference in labour market participation for patients in the two groups after 1 year. Applying sequence analysis showed differences in subsequent labour market participation after 2 years after baseline in favour of the brief intervention group versus the multidisciplinary intervention group. Conclusion The study indicated that sequence analysis could provide added analytical value as a supplement to traditional regression analysis in prospective studies of RTW among patients with LBP. PMID:28729315

Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

NASA Technical Reports Server (NTRS)

Kumar, Sricharan; Srivistava, Ashok N.

2012-01-01

Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

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

Relationship between drug abuse and intimate partner violence: a longitudinal study among women receiving methadone.

PubMed

El-Bassel, Nabila; Gilbert, Louisa; Wu, Elwin; Go, Hyun; Hill, Jennifer

2005-03-01

We examined whether frequent drug use increases the likelihood of subsequent sexual or physical intimate partner violence (IPV) and whether IPV increases the likelihood of subsequent frequent drug use. A random sample of 416 women on methadone was assessed at baseline (wave 1) and at 6 months (wave 2), and 12 months (wave 3) following the initial assessment. Propensity score matching and multiple logistic regression were employed. Women who reported frequent crack use at wave 2 were more likely than non-drug using women to report IPV at wave 3 (odds ratio [OR]=4.4; 95% confidence interval [CI]=2.1, 9.1; P<.01), and frequent marijuana users at wave 2 were more likely than non-drug users to report IPV at wave 3 (OR=4.5; 95% CI=2.4, 8.4; P<.01). In addition, women who reported IPV at wave 2 were more likely than women who did not report IPV to indicate frequent heroin use at wave 3 (OR=2.7; 95% CI=1.1, 6.5; P=.04). Our findings suggest that the relationship between frequent drug use and IPV is bidirectional and varies by type of drug.

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

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

PubMed

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

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.

An examination of racial bias in the Beck Depression Inventory-II.

PubMed

Sashidharan, Tracy; Pawlow, Laura A; Pettibone, Jonathan C

2012-04-01

Historically, many psychological measures were developed and standardized based on a primarily Caucasian population. These tests are subsequently applied to minorities and may be inappropriate and possibly even pathologizing. The widely used Beck Depression Inventory-II (BDI-II) was initially standardized on a sample of Caucasian university students and its use with minorities has only recently been investigated. This study examined the possibility of racial bias in the BDI-II by comparing Caucasian and African American Midwestern university students. A hierarchical multiple regression compared the scores of the BDI-II with a similar measure of depression that is standardized for use with African Americans. There was no evidence of racial bias discovered in the BDI-II in this sample. Implications and future directions of research are discussed. PsycINFO Database Record (c) 2012 APA, all rights reserved.

The Moderating Capacity of Racial Identity Between Perceived Discrimination and Psychological Well-being Over Time among African American Youth

PubMed Central

Seaton, Eleanor K.; Upton, Rachel D.; Sellers, Robert M.; Neblett, Enrique W.; Hammond, Wizdom Powell

2011-01-01

The present study examined the influence of racial identity in the longitudinal relationship between perceptions of racial discrimination and psychological well-being for approximately 560 African American youth. Latent curve modeling (LCM) and parallel process multiple-indicator LCMs with latent moderators were used to assess whether perceptions of racial discrimination predicted the intercept (initial levels) and the slope (rate of change) of psychological well-being over time, and whether racial identity moderates these relationships. The results indicated that African American adolescents who reported higher psychological responses to discrimination frequency levels at the first time point had lower initial levels of well-being. Regressing the slope factor for psychological well-being on frequency of discrimination also revealed a non-significant result for subsequent well-being levels. PMID:21954919

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)

Ultrasound-enhanced bioscouring of greige cotton: regression analysis of process factors

USDA-ARS?s Scientific Manuscript database

Process factors of enzyme concentration, time, power and frequency were investigated for ultrasound-enhanced bioscouring of greige cotton. A fractional factorial experimental design and subsequent regression analysis of the process factors were employed to determine the significance of each factor a...

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.

Past and present socioeconomic circumstances and psychotropic medication: a register-linkage study.

PubMed

Mauramo, Elina; Lallukka, Tea; Laaksonen, Mikko; Martikainen, Pekka; Rahkonen, Ossi; Lahelma, Eero

2012-12-01

Various domains of socioeconomic circumstances are associated with self-reported mental health, but we lack evidence from studies using medically confirmed mental health outcomes. This longitudinal study aimed to examine the associations of multiple domains of socioeconomic circumstances with subsequent prescribed psychotropic medication among Finnish public sector employees. Baseline survey data among 40-60-year-old employees of City of Helsinki were linked with Social Insurance Institution of Finland register data on psychotropic medication purchases (n=5563). HRs were calculated using Cox regression to examine associations of parental and own education, childhood and current economic difficulties, occupational class, household income and housing tenure with antidepressants, sleeping pills and sedatives and any psychotropic medication during a 5-year follow-up. In age and previous psychotropic medication adjusted models, the risk of antidepressant medication was higher in those with childhood (women: HR=1.29, men: HR=1.64) and current economic difficulties (women: HR=1.30-1.54), rented housing (women: HR=1.20, men: HR=1.45) and the second lowest income group (men: HR=1.71). Gradual adjustments had little effect on the associations. For sleeping pills and sedatives, similar associations were found in women for current economic difficulties, and in men for housing tenure. Results for any psychotropic medication reflected those observed for antidepressants. Past and present economic difficulties and housing tenure were more important determinants of subsequent psychotropic medication among employees than the conventional socioeconomic determinants. The associations were somewhat inconsistent between the medication groups and the sexes. The results support the importance of examining multiple domains of socioeconomic circumstances simultaneously.

Modeling mountain pine beetle disturbance in Glacier National Park using multiple lines of evidence

USGS Publications Warehouse

Assal, Timothy; Sibold, Jason

2013-01-01

Temperate forest ecosystems are subject to various disturbances which contribute to ecological legacies that can have profound effects on the structure of the ecosystem. Impacts of disturbance can vary widely in extent, duration and severity over space and time. Given that global climate change is expected to increase rates of forest disturbance, an understanding of these events are critical in the interpretation of contemporary forest patterns and those of the near future. We seek to understand the impact of the 1970s mountain pine beetle outbreak on the landscape of Glacier National Park and investigate any connection between this event and subsequent decades of extensive wildfire. The lack of spatially explicit data on the mountain pine beetle disturbance represents a major data gap and inhibits our ability to test for correlations between outbreak severity and fire severity. To overcome this challenge, we utilized multiple lines of evidence to model forest canopy mortality as a proxy for outbreak severity. We used historical aerial and landscape photos, reports, aerial survey data, a six year collection of Landsat imagery and abiotic data in combination with regression analysis. The use of remotely sensed data is critical in large areas where subsequent disturbance (fire) has erased some of the evidence from the landscape. Results indicate that this method is successful in capturing the spatial heterogeneity of the outbreak in a topographically complex landscape. Furthermore, this study provides an example on the use of existing data to reduce levels of uncertainty associated with an historic disturbance.

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.

Which factors predict the time spent answering queries to a drug information centre?

PubMed Central

Reppe, Linda A.; Spigset, Olav

2010-01-01

Objective To develop a model based upon factors able to predict the time spent answering drug-related queries to Norwegian drug information centres (DICs). Setting and method Drug-related queries received at 5 DICs in Norway from March to May 2007 were randomly assigned to 20 employees until each of them had answered a minimum of five queries. The employees reported the number of drugs involved, the type of literature search performed, and whether the queries were considered judgmental or not, using a specifically developed scoring system. Main outcome measures The scores of these three factors were added together to define a workload score for each query. Workload and its individual factors were subsequently related to the measured time spent answering the queries by simple or multiple linear regression analyses. Results Ninety-six query/answer pairs were analyzed. Workload significantly predicted the time spent answering the queries (adjusted R2 = 0.22, P < 0.001). Literature search was the individual factor best predicting the time spent answering the queries (adjusted R2 = 0.17, P < 0.001), and this variable also contributed the most in the multiple regression analyses. Conclusion The most important workload factor predicting the time spent handling the queries in this study was the type of literature search that had to be performed. The categorisation of queries as judgmental or not, also affected the time spent answering the queries. The number of drugs involved did not significantly influence the time spent answering drug information queries. PMID:20922480

Proposing a Tentative Cut Point for the Compulsive Sexual Behavior Inventory

PubMed Central

Storholm, Erik David; Fisher, Dennis G.; Napper, Lucy E.; Reynolds, Grace L.

2015-01-01

Bivariate analyses were utilized in order to identify the relations between scores on the Compulsive Sexual Behavior Inventory (CSBI) and self-report of risky sexual behavior and drug abuse among 482 racially and ethnically diverse men and women. CSBI scores were associated with both risky sexual behavior and drug abuse among a diverse non-clinical sample, thereby providing evidence of criterion-related validity. The variables that demonstrated a high association with the CSBI were subsequently entered into a multiple regression model. Four variables (number of sexual partners in the last 30 days, self-report of trading drugs for sex, having paid for sex, and perceived chance of acquiring HIV) were retained as variables with good model fit. Receiver operating characteristic (ROC) curve analyses were conducted in order to determine the optimal tentative cut point for the CSBI. The four variables retained in the multiple regression model were utilized as exploratory gold standards in order to construct ROC curves. The ROC curves were then compared to one another in order to determine the point that maximized both sensitivity and specificity in the identification of compulsive sexual behavior with the CSBI scale. The current findings suggest that a tentative cut point of 40 may prove clinically useful in discriminating between persons who exhibit compulsive sexual behavior and those who do not. Because of the association between compulsive sexual behavior and HIV, STIs, and drug abuse, it is paramount that a psychometrically sound measure of compulsive sexual behavior is made available to all healthcare professionals working in disease prevention and other areas. PMID:21203814

Proposing a tentative cut point for the Compulsive Sexual Behavior Inventory.

PubMed

Storholm, Erik David; Fisher, Dennis G; Napper, Lucy E; Reynolds, Grace L; Halkitis, Perry N

2011-12-01

Bivariate analyses were utilized in order to identify the relations between scores on the Compulsive Sexual Behavior Inventory (CSBI) and self-report of risky sexual behavior and drug abuse among 482 racially and ethnically diverse men and women. CSBI scores were associated with both risky sexual behavior and drug abuse among a diverse non-clinical sample, thereby providing evidence of criterion-related validity. The variables that demonstrated a high association with the CSBI were subsequently entered into a multiple regression model. Four variables (number of sexual partners in the last 30 days, self-report of trading drugs for sex, having paid for sex, and perceived chance of acquiring HIV) were retained as variables with good model fit. Receiver operating characteristic (ROC) curve analyses were conducted in order to determine the optimal tentative cut point for the CSBI. The four variables retained in the multiple regression model were utilized as exploratory gold standards in order to construct ROC curves. The ROC curves were then compared to one another in order to determine the point that maximized both sensitivity and specificity in the identification of compulsive sexual behavior with the CSBI scale. The current findings suggest that a tentative cut point of 40 may prove clinically useful in discriminating between persons who exhibit compulsive sexual behavior and those who do not. Because of the association between compulsive sexual behavior and HIV, STIs, and drug abuse, it is paramount that a psychometrically sound measure of compulsive sexual behavior is made available to all healthcare professionals working in disease prevention and other areas.

Infectious mononucleosis and multiple sclerosis - Updated review on associated risk.

PubMed

Sheik-Ali, Sharaf

2017-05-01

There has been substantial evidence accumulating on the role of infectious mononucleosis (IM) and the subsequent risk of obtaining Multiple Sclerosis (MS). Up to date studies not previously explored were reviewed by the author to further clarify the association. Medline and Web of Science were searched with no time constraints for articles exploring an association between Multiple Sclerosis and Infectious Mononucleosis. 24 articles were found, totalling 1063 cases and 13,227 cohort/controls. 23/24 (96%) articles reported a significant association of Infectious Mononucleosis on the risk of subsequent multiple sclerosis. Overall, new literature on IM and risk of MS categorically supports the association. Future work should focus on other risk factors such as age and gender on IM and subsequent risk of MS. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

OS087. Maternal characteristics, mean arterial pressure and PLGF in early prediction of preeclampsia.

PubMed

Kuc, S; Koster, M P; Franx, A; Schielen, P C; Visser, G H

2012-07-01

In a previous study, we described the predictive value of first-trimester pregnancy-associated plasma protein-A (PAPP-A), free beta-subunit of human chorionic gonadotrophin (fb-hCG), Placental Growth Factor (PlGF) and A Desintegrin And Metalloproteinase 12 (ADAM12) for early onset preeclampsia (delivery <34 weeks) [1]. The objective of the current study was to obtain the predictive value of these serum makers, for both early onset PE (EOPE) and late onset PE (LOPE), combined with maternal characteristics and first-trimester maternal mean arterial blood pressure (MAP). This was a nested case-control study, using stored first-trimester maternal serum from 167 women who subsequently developed PE, and 500 uncomplicated singleton pregnancies which resulted in a live birth =>37 weeks. Maternal characteristics (i.e. medical records, parity, weight, length) MAP and pregnancy outcome (i.e. gestational age at delivery, birthweight, fetal sex) were collected for each individual and used to calculate prior risks for PE in a multiple logistic regression model. MAP values and marker levels of PAPP-A, fb-hCG, PlGF and ADAM12 were expressed as multiples of the gestation-specific normal median (MoMs). Subsequently, MoMs were log-transformed and compared between PE and controls using Student's t-tests. Posterior risks were calculated using different combinations of variables;(1) maternal characteristics, serum markers, and MAP separately (2) maternal characteristics combined with serum markers or MAP (3) maternal characteristics combined with serum markers and MAP. The model-predicted detection rates (DR) for fixed 10% false-positive rates were obtained for EOPE and LOPE with or without intra-uterine growth restriction (IUGR,birth weight <10th centile). The maternal characteristics: maternal age, weight, length, smoking status and nulliparity were discriminative between PE and control groups and therefore incorporated in the multiple logistic regression model. MoM MAP was significantly elevated (1.10 p<0.001; 1.07 p<0.001) and MoM PlGF was significantly reduced (0.95 p=0.016; 0.90 p=0.029) in the EOPE and LOPE group, respectively. The differences in markers for IUGR groups were larger. The estimated DRs of the three different models are presented in the table. This study demonstrates that first-trimester MAP and PlGF combined with maternal characteristics are promising markers in risk assessment for PE. Combination of markers proved especially useful for risk assessment for term PE. Detection rates were higher in the presence of IUGR. Copyright © 2012. Published by Elsevier B.V.

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)

Fewer acute respiratory infection episodes among patients receiving treatment for gastroesophageal reflux disease

PubMed Central

Xirasagar, Sudha; Chung, Shiu-Dong; Tsai, Ming-Chieh; Chen, Chao-Hung

2017-01-01

Patients with gastroesophageal reflux disease (GERD) present with comorbid complications with implications for healthcare utilization. To date, little is known about the effects of GERD treatment with a proton-pump inhibitor (PPI) on patients’ subsequent healthcare utilization for acute respiratory infections (ARIs). This population-based study compared ARI episodes captured through outpatient visits, one year before and one year after GERD patients received PPI treatment. We used retrospective data from the Longitudinal Health Insurance Database 2005 in Taiwan, comparing 21,486 patients diagnosed with GERD from 2010 to 2012 with 21,486 age-sex matched comparison patients without GERD. Annual ARI episodes represented by ambulatory care visits for ARI (visits during a 7-day period bundled into one episode), were compared between the patient groups during the 1-year period before and after the index date (date of GERD diagnosis for study patients, first ambulatory visit in the same year for their matched comparison counterpart). Multiple regression analysis using a difference-in-difference approach was performed to estimate the adjusted association between GERD treatment and the subsequent annual ARI rate. We found that the mean annual ARI episode rate among GERD patients reduced by 11.4%, from 4.39 before PPI treatment, to 3.89 following treatment (mean change = -0.5 visit, 95% confidence interval (CI) = (-0.64, -0.36)). In Poisson regression analysis, GERD treatment showed an independent association with the annual ARI rate, showing a negative estimate (with p<0.001). The study suggests that GERD treatment with PPIs may help reduce healthcare visits for ARIs, highlighting the importance of treatment-seeking by GERD patients and compliance with treatment. PMID:28222168

Examination of the Effect of Implantable Cardioverter-Defibrillators on Health-Related Quality of Life

PubMed Central

Noyes, Katia; Corona, Ethan; Veazie, Peter; Dick, Andrew W.; Zhao, Hongwei; Moss, Arthur J.

2015-01-01

Background While implantable cardioverter-defibrillators (ICDs) improve survival, their benefit in terms of health-related quality of life (HRQOL) is negligible. Objective To examine how shocks and congestive heart failure (CHF) mediate the effect of ICDs on HRQOL. Methods The US patients from the MADIT-II (Multicenter Automatic Defibrillator Trial-II) trial (n = 983) were randomized to receive an ICD or medical treatment only. HRQOL was assessed using the Health Utility Index 3 at baseline and 3, 12, 24, and 36 months following randomization. Logistic regressions were used to test for the effect of ICDs on the CHF indicator, and linear regressions were used to examine the effect of ICD shocks and CHF on HRQOL in living patients. We used a Monte Carlo simulation and a parametric Weibull distribution survival model to test for the effect of selective attrition. Observations were clustered by patients and robust standard errors (RSEs) were used to control for the non-independence of multiple observations provided by the same patient. Results Patients in the ICD arm had 41% higher odds of experiencing CHF since their last assessment compared with those in the control arm (RSE = 0.19, p = 0.01). Developing CHF reduced HRQOL at the subsequent visit by 0.07 (p < 0.01). Having ICD shocks reduced overall HRQOL by 0.04 (p = 0.04) at the subsequent assessment. The negative effect of ICD firing on HRQOL was an order of magnitude greater than the effect of CHF. Conclusions A higher prevalence of CHF and shocks among patients with ICDs and their negative effect on HRQOL may partially explain the lack of HRQOL benefit of ICD therapy. PMID:19929037

The Effects of Optimism and Gratitude on Physical Activity, Biomarkers, and Readmissions Following an Acute Coronary Syndrome: The Gratitude Research in Acute Coronary Events (GRACE) Study

PubMed Central

Huffman, Jeff C.; Beale, Eleanor E.; Celano, Christopher M.; Beach, Scott R.; Belcher, Arianna M.; Moore, Shannon V.; Suarez, Laura; Motiwala, Shweta R.; Gandhi, Parul U.; Gaggin, Hanna; Januzzi, James L.

2015-01-01

Background Positive psychological constructs, such as optimism, are associated with beneficial health outcomes. However, no study has separately examined the effects of multiple positive psychological constructs on behavioral, biological, and clinical outcomes after an acute coronary syndrome (ACS). Accordingly, we aimed to investigate associations of baseline optimism and gratitude with subsequent physical activity, prognostic biomarkers, and cardiac rehospitalizations in post-ACS patients. Methods and Results Participants were enrolled during admission for ACS and underwent assessments at baseline (2 weeks post-ACS) and follow-up (6 months later). Associations between baseline positive psychological constructs and subsequent physical activity/biomarkers were analyzed using multivariable linear regression. Associations between baseline positive constructs and 6-month rehospitalizations were assessed via multivariable Cox regression. Overall, 164 participants enrolled and completed the baseline 2-week assessments. Baseline optimism was significantly associated with greater physical activity at 6 months (n=153; β=102.5; 95% confidence interval [13.6-191.5]; p=.024), controlling for baseline activity and sociodemographic, medical, and negative psychological covariates. Baseline optimism was also associated with lower rates of cardiac readmissions at 6 months (N=164), controlling for age, gender, and medical comorbidity (hazard ratio=.92; 95% confidence interval [.86-.98]; p=.006). There were no significant relationships between optimism and biomarkers. Gratitude was minimally associated with post-ACS outcomes. Conclusions Post-ACS optimism, but not gratitude, was prospectively and independently associated with superior physical activity and fewer cardiac readmissions. Whether interventions that target optimism can successfully increase optimism or improve cardiovascular outcomes in post-ACS patients is not yet known, but can be tested in future studies. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT01709669. PMID:26646818

Patient Satisfaction, Treatment Experience, and Disability Outcomes in a Population-Based Cohort of Injured Workers in Washington State: Implications for Quality Improvement

PubMed Central

Wickizer, Thomas M; Franklin, Gary; Fulton-Kehoe, Deborah; Turner, Judith A; Mootz, Robert; Smith-Weller, Terri

2004-01-01

Objective To determine what aspects of patient satisfaction are most important in explaining the variance in patients' overall treatment experience and to evaluate the relationship between treatment experience and subsequent outcomes. Data Sources and Setting Data from a population-based survey of 804 randomly selected injured workers in Washington State filing a workers' compensation claim between November 1999 and February 2000 were combined with insurance claims data indicating whether survey respondents were receiving disability compensation payments for being out of work at 6 or 12 months after claim filing. Study Design We conducted a two-step analysis. In the first step, we tested a multiple linear regression model to assess the relationship of satisfaction measures to patients' overall treatment experience. In the second step, we used logistic regression to assess the relationship of treatment experience to subsequent outcomes. Principal Findings Among injured workers who had ongoing follow-up care after their initial treatment (n=681), satisfaction with interpersonal and technical aspects of care and with care coordination was strongly and positively associated with overall treatment experience (p<0.001). As a group, the satisfaction measures explained 38 percent of the variance in treatment experience after controlling for demographics, satisfaction with medical care prior to injury, job satisfaction, type of injury, and provider type. Injured workers who reported less-favorable treatment experience were 3.54 times as likely (95 percent confidence interval, 1.20–10.95, p=.021) to be receiving time-loss compensation for inability to work due to injury 6 or 12 months after filing a claim, compared to patients whose treatment experience was more positive. PMID:15230925

Post-infectious irritable bowel syndrome (PI-IBS) after infection with Shiga-like toxin-producing Escherichia coli (STEC) O104:H4: A cohort study with prospective follow-up.

PubMed

Andresen, Viola; Löwe, Bernd; Broicher, Wiebke; Riegel, Björn; Fraedrich, Katharina; von Wulffen, Moritz; Gappmayer, Kerrin; Wegscheider, Karl; Treszl, András; Rose, Matthias; Layer, Peter; Lohse, Ansgar W

2016-02-01

In May/June 2011, the new Shiga-like toxin-producing Escherichia coli (STEC) strain O104:H4 caused the severest outbreak ever recorded of hemorrhagic enterocolitis in 3842 patients in Germany. As bacterial enterocolitis is an established risk factor of subsequent irritable bowel syndrome (IBS), we aimed to estimate prevalence and incidence of post-infectious (PI)-IBS after six and 12 months in a cohort of STEC O104:H4 patients and to prospectively identify associated somatic and psychometric risk factors. A total of 389 patients were studied prospectively at baseline and at six and 12 months after STEC infection using STEC disease-related questionnaires and validated instruments for IBS (Rome III) and psychological factors. Frequencies and logistic regression models using multiple imputations were applied to assess predictor variables. Prevalence of IBS increased from 9.8% prior to STEC infection to 23.6% at six and 25.3% at 12 months after STEC infection. In patients without IBS symptoms prior to STEC infection, incidence of new IBS was 16.9%. Logistic regression models indicated higher somatization and anxiety scores as risk factors for, and mesalazine treatment during, STEC infection as the only significant protective factor against IBS. No other factor analyzed, including disease severity, showed an association. PI-IBS rates following this unusually severe STEC outbreak were similar to what has been observed after other infectious gastroenteritis outbreaks. Our findings suggest that mesalazine may have reduced the risk of subsequent PI-IBS. As altered mucosal immune activity is a pivotal pathogenic factor in PI-IBS, our observation of a potential protective effect of mesalazine might be explained by its known modulatory action on mucosal immunity, and may warrant further investigation.

Effects of Optimism and Gratitude on Physical Activity, Biomarkers, and Readmissions After an Acute Coronary Syndrome: The Gratitude Research in Acute Coronary Events Study.

PubMed

Huffman, Jeff C; Beale, Eleanor E; Celano, Christopher M; Beach, Scott R; Belcher, Arianna M; Moore, Shannon V; Suarez, Laura; Motiwala, Shweta R; Gandhi, Parul U; Gaggin, Hanna K; Januzzi, James L

2016-01-01

Positive psychological constructs, such as optimism, are associated with beneficial health outcomes. However, no study has separately examined the effects of multiple positive psychological constructs on behavioral, biological, and clinical outcomes after an acute coronary syndrome (ACS). Accordingly, we aimed to investigate associations of baseline optimism and gratitude with subsequent physical activity, prognostic biomarkers, and cardiac rehospitalizations in post-ACS patients. Participants were enrolled during admission for ACS and underwent assessments at baseline (2 weeks post-ACS) and follow-up (6 months later). Associations between baseline positive psychological constructs and subsequent physical activity/biomarkers were analyzed using multivariable linear regression. Associations between baseline positive constructs and 6-month rehospitalizations were assessed via multivariable Cox regression. Overall, 164 participants enrolled and completed the baseline 2-week assessments. Baseline optimism was significantly associated with greater physical activity at 6 months (n=153; β=102.5; 95% confidence interval, 13.6-191.5; P=0.024), controlling for baseline activity and sociodemographic, medical, and negative psychological covariates. Baseline optimism was also associated with lower rates of cardiac readmissions at 6 months (n=164), controlling for age, sex, and medical comorbidity (hazard ratio, 0.92; 95% confidence interval, [0.86-0.98]; P=0.006). There were no significant relationships between optimism and biomarkers. Gratitude was minimally associated with post-ACS outcomes. Post-ACS optimism, but not gratitude, was prospectively and independently associated with superior physical activity and fewer cardiac readmissions. Whether interventions that target optimism can successfully increase optimism or improve cardiovascular outcomes in post-ACS patients is not yet known, but can be tested in future studies. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01709669. © 2015 American Heart Association, Inc.

The effect of multiple primary rules on cancer incidence rates and trends

PubMed Central

Weir, Hannah K.; Johnson, Christopher J.; Ward, Kevin C.; Coleman, Michel P.

2018-01-01

Purpose An examination of multiple primary cancers can provide insight into the etiologic role of genes, the environment, and prior cancer treatment on a cancer patient’s risk of developing a subsequent cancer. Different rules for registering multiple primary cancers (MP) are used by cancer registries throughout the world making data comparisons difficult. Methods We evaluated the effect of SEER and IARC/IACR rules on cancer incidence rates and trends using data from the SEER Program. We estimated age-standardized incidence rate (ASIR) and trends (1975–2011) for the top 26 cancer categories using joinpoint regression analysis. Results ASIRs were higher using SEER compared to IARC/IACR rules for all cancers combined (3 %) and, in rank order, melanoma (9 %), female breast (7 %), urinary bladder (6 %), colon (4 %), kidney and renal pelvis (4 %), oral cavity and pharynx (3 %), lung and bronchus (2 %), and non-Hodgkin lymphoma (2 %). ASIR differences were largest for patients aged 65+ years. Trends were similar using both MP rules with the exception of cancers of the urinary bladder, and kidney and renal pelvis. Conclusions The choice of multiple primary coding rules effects incidence rates and trends. Compared to SEER MP coding rules, IARC/IACR rules are less complex, have not changed over time, and report fewer multiple primary cancers, particularly cancers that occur in paired organs, at the same anatomic site and with the same or related histologic type. Cancer registries collecting incidence data using SEER rules may want to consider including incidence rates and trends using IARC/IACR rules to facilitate international data comparisons. PMID:26809509

Inherited genetic variants associated with occurrence of multiple primary melanoma.

PubMed

Gibbs, David C; Orlow, Irene; Kanetsky, Peter A; Luo, Li; Kricker, Anne; Armstrong, Bruce K; Anton-Culver, Hoda; Gruber, Stephen B; Marrett, Loraine D; Gallagher, Richard P; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J; Cust, Anne E; Ollila, David W; Begg, Colin B; Berwick, Marianne; Thomas, Nancy E

2015-06-01

Recent studies, including genome-wide association studies, have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 SNPs from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% confidence intervals were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma, while NCOA6 rs4911442 approached significance (P = 0.06). The GEM Study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. ©2015 American Association for Cancer Research.

Inherited genetic variants associated with occurrence of multiple primary melanoma

PubMed Central

Gibbs, David C.; Orlow, Irene; Kanetsky, Peter A.; Luo, Li; Kricker, Anne; Armstrong, Bruce K.; Anton-Culver, Hoda; Gruber, Stephen B.; Marrett, Loraine D.; Gallagher, Richard P.; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J.; Cust, Anne E.; Ollila, David W.; Begg, Colin B.; Berwick, Marianne; Thomas, Nancy E.

2015-01-01

Recent studies including genome-wide association studies have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 single nucleotide polymorphisms (SNP) from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% CIs were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma while NCOA6 rs4911442 approached significance (P = 0.06). The GEM study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. PMID:25837821

Too attractive: the growing problem of magnet ingestions in children.

PubMed

Brown, Julie C; Otjen, Jeffrey P; Drugas, George T

2013-11-01

Small, powerful magnets are increasingly available in toys and other products and pose a health risk. Small spherical neodymium magnets marketed since 2008 are of particular concern. The objective of this study was to determine the incidence, characteristics, and management of single and multiple-magnet ingestions over time. Magnet ingestion cases at a tertiary children's hospital were identified using radiology reports from June 2002 to December 2012. Cases were verified by chart and imaging review. Relative risk regressions were used to determine changes in the incidence of ingestions and interventions over time. Of 56 cases of magnet ingestion, 98% occurred in 2006 or later, and 57% involved multiple magnets. Median age was 8 years (range, 0-18 years). Overall, 21% of single and 88% of multiple ingestions had 2 or more imaging series obtained, whereas no single and 56.3% of multiple ingestions required intervention (25.0% endoscopy, 18.8% surgery, 12.5% both). Magnet ingestions increased in 2010 to 2012 compared with 2007 to 2009 (relative risk, 1.9; 95% confidence interval, 1.2-3.0). Small, spherical magnets likely from magnet sets comprised 27% of ingestions, all ingested 2010 or later: 86% involved multiple magnets, 50% of which required intervention. Excluding these cases, ingestions of other magnets did not increase in 2010 to 2012 compared with 2007 to 2009 (relative risk, 0.94; 95% confidence interval, 0.6-1.4). The incidence of pediatric magnet ingestions and subsequent interventions has increased over time. Multiple-magnet ingestions result in high utilization of radiological imaging and surgical interventions. Recent increases parallel the increased availability of small, spherical magnet sets. Young and at-risk children should not have access to these and other small magnets. Improved regulation and magnet safety standards are needed.

Associations between content types of early media exposure and subsequent attentional problems.

PubMed

Zimmerman, Frederick J; Christakis, Dimitri A

2007-11-01

Television and video/DVD viewing among very young children has become both pervasive and heavy. Previous studies have reported an association between early media exposure and problems with attention regulation but did not have data on the content type that children watched. We tested the hypothesis that early television viewing of 3 content types is associated with subsequent attentional problems. The 3 different content types are educational, nonviolent entertainment, and violent entertainment. Participants were children in a nationally representative sample collected in 1997 and reassessed in 2002. The analysis was a logistic regression of a high score on a validated parent-reported measure of attentional problems, regressed on early television exposure by content and several important sociodemographic control variables. Viewing of educational television before age 3 was not associated with attentional problems 5 years later. However, viewing of either violent or non-violent entertainment television before age 3 was significantly associated with subsequent attentional problems, and the magnitude of the association was large. Viewing of any content type at ages 4 to 5 was not associated with subsequent problems. The association between early television viewing and subsequent attentional problems is specific to noneducational viewing and to viewing before age 3.

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).

[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.

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

PubMed

Slinker, B K; Glantz, S A

1985-07-01

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

Influence of maternal age, birth-to-conception intervals and prior perinatal factors on perinatal outcomes.

PubMed

Farahati, M; Bozorgi, N; Luke, B

1993-10-01

This study evaluated the influence of prior perinatal factors on birth weight, length of gestation, and maternal pregravid and postpartum weights in subsequent pregnancies. The study sample included 47 women each with first, second and third pregnancies. Mean pregravid weight increased by 5.2 lb between the first and second pregnancies and by 4.4 lb between the second and third pregnancies. Total weight gain averaged 31 lb for the first pregnancy and 28.4 and 28.3 lb for the second and third pregnancies, respectively. Mean birth weight increased by 111 g between the first and second pregnancies and by 199 g between the second and third pregnancies. Mean gestational age was similar for all three pregnancies, averaging 39.5 weeks. Using stepwise forward multiple regression analyses, we determined that birth weight and length of gestation are both influenced significantly by prior birth weight and length of gestation; subsequent pregravid weight is influenced significantly by prior rate of gain, pregravid weight and postpartum weight; and postpartum weight is significantly influenced by prior rate of gain and birth weight. Comparisons across three pregnancies for the same woman showed that differences in birth-to-conception interval were not associated with higher postpartum weight or subsequent pregravid weight. These data indicate that in healthy, nonsmoking, low-risk women, the maternal and infant outcomes of pregnancies are significantly influenced by prior outcomes but not by either short birth-to-conception interval or greater maternal age.

Objective Structured Assessment of Technical Skills (OSATS) evaluation of hysteroscopy training: a prospective study.

PubMed

Alici, Ferizan; Buerkle, Bernd; Tempfer, Clemens B

2014-07-01

To describe the performance curve of hysteroscopy-naïve probands repeatedly working through a surgery algorithm on a hysteroscopy trainer. We prospectively recruited medical students to a 30min demonstration session teaching a standardized surgery algorithm. Subjects subsequently performed three training courses immediately after training (T1) and after 24h (T2) and 48h (T3). Skills were recorded with a 20-item Objective Structured Assessment of Technical Skills (OSATS) at T1, T2, and T3. The presence of a sustained OSATS score improvement from T1 to T3 was the primary outcome. Performance time (PT) and self assessment (SA) were secondary outcomes. Statistics were performed using paired T-test and multiple linear regression analysis. 92 subjects were included. OSATS scores significantly improved over time from T1 to T2 (15.21±1.95 vs. 16.02±2.06, respectively; p<0.0001) and from T2 to T3 (16.02±2.06 vs. 16.95±1.61, respectively; p<0.0001). The secondary outcomes PT (414±119s vs. 357±88s vs. 304±91s; p<0.0001) and SA (3.02±0.85 vs. 3.80±0.76 vs. 4.41±0.67; p<0.0001) also showed an improvement over time with quicker performance and higher confidence. SA, but not PT demonstrated construct validity. In a multiple linear regression analysis, gender (odds ratio (OR) 0.96; 95% confidence interval (CI) 0.35-2.71; p=0.9) did not independently influence the likelihood of OSATS score improvement. In a hysteroscopy-naïve population, there is a continuous and sustained improvement of surgical proficiency and confidence after multiple training courses on a hysteroscopy trainer. Serial hysteroscopy trainings may be helpful for teaching hysteroscopy skills. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

PubMed

Barzegar, Rahim; Moghaddam, Asghar Asghari; Deo, Ravinesh; Fijani, Elham; Tziritis, Evangelos

2018-04-15

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO 3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO 3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model. Copyright © 2017 Elsevier B.V. All rights reserved.

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…

Both Low Temperature and Shorter Duration of Food Availability Delay Testicular Regression and Affect the Daily Cycle in Body Temperature in a Songbird.

PubMed

Dawson, Alistair

Photoperiodic control of reproduction in birds is based on two processes, a positive effect leading to gonadal maturation and an inhibitory effect subsequently inducing regression. Nonphotoperiodic cues can modulate photoperiodic control, particularly the inhibitory process. In previous studies of common starlings (Sturnus vulgaris), (1) restriction of food availability to 8 h after dawn had little effect on testicular maturation but dramatically delayed subsequent regression and (2) lower ambient temperature also had little effect during maturation but delayed regression. Could the effects of food restriction and temperature share a common underlying mechanism? Four groups of starlings were kept on a simulated natural cycle in photoperiod in a 2 × 2 factorial experimental design. Two groups were held under an ambient temperature of 16°C, and the other two were held under 6°C. One of each of these groups had food provided ad lib., and in the other two groups access to food was denied 7 h after dawn. In both the ad lib. food groups and the food-restricted groups, lower temperature had little effect on testicular maturation but delayed subsequent regression and molt. In both the 16°C groups and the 6°C groups, food restriction had no effect on testicular maturation but delayed regression and molt. The daily cycle in body temperature was recorded in all groups when the photoperiod had reached 12L∶12D, the photoperiod at which regression is initiated. In both 6°C groups, nighttime body temperature was lower than in the 16°C groups, a characteristic of shorter photoperiods. In the two ad lib. food groups high daytime temperature was maintained until dusk, whereas in the two food-restricted groups body temperature began to decrease after food withdrawal. Thus, both lower temperature and food restriction delayed regression, as if the photoperiod was shorter than it actually was, and both resulted in daily cycles in body temperature that reflected cycles under shorter photoperiods. This implies that the daily cycle in body temperature is possibly a common pathway through which nonphotoperiodic cues may operate.

An application of the Health Action Process Approach model to oral hygiene behaviour and dental plaque in adolescents with fixed orthodontic appliances.

PubMed

Scheerman, Janneke F M; van Empelen, Pepijn; van Loveren, Cor; Pakpour, Amir H; van Meijel, Berno; Gholami, Maryam; Mierzaie, Zaher; van den Braak, Matheus C T; Verrips, Gijsbert H W

2017-11-01

The Health Action Process Approach (HAPA) model addresses health behaviours, but it has never been applied to model adolescents' oral hygiene behaviour during fixed orthodontic treatment. This study aimed to apply the HAPA model to explain adolescents' oral hygiene behaviour and dental plaque during orthodontic treatment with fixed appliances. In this cross-sectional study, 116 adolescents with fixed appliances from an orthodontic clinic situated in Almere (the Netherlands) completed a questionnaire assessing oral health behaviours and the psychosocial factors of the HAPA model. Linear regression analyses were performed to examine the factors associated with dental plaque, toothbrushing, and the use of a proxy brush. Stepwise regression analysis showed that lower amounts of plaque were significantly associated with higher frequency of the use of a proxy brush (R 2 = 45%), higher intention of the use of a proxy brush (R 2 = 5%), female gender (R 2 = 2%), and older age (R 2 = 2%). The multiple regression analyses revealed that higher action self-efficacy, intention, maintenance self-efficacy, and a higher education were significantly associated with the use of a proxy brush (R 2 = 45%). Decreased levels of dental plaque are mainly associated with increased use of a proxy brush that is subsequently associated with a higher intention and self-efficacy to use the proxy brush. © 2017 BSPD, IAPD and John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Sequence analysis to assess labour market participation following vocational rehabilitation: an observational study among patients sick-listed with low back pain from a randomised clinical trial in Denmark.

PubMed

Lindholdt, Louise; Labriola, Merete; Nielsen, Claus Vinther; Horsbøl, Trine Allerslev; Lund, Thomas

2017-07-20

The return-to-work (RTW) process after long-term sickness absence is often complex and long and implies multiple shifts between different labour market states for the absentee. Standard methods for examining RTW research typically rely on the analysis of one outcome measure at a time, which will not capture the many possible states and transitions the absentee can go through. The purpose of this study was to explore the potential added value of sequence analysis in supplement to standard regression analysis of a multidisciplinary RTW intervention among patients with low back pain (LBP). The study population consisted of 160 patients randomly allocated to either a hospital-based brief or a multidisciplinary intervention. Data on labour market participation following intervention were obtained from a national register and analysed in two ways: as a binary outcome expressed as active or passive relief at a 1-year follow-up and as four different categories for labour market participation. Logistic regression and sequence analysis were performed. The logistic regression analysis showed no difference in labour market participation for patients in the two groups after 1 year. Applying sequence analysis showed differences in subsequent labour market participation after 2 years after baseline in favour of the brief intervention group versus the multidisciplinary intervention group. The study indicated that sequence analysis could provide added analytical value as a supplement to traditional regression analysis in prospective studies of RTW among patients with LBP. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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.

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

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.

Early Mother-Child Separation, Parenting, and Child Well-Being in Early Head Start Families

PubMed Central

Howard, Kimberly; Martin, Anne; Berlin, Lisa J.; Brooks-Gunn, Jeanne

2011-01-01

Drawing on theories of attachment and family instability, this study examined associations between early mother-child separation and subsequent maternal parenting behaviors and children’s outcomes in a sample of 2080 families who participated in the Early Head Start Research and Evaluation Project, the vast majority of whom were poor. Multiple regression models revealed that, controlling for baseline family and maternal characteristics and indicators of family instability, the occurrence of a mother-child separation of a week or longer within the first two years of life was related to higher levels of child negativity (at age 3) and aggression (at ages 3 and 5). The effect of separation on child aggression at age 5 was mediated by aggression at age 3, suggesting that the effects of separation on children’s aggressive behavior are early and persistent. PMID:21240692

Aesthetic valence of visual illusions

PubMed Central

Stevanov, Jasmina; Marković, Slobodan; Kitaoka, Akiyoshi

2012-01-01

Visual illusions constitute an interesting perceptual phenomenon, but they also have an aesthetic and affective dimension. We hypothesized that the illusive nature itself causes the increased aesthetic and affective valence of illusions compared with their non-illusory counterparts. We created pairs of stimuli. One qualified as a standard visual illusion whereas the other one did not, although they were matched in as many perceptual dimensions as possible. The phenomenal quality of being an illusion had significant effects on “Aesthetic Experience” (fascinating, irresistible, exceptional, etc), “Evaluation” (pleasant, cheerful, clear, bright, etc), “Arousal” (interesting, imaginative, complex, diverse, etc), and “Regularity” (balanced, coherent, clear, realistic, etc). A subsequent multiple regression analysis suggested that Arousal was a better predictor of Aesthetic Experience than Evaluation. The findings of this study demonstrate that illusion is a phenomenal quality of the percept which has measurable aesthetic and affective valence. PMID:23145272

Exploitation of the Apoptosis-Primed State of MYCN-Amplified Neuroblastoma to Develop a Potent and Specific Targeted Therapy Combination

PubMed Central

Ham, Jungoh; Costa, Carlotta; Sano, Renata; Lochmann, Timothy L.; Sennott, Erin M.; Patel, Neha U.; Dastur, Anahita; Gomez-Caraballo, Maria; Krytska, Kateryna; Hata, Aaron N.; Floros, Konstantinos V.; Hughes, Mark T.; Jakubik, Charles T.; Heisey, Daniel A.R.; Ferrell, Justin T.; Bristol, Molly L.; March, Ryan J.; Yates, Craig; Hicks, Mark A.; Nakajima, Wataru; Gowda, Madhu; Windle, Brad E.; Dozmorov, Mikhail G.; Garnett, Mathew J.; McDermott, Ultan; Harada, Hisashi; Taylor, Shirley M.; Morgan, Iain M.; Benes, Cyril H.; Engelman, Jeffrey A.; Mossé, Yael P.; Faber, Anthony C.

2016-01-01

Summary Fewer than half of children with high-risk neuroblastoma survive. Many of these tumors harbor high-level amplification of MYCN, which correlates with poor disease outcome. Using data from our large drug screen we predicted, and subsequently demonstrated, that MYCN-amplified neuroblastomas are sensitive to the BCL-2 inhibitor ABT-199. This sensitivity occurs in part through low anti-apoptotic BCL-xL expression, high pro-apoptotic NOXA expression, and paradoxical, MYCN-driven upregulation of NOXA. Screening for enhancers of ABT-199 sensitivity in MYCN-amplified neuroblastomas, we demonstrate that the Aurora Kinase A inhibitor MLN8237 combines with ABT-199 to induce widespread apoptosis. In diverse models of MYCN-amplified neuroblastoma, including a patient-derived xenograft model, this combination uniformly induced tumor shrinkage, and in multiple instances led to complete tumor regression. PMID:26859456

Long-term treatment of an addictive personality.

PubMed

Seymour, Peter M

2003-01-01

There is infrequent discussion of long-term psychotherapy of persons with addiction, particularly in the self-psychology literature. In addition, some question whether long-term psychotherapy can be helpful in severe psychiatric disorders. The author describes the treatment of a woman with multiple diagnoses, including bulimia and alcohol and drug addiction, which took place over a period of almost 7 years. These issues are addressed from a self-psychological perspective, with progression of the treatment from early facilitation of a selfobject transference to more intense selfobject transference-countertransference states. Behavioral interventions (e.g., recommendation of inpatient chemical dependency treatment) are also discussed. The author describes the patient's dramatic progress and subsequent regression. Finally, there is a discussion of the addiction from self-psychological and biological perspectives of this woman's particular developmental and treatment issues, as well as a discussion of the confrontation and limit setting in a self-psychologically oriented treatment.

Psychological Factors and Alcohol Use in Problematic Mobile Phone Use in the Spanish Population

PubMed Central

De-Sola, José; Talledo, Hernán; Rubio, Gabriel; de Fonseca, Fernando Rodríguez

2017-01-01

This research aims to study the existing relationships among the factors of state anxiety, depression, impulsivity, and alcohol consumption regarding problematic mobile phone use, as assessed by the Mobile Phone Problem Use Scale. The study was conducted among 1,126 participants recruited among the general Spanish population, aged 16–65 years, by assessing the predictive value of these variables regarding this problematic use. Initially tobacco use was also considered being subsequently refused because of the low internal consistency of the scale used. In general terms, the results show that this problematic use is mainly related to state anxiety and impulsivity, through the dimensions of Positive and Negative Urgency. Considering its predictive value, multiple regression analysis reveals that state anxiety, positive and negative urgency, and alcohol consumption may predict problematic mobile phone use, ruling out the influence of depression. PMID:28217101

Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses.

PubMed

Khanfar, Mohammad A; Banat, Fahmy; Alabed, Shada; Alqtaishat, Saja

2017-02-01

High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an [Formula: see text] value of 237 nM.

Inducible transgenics. New lessons on events governing the induction and commitment in mammary tumorigenesis.

PubMed

Hulit, J; Di Vizio, D; Pestell, R G

2001-01-01

Breast cancer arises from multiple genetic events that together contribute to the established, irreversible malignant phenotype. The development of inducible tissue-specific transgenics has allowed a careful dissection of the events required for induction and subsequent maintenance of tumorigenesis. Mammary gland targeted expression of oncogenic Ras or c-Myc is sufficient for the induction of mammary gland tumorigenesis in the rodent, and when overexpressed together the rate of tumor onset is substantially enhanced. In an exciting recent finding, D'Cruz et al discovered tetracycline-regulated c-Myc overexpression in the mammary gland induced invasive mammary tumors that regressed upon withdrawal of c-Myc expression. Almost one-half of the c-Myc-induced tumors harbored K-ras or N-ras gene point mutations, correlating with tumor persistence on withdrawal of c-Myc transgene expression. These findings suggest maintenance of tumorigenesis may involve a second mutation within the Ras pathway.

Psychological Factors and Alcohol Use in Problematic Mobile Phone Use in the Spanish Population.

PubMed

De-Sola, José; Talledo, Hernán; Rubio, Gabriel; de Fonseca, Fernando Rodríguez

2017-01-01

This research aims to study the existing relationships among the factors of state anxiety, depression, impulsivity, and alcohol consumption regarding problematic mobile phone use, as assessed by the Mobile Phone Problem Use Scale. The study was conducted among 1,126 participants recruited among the general Spanish population, aged 16-65 years, by assessing the predictive value of these variables regarding this problematic use. Initially tobacco use was also considered being subsequently refused because of the low internal consistency of the scale used. In general terms, the results show that this problematic use is mainly related to state anxiety and impulsivity, through the dimensions of Positive and Negative Urgency. Considering its predictive value, multiple regression analysis reveals that state anxiety, positive and negative urgency, and alcohol consumption may predict problematic mobile phone use, ruling out the influence of depression.

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…

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.

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)

MADRS symptom subtypes in ECT-treated depressed patients: relationship to response and subsequent ECT.

PubMed

Spashett, Renee; Fernie, Gordon; Reid, Ian C; Cameron, Isobel M

2014-09-01

This study aimed to explore the relationship of Montgomery-Åsberg Depression Rating Scale (MADRS) symptom subtypes with response to electroconvulsive therapy (ECT) and subsequent ECT treatment within 12 months. A consecutive sample of 414 patients with depression receiving ECT in the North East of Scotland was assessed by retrospective chart review. Response rate was defined as greater than or equal to 50% decrease in pretreatment total MADRS score or a posttreatment total MADRS less than or equal to 10. Principal component analyses were conducted on a sample with psychotic features (n = 124) and a sample without psychotic features (n = 290). Scores on extracted factor subscales, clinical and demographic characteristics were assessed for association with response and subsequent ECT treatment within 12 months. Where more than 1 variable was associated with response or subsequent ECT, logistic regression analysis was applied. MADRS symptom subtypes formed 3 separate factors in both samples. Logistic regression revealed older age and high "Despondency" subscale score predicted response in the nonpsychotic group. Older age alone predicted response in the group with psychotic features. Nonpsychotic patients subsequently re-treated with ECT were older than those not prescribed subsequent ECT. No association of variables emerged with subsequent ECT treatment in the group with psychotic features. Being of older age and the presence of psychotic features predicted response. Presence of psychotic features alone predicted subsequent retreatment. Subscale scores of the MADRS are of limited use in predicting which patients with depression will respond to ECT, with the exception of "Despondency" subscale scores in patients without psychotic features.

Multiple-choice pretesting potentiates learning of related information.

PubMed

Little, Jeri L; Bjork, Elizabeth Ligon

2016-10-01

Although the testing effect has received a substantial amount of empirical attention, such research has largely focused on the effects of tests given after study. The present research examines the effect of using tests prior to study (i.e., as pretests), focusing particularly on how pretesting influences the subsequent learning of information that is not itself pretested but that is related to the pretested information. In Experiment 1, we found that multiple-choice pretesting was better for the learning of such related information than was cued-recall pretesting or a pre-fact-study control condition. In Experiment 2, we found that the increased learning of non-pretested related information following multiple-choice testing could not be attributed to increased time allocated to that information during subsequent study. Last, in Experiment 3, we showed that the benefits of multiple-choice pretesting over cued-recall pretesting for the learning of related information persist over 48 hours, thus demonstrating the promise of multiple-choice pretesting to potentiate learning in educational contexts. A possible explanation for the observed benefits of multiple-choice pretesting for enhancing the effectiveness with which related nontested information is learned during subsequent study is discussed.

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.

Should the poor have no medicines to cure? A study on the association between social class and social security among the rural migrant workers in urban China.

PubMed

Guan, Ming

2017-11-07

The rampant urbanization and medical marketization in China have resulted in increased vulnerabilities to health and socioeconomic disparities among the rural migrant workers in urban China. In the Chinese context, the socioeconomic characteristics of rural migrant workers have attracted considerable research attention in the recent past years. However, to date, no previous studies have explored the association between the socioeconomic factors and social security among the rural migrant workers in urban China. This study aims to explore the association between socioeconomic inequity and social security inequity and the subsequent associations with medical inequity and reimbursement rejection. Data from a regionally representative sample of 2009 Survey of Migrant Workers in Pearl River Delta in China were used for analyses. Multiple logistic regressions were used to analyze the impacts of socioeconomic factors on the eight dimensions of social security (sick pay, paid leave, maternity pay, medical insurance, pension insurance, occupational injury insurance, unemployment insurance, and maternity insurance) and the impacts of social security on medical reimbursement rejection. The zero-inflated negative binomial regression model (ZINB regression) was adopted to explore the relationship between socioeconomic factors and hospital visits among the rural migrant workers with social security. The study population consisted of 848 rural migrant workers with high income who were young and middle-aged, low-educated, and covered by social security. Reimbursement rejection and abusive supervision for the rural migrant workers were observed. Logistic regression analysis showed that there were significant associations between socioeconomic factors and social security. ZINB regression showed that there were significant associations between socioeconomic factors and hospital visits among the rural migrant workers. Also, several dimensions of social security had significant associations with reimbursement rejections. This study showed that social security inequity, medical inequity, and reimbursement inequity happened to the rural migrant workers simultaneously. Future policy should strengthen health justice and enterprises' medical responsibilities to the employed rural migrant workers.

Distinct Patterns of Desynchronized Limb Regression in Malagasy Scincine Lizards (Squamata, Scincidae)

PubMed Central

Miralles, Aurélien; Hipsley, Christy A.; Erens, Jesse; Gehara, Marcelo; Rakotoarison, Andolalao; Glaw, Frank; Müller, Johannes; Vences, Miguel

2015-01-01

Scincine lizards in Madagascar form an endemic clade of about 60 species exhibiting a variety of ecomorphological adaptations. Several subclades have adapted to burrowing and convergently regressed their limbs and eyes, resulting in a variety of partial and completely limbless morphologies among extant taxa. However, patterns of limb regression in these taxa have not been studied in detail. Here we fill this gap in knowledge by providing a phylogenetic analysis of DNA sequences of three mitochondrial and four nuclear gene fragments in an extended sampling of Malagasy skinks, and microtomographic analyses of osteology of various burrowing taxa adapted to sand substrate. Based on our data we propose to (i) consider Sirenoscincus Sakata & Hikida, 2003, as junior synonym of Voeltzkowia Boettger, 1893; (ii) resurrect the genus name Grandidierina Mocquard, 1894, for four species previously included in Voeltzkowia; and (iii) consider Androngo Brygoo, 1982, as junior synonym of Pygomeles Grandidier, 1867. By supporting the clade consisting of the limbless Voeltzkowia mira and the forelimb-only taxa V. mobydick and V. yamagishii, our data indicate that full regression of limbs and eyes occurred in parallel twice in the genus Voeltzkowia (as hitherto defined) that we consider as a sand-swimming ecomorph: in the Voeltzkowia clade sensu stricto the regression first affected the hindlimbs and subsequently the forelimbs, whereas the Grandidierina clade first regressed the forelimbs and subsequently the hindlimbs following the pattern prevalent in squamates. Timetree reconstructions for the Malagasy Scincidae contain a substantial amount of uncertainty due to the absence of suitable primary fossil calibrations. However, our preliminary reconstructions suggest rapid limb regression in Malagasy scincids with an estimated maximal duration of 6 MYr for a complete regression in Paracontias, and 4 and 8 MYr respectively for complete regression of forelimbs in Grandidierina and hindlimbs in Voeltzkowia. PMID:26042667

Distinct patterns of desynchronized limb regression in malagasy scincine lizards (squamata, scincidae).

PubMed

Miralles, Aurélien; Hipsley, Christy A; Erens, Jesse; Gehara, Marcelo; Rakotoarison, Andolalao; Glaw, Frank; Müller, Johannes; Vences, Miguel

2015-01-01

Scincine lizards in Madagascar form an endemic clade of about 60 species exhibiting a variety of ecomorphological adaptations. Several subclades have adapted to burrowing and convergently regressed their limbs and eyes, resulting in a variety of partial and completely limbless morphologies among extant taxa. However, patterns of limb regression in these taxa have not been studied in detail. Here we fill this gap in knowledge by providing a phylogenetic analysis of DNA sequences of three mitochondrial and four nuclear gene fragments in an extended sampling of Malagasy skinks, and microtomographic analyses of osteology of various burrowing taxa adapted to sand substrate. Based on our data we propose to (i) consider Sirenoscincus Sakata & Hikida, 2003, as junior synonym of Voeltzkowia Boettger, 1893; (ii) resurrect the genus name Grandidierina Mocquard, 1894, for four species previously included in Voeltzkowia; and (iii) consider Androngo Brygoo, 1982, as junior synonym of Pygomeles Grandidier, 1867. By supporting the clade consisting of the limbless Voeltzkowia mira and the forelimb-only taxa V. mobydick and V. yamagishii, our data indicate that full regression of limbs and eyes occurred in parallel twice in the genus Voeltzkowia (as hitherto defined) that we consider as a sand-swimming ecomorph: in the Voeltzkowia clade sensu stricto the regression first affected the hindlimbs and subsequently the forelimbs, whereas the Grandidierina clade first regressed the forelimbs and subsequently the hindlimbs following the pattern prevalent in squamates. Timetree reconstructions for the Malagasy Scincidae contain a substantial amount of uncertainty due to the absence of suitable primary fossil calibrations. However, our preliminary reconstructions suggest rapid limb regression in Malagasy scincids with an estimated maximal duration of 6 MYr for a complete regression in Paracontias, and 4 and 8 MYr respectively for complete regression of forelimbs in Grandidierina and hindlimbs in Voeltzkowia.

Longitudinal and concurrent links between memory span, anxiety symptoms, and subsequent executive functioning in young children

PubMed Central

Visu-Petra, Laura; Stanciu, Oana; Benga, Oana; Miclea, Mircea; Cheie, Lavinia

2014-01-01

It has been conjectured that basic individual differences in attentional control influence higher-level executive functioning and subsequent academic performance in children. The current study sets out to complement the limited body of research on early precursors of executive functions (EFs). It provides both a cross-sectional, as well as a longitudinal exploration of the relationship between EF and more basic attentional control mechanisms, assessed via children's performance on memory storage tasks, and influenced by individual differences in anxiety. Multiple measures of verbal and visuospatial short-term memory (STM) were administered to children between 3 and 6 years old, alongside a non-verbal measure of intelligence, and a parental report of anxiety symptoms. After 9 months, children were re-tested on the same STM measures, at which time we also administered multiple measures of executive functioning: verbal and visuospatial working memory (WM), inhibition, and shifting. A cross-sectional view of STM development indicated that between 3 and 6 years the trajectory of visuospatial STM and EF underwent a gradual linear improvement. However, between 5 and 6 years progress in verbal STM performance stagnated. Hierarchical regression models revealed that trait anxiety was negatively associated with WM and shifting, while non-verbal intelligence was positively related to WM span. When age, gender, non-verbal intelligence, and anxiety were controlled for, STM (measured at the first assessment) was a very good predictor of overall executive performance. The models were most successful in predicting WM, followed by shifting, yet poorly predicted inhibition measures. Further longitudinal research is needed to directly address the contribution of attentional control mechanisms to emerging executive functioning and to the development of problematic behavior during early development. PMID:24904462

Citation Impact of Collaboration in Radiology Research.

PubMed

Rosenkrantz, Andrew B; Parikh, Ujas; Duszak, Richard

2018-02-01

Team science involving multidisciplinary and multi-institutional collaboration is increasingly recognized as a means of strengthening the quality of scientific research. The aim of this study was to assess associations between various forms of collaboration and the citation impact of published radiology research. In 2010, 876 original research articles published in Academic Radiology, the American Journal of Roentgenology, JACR, and Radiology were identified with at least one radiology-affiliated author. All articles were manually reviewed to extract features related to all authors' disciplines and institutions. Citations to these articles through September 2016 were extracted from Thomson Reuters Web of Science. Subsequent journal article citation counts were significantly higher (P < .05) for original research articles with at least seven versus six or fewer authors (26.2 ± 30.8 versus 20.3 ± 23.1, respectively), with authors from multiple countries versus from a single country (32.3 ± 39.2 versus 22.0 ± 25.0, respectively), with rather than without a nonuniversity collaborator (28.7 ± 38.6 versus 22.4 ± 24.9, respectively), and with rather than without a nonclinical collaborator (26.5 ± 33.1 versus 21.9 ± 24.4, respectively). On multivariate regression analysis, the strongest independent predictors of the number of citations were authors from multiple countries (β = 9.14, P = .002), a nonuniversity collaborator (β = 4.80, P = .082), and at least seven authors (β = 4.11, P = .038). With respect to subsequent journal article citations, various forms of collaboration are associated with greater scholarly impact of published radiology research. To enhance the relevance of their research, radiology investigators are encouraged to pursue collaboration across traditional disciplinary, institutional, and geographic boundaries. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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…

Perceived usefulness and perceived ease of use of electronic health records among nurses: Application of Technology Acceptance Model.

PubMed

Tubaishat, Ahmad

2017-09-18

Electronic health records (EHRs) are increasingly being implemented in healthcare organizations but little attention has been paid to the degree to which nurses as end-users will accept these systems and subsequently use them. To explore nurses' perceptions of usefulness and ease-of-use of EHRs. The relationship between these constructs was examined, and its predictors were studied. A national exploratory study was conducted with 1539 nurses from 15 randomly selected hospitals, representative of different regions and healthcare sectors in Jordan. Data were collected using a self-administered questionnaire, which was based on the Technology Acceptance Model. Correlations and linear multiple regression were utilized to analyze the data. Jordanian nurses demonstrated a positive perception of the usefulness and ease-of-use of EHRs, and subsequently accepted the technology. Significant positive correlations were found between these two constructs. The variables that predict usefulness were the gender, professional rank, EHR experience, and computer skills of the nurses. The perceived ease-of-use was affected by nursing and EHR experience, and computers skills. This study adds to the growing body of knowledge on issues related to the acceptance of technology in the health informatics field, focusing on nurses' acceptance of EHRs.

Associations of military divorce with mental, behavioral, and physical health outcomes.

PubMed

Wang, Lawrence; Seelig, Amber; Wadsworth, Shelley MacDermid; McMaster, Hope; Alcaraz, John E; Crum-Cianflone, Nancy F

2015-06-19

Divorce has been linked with poor physical and mental health outcomes among civilians. Given the unique stressors experienced by U.S. service members, including lengthy and/or multiple deployments, this study aimed to examine the associations of recent divorce on health and military outcomes among a cohort of U.S. service members. Millennium Cohort participants from the first enrollment panel, married at baseline (2001-2003), and married or divorced at follow-up (2004-2006), (N = 29,314). Those divorced were compared to those who remained married for mental, behavioral, physical health, and military outcomes using logistic regression models. Compared to those who remained married, recently divorced participants were significantly more likely to screen positive for new-onset posttraumatic stress disorder, depression, smoking initiation, binge drinking, alcohol-related problems, and experience moderate weight gain. However, they were also more likely be in the highest 15(th) percentile of physical functioning, and be able to deploy within the subsequent 3-year period after divorce. Recent divorce among military members was associated with adverse mental health outcomes and risky behaviors, but was also associated with higher odds of subsequent deployment. Attention should be given to those recently divorced regarding mental health and substance abuse treatment and prevention strategies.

Preschool negative emotionality predicts activity and connectivity of the fusiform face area and amygdala in later childhood.

PubMed

Kann, Sarah J; O'Rawe, Jonathan F; Huang, Anna S; Klein, Daniel N; Leung, Hoi-Chung

2017-09-01

Negative emotionality (NE) refers to individual differences in the propensity to experience and react with negative emotions and is associated with increased risk of psychological disorder. However, research on the neural bases of NE has focused almost exclusively on amygdala activity during emotional face processing. This study broadened this framework by examining the relationship between observed NE in early childhood and subsequent neural responses to emotional faces in both the amygdala and the fusiform face area (FFA) in a late childhood/early adolescent sample. Measures of NE were obtained from children at age 3 using laboratory observations, and functional magnetic resonance imaging (fMRI) data were collected when these children were between the ages of 9 and 12 while performing a visual stimulus identity matching task with houses and emotional faces as stimuli. Multiple regression analyses revealed that higher NE at age 3 is associated with significantly greater activation in the left amygdala and left FFA but lower functional connectivity between these two regions during the face conditions. These findings suggest that those with higher early NE have subsequent alterations in both activity and connectivity within an extended network during face processing. © The Author (2017). Published by Oxford University Press.

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.

Examining gender salary disparities: an analysis of the 2003 multistate salary survey.

PubMed

Brown, Lawrence M; Schommer, Jon C; Mott, Dave; Gaither, Caroline A; Doucette, William R; Zgarrick, Dave P; Droege, Marcus

2006-09-01

Pharmacist salary and wage surveys have been conducted at the state and national level for more than 20 years; however, it is not known to what extent, if any, wage disparities due to gender still exist. The overall objective of this study was to determine if wage disparities exist among male and female pharmacists at the multistate and individual state level for each of 6 states studied. A secondary objective was to explore the effect of various demographic variables on the hourly wages of pharmacists. Data were collected from 1,688 pharmacists in 6 states during 2003 using a cross-sectional descriptive survey design. A multiple regression analysis on hourly wage testing the effects of state of practice, practice setting, position, terminal degree, and years in practice was conducted. Subsequent multiple regression analyses were conducted individually for each of the 6 states to test the effects of the above variables on hourly wage for both male and female pharmacists, followed by state-level analyses for male and female pharmacists, respectively. For the pooled data, all variables were found to be significant predictors of hourly wage, except for earning a PharmD degree without a residency or graduate degree. Gender was not a significant predictor of wage disparities in the state-level analyses. Position was the only significant predictor of wage disparities in all states (except Tennessee) such that pharmacists in management positions make significantly higher salaries than those in staff positions. The results of these analyses suggest that wage disparities due to gender do not exist at the state level for the 6 states surveyed, when controlling for practice setting, position, terminal degree, and years in practice. The larger number of men in management positions may explain lower wages for female pharmacists.

Health-related quality of life of mothers of very low birth weight children at the age of five: results from the Newborn Lung Project Statewide Cohort Study.

PubMed

Witt, Whitney P; Litzelman, Kristin; Spear, Hilary A; Wisk, Lauren E; Levin, Nataliya; McManus, Beth M; Palta, Mari

2012-11-01

This study aimed to determine the health-related quality of life (HRQoL) in mothers of 5-year-old very low birth weight (VLBW) and normal birth weight (NBW) children, with a focus on the role of stress. This cohort study is ancillary to the Newborn Lung Project. A telephone interview collected information on symptoms of stress and HRQoL from 297 mothers of VLBW children and 290 mothers of NBW children who were enrolled in the Newborn Lung Project Statewide Cohort Study. Staged multiple regression analyses were used to evaluate the relationship between caregiver status and maternal HRQoL and the role stress played in this relationship. Additional multiple regression analyses were also used to evaluate the correlates of poor maternal HRQoL among VLBW mothers. Mothers of VLBW children experienced worse physical and mental HRQoL than mothers of NBW children. Adjusted analyses showed that physical HRQoL was significantly different between these mothers (β: -1.87, P = 0.001); this relationship was attenuated by maternal stress. Among the mothers of VLBW children, stress significantly contributed to adverse HRQoL outcomes when children were aged five. Child behavior problems at the age of two were also associated with worse subsequent maternal mental HRQoL (β: -0.18, P = 0.004), while each week of neonatal intensive care unit stay was associated with worse physical HRQoL (β: -0.26, P = 0.02). Caring for a VLBW child is negatively associated with the HRQoL of mothers; this relationship might be, in part, explained by maternal stress. Addressing maternal stress may be an important way to improve long-term HRQoL.

Pre-fracture nutritional status is predictive of functional status at discharge during the acute phase with hip fracture patients: A multicenter prospective cohort study.

PubMed

Inoue, Tatsuro; Misu, Syogo; Tanaka, Toshiaki; Sakamoto, Hiroki; Iwata, Kentaro; Chuman, Yuki; Ono, Rei

2017-10-01

Malnutrition is common in patients with hip fractures, and elderly patients with hip fractures lose functional independence and often fail to recover previous functional status. The aim of this study was to determine whether pre-fracture nutritional status predicts functional status of patients with hip fracture at discharge from acute hospitals. In the present multicenter prospective cohort study, pre-fracture nutritional status was assessed using the Mini Nutritional Assessment Short-Form (MNA-SF). At discharge from acute hospitals, functional status was evaluated using a functional independent measurement instrument (FIM). Subsequently, multiple regression analyses were performed using FIM as the dependent variable and MNA-SF as the independent variable. Among the 204 patients analyzed in the present study, the mean length of hospital stay was 26.2 ± 12.6 days, and according to MNA-SF assessments, 51 (25.0%) patients were malnourished, 98 (48.0%) were at risk of malnutrition, and 55 (27.0%) were well-nourished before fracture. At discharge, FIM scores were higher in patients who were well-nourished than in those who were malnourished or were at risk of malnutrition (p < 0.01). After adjustment for confounding factors, multiple regression analyses showed that MNA-SF was a significant independent predictor for FIM at discharge (well-nourished vs. malnourished, β = -0.86, p < 0.01). Pre-fracture nutritional status was a significant independent predictor for functional status at discharge during the acute phase, warranting early assessment of nutritional status and early intervention for successful postoperative rehabilitation. Copyright © 2016 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Risk-adjusted econometric model to estimate postoperative costs: an additional instrument for monitoring performance after major lung resection.

PubMed

Brunelli, Alessandro; Salati, Michele; Refai, Majed; Xiumé, Francesco; Rocco, Gaetano; Sabbatini, Armando

2007-09-01

The objectives of this study were to develop a risk-adjusted model to estimate individual postoperative costs after major lung resection and to use it for internal economic audit. Variable and fixed hospital costs were collected for 679 consecutive patients who underwent major lung resection from January 2000 through October 2006 at our unit. Several preoperative variables were used to develop a risk-adjusted econometric model from all patients operated on during the period 2000 through 2003 by a stepwise multiple regression analysis (validated by bootstrap). The model was then used to estimate the postoperative costs in the patients operated on during the 3 subsequent periods (years 2004, 2005, and 2006). Observed and predicted costs were then compared within each period by the Wilcoxon signed rank test. Multiple regression and bootstrap analysis yielded the following model predicting postoperative cost: 11,078 + 1340.3X (age > 70 years) + 1927.8X cardiac comorbidity - 95X ppoFEV1%. No differences between predicted and observed costs were noted in the first 2 periods analyzed (year 2004, $6188.40 vs $6241.40, P = .3; year 2005, $6308.60 vs $6483.60, P = .4), whereas in the most recent period (2006) observed costs were significantly lower than the predicted ones ($3457.30 vs $6162.70, P < .0001). Greater precision in predicting outcome and costs after therapy may assist clinicians in the optimization of clinical pathways and allocation of resources. Our economic model may be used as a methodologic template for economic audit in our specialty and complement more traditional outcome measures in the assessment of performance.

Global estimation of long-term persistence in annual river runoff

NASA Astrophysics Data System (ADS)

Markonis, Y.; Moustakis, Y.; Nasika, C.; Sychova, P.; Dimitriadis, P.; Hanel, M.; Máca, P.; Papalexiou, S. M.

2018-03-01

Long-term persistence (LTP) of annual river runoff is a topic of ongoing hydrological research, due to its implications to water resources management. Here, we estimate its strength, measured by the Hurst coefficient H, in 696 annual, globally distributed, streamflow records with at least 80 years of data. We use three estimation methods (maximum likelihood estimator, Whittle estimator and least squares variance) resulting in similar mean values of H close to 0.65. Subsequently, we explore potential factors influencing H by two linear (Spearman's rank correlation, multiple linear regression) and two non-linear (self-organizing maps, random forests) techniques. Catchment area is found to be crucial for medium to larger watersheds, while climatic controls, such as aridity index, have higher impact to smaller ones. Our findings indicate that long-term persistence is weaker than found in other studies, suggesting that enhanced LTP is encountered in large-catchment rivers, were the effect of spatial aggregation is more intense. However, we also show that the estimated values of H can be reproduced by a short-term persistence stochastic model such as an auto-regressive AR(1) process. A direct consequence is that some of the most common methods for the estimation of H coefficient, might not be suitable for discriminating short- and long-term persistence even in long observational records.

How well are journal and clinical article characteristics associated with the journal impact factor? a retrospective cohort study

PubMed Central

Lokker, Cynthia; Haynes, R. Brian; Chu, Rong; McKibbon, K. Ann; Wilczynski, Nancy L; Walter, Stephen D

2012-01-01

Objective: Journal impact factor (JIF) is often used as a measure of journal quality. A retrospective cohort study determined the ability of clinical article and journal characteristics, including appraisal measures collected at the time of publication, to predict subsequent JIFs. Methods: Clinical research articles that passed methods quality criteria were included. Each article was rated for relevance and newsworthiness by 3 to 24 physicians from a panel of more than 4,000 practicing clinicians. The 1,267 articles (from 103 journals) were divided 60∶40 into derivation (760 articles) and validation sets (507 articles), representing 99 and 88 journals, respectively. A multiple regression model was produced determining the association of 10 journal and article measures with the 2007 JIF. Results: Four of the 10 measures were significant in the regression model: number of authors, number of databases indexing the journal, proportion of articles passing methods criteria, and mean clinical newsworthiness scores. With the number of disciplines rating the article, the 5 variables accounted for 61% of the variation in JIF (R2 = 0.607, 95% CI 0.444 to 0.706, P<0.001). Conclusion: For the clinical literature, measures of scientific quality and clinical newsworthiness available at the time of publication can predict JIFs with 60% accuracy. PMID:22272156

Does School Connectedness Buffer the Impact of Peer Victimization on Early Adolescents' Subsequent Adjustment Problems?

ERIC Educational Resources Information Center

Loukas, Alexandra; Pasch, Keryn E.

2013-01-01

The current study examined the role of school connectedness as a moderator of the associations between overt and relational forms of peer victimization and early adolescents' subsequent adjustment problems. Data were collected from 490 adolescents when they were initially in the sixth and seventh grades and again 1 year later. Regression analyses…

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.

Use of nicotine replacement therapy to reduce or delay smoking but not to quit: prevalence and association with subsequent cessation efforts

PubMed Central

Levy, Douglas E; Thorndike, Anne N; Biener, Lois; Rigotti, Nancy A

2007-01-01

Objective To assess the prevalence of nicotine replacement therapy (NRT) use for purposes other than quitting smoking and examine the relation of this non‐standard NRT use (NSNRT) with subsequent smoking cessation efforts. Design A population based cohort study of adult smokers who were interviewed by telephone at baseline (2001–2) and at two year follow‐up. The association between NSNRT use to cut down on smoking or to delay smoking before baseline and cessation attempts and smoking outcomes at two year follow‐up was assessed using logistic regression to adjust for multiple potential confounding factors. Setting Massachusetts, USA. Subjects 1712 adult smokers in Massachusetts who were selected using a random digit dial telephone survey. Main outcome measures Quit attempt in 12 months before follow‐up, NRT use at quit attempt in 12 months before follow‐up, smoking cessation by follow‐up, or 50% reduction in cigarettes smoked per day between baseline and follow‐up. Results 18.7% of respondents reported ever having used NSNRT. In a multiple logistic regression analysis, there was no statistically significant association between past NSNRT use and quit attempts (ORcut down  = 0.89, 95% CI 0.59 to 1.33; ORdelay  = 1.29, 95% CI 0.73 to 2.29), smoking cessation (ORcut down  = 0.74, 95% CI 0.43 to 1.24; ORdelay  = 1.22, 95% CI 0.60 to 2.50) or 50% reduction in cigarettes smoked per day (ORcut down  = 0.93, 95% CI 0.62 to 1.38; ORdelay  = 0.80, 95% CI 0.43 to 1.49) at follow‐up. Past use of NRT to cut down on cigarettes was associated with use of NRT at a follow‐up quit attempt (ORcut down  = 2.28, 95% CI 1.50 to 3.47) but past use of NRT to delay smoking was not (ORdelay  = 1.25, 95% CI 0.67 to 2.34). Conclusions Use of NRT for reasons other than quitting smoking may be more common than was previously estimated. This population based survey finds no strong evidence that NRT use for purposes other than quitting smoking is either harmful or helpful. PMID:18048614

Comparative study of biodegradability prediction of chemicals using decision trees, functional trees, and logistic regression.

PubMed

Chen, Guangchao; Li, Xuehua; Chen, Jingwen; Zhang, Ya-Nan; Peijnenburg, Willie J G M

2014-12-01

Biodegradation is the principal environmental dissipation process of chemicals. As such, it is a dominant factor determining the persistence and fate of organic chemicals in the environment, and is therefore of critical importance to chemical management and regulation. In the present study, the authors developed in silico methods assessing biodegradability based on a large heterogeneous set of 825 organic compounds, using the techniques of the C4.5 decision tree, the functional inner regression tree, and logistic regression. External validation was subsequently carried out by 2 independent test sets of 777 and 27 chemicals. As a result, the functional inner regression tree exhibited the best predictability with predictive accuracies of 81.5% and 81.0%, respectively, on the training set (825 chemicals) and test set I (777 chemicals). Performance of the developed models on the 2 test sets was subsequently compared with that of the Estimation Program Interface (EPI) Suite Biowin 5 and Biowin 6 models, which also showed a better predictability of the functional inner regression tree model. The model built in the present study exhibits a reasonable predictability compared with existing models while possessing a transparent algorithm. Interpretation of the mechanisms of biodegradation was also carried out based on the models developed. © 2014 SETAC.

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

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.

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...

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…

Time Course of Changes in Maternal Left Ventricular Function during Subsequent Pregnancy in Women with a History of Gestational Hypertensive Disorders.

PubMed

Hieda, Michinari; Yoo, Jeung-Ki; Sun, Dan-Dan; Okada, Yoshiyuki; Parker, Rosemary S; Roberts-Reeves, Monique A; Adams-Huet, Beverley; Nelson, David B; Levine, Benjamin D; Fu, Qi

2018-06-13

Women with a history of gestational hypertensive disorders (GHD) are at increased risk for developing perinatal cardiovascular complications (e.g., gestational hypertension, preeclampsia, etc.) in subsequent pregnancies. The underlying mechanisms remain uncertain, but impaired maternal left ventricular function may be one contributing factor for these complications. We evaluated the time course of changes in left ventricular function before, during and after pregnancy in women with prior GHD. Sixteen women with a history of GHD (the high-risk group), and 25 women without such a history (controls) were enrolled. Resting hemodynamic and echocardiographic measurements were longitudinally performed prior to pregnancy, during early (4-8 weeks of gestation), late pregnancy (32-36 weeks), and postpartum (6-10 weeks after delivery). Pregnancy outcomes were obtained after delivery. At pre-pregnancy, there was no difference in blood pressure and heart rate between the groups. Corrected isovolumetric relaxation time was longer, E/e' was larger, and Tei-index was greater in the high-risk group than controls. Moreover, the rate of GHD during the study was significantly greater in the high-risk group than controls (Odds Ratio: 8.94 [95% CI: 1.55-51.5], P=0.007). Multiple logistic regression analysis adjusted for age demonstrated that pre-pregnancy E/e' was an independent predictor for GHD (P=0.017). Thus, women with a history of GHD have modestly impaired cardiac function pre-pregnancy compared to controls, which identifies an increased susceptibility to developing cardiovascular complications during a subsequent pregnancy.

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.

The importance of extent of choroid plexus cauterization in addition to endoscopic third ventriculostomy for infantile hydrocephalus: a retrospective North American observational study using propensity score-adjusted analysis.

PubMed

Fallah, Aria; Weil, Alexander G; Juraschka, Kyle; Ibrahim, George M; Wang, Anthony C; Crevier, Louis; Tseng, Chi-Hong; Kulkarni, Abhaya V; Ragheb, John; Bhatia, Sanjiv

2017-12-01

OBJECTIVE Combined endoscopic third ventriculostomy (ETC) and choroid plexus cauterization (CPC)-ETV/CPC- is being investigated to increase the rate of shunt independence in infants with hydrocephalus. The degree of CPC necessary to achieve improved rates of shunt independence is currently unknown. METHODS Using data from a single-center, retrospective, observational cohort study involving patients who underwent ETV/CPC for treatment of infantile hydrocephalus, comparative statistical analyses were performed to detect a difference in need for subsequent CSF diversion procedure in patients undergoing partial CPC (describes unilateral CPC or bilateral CPC that only extended from the foramen of Monro [FM] to the atrium on one side) or subtotal CPC (describes CPC extending from the FM to the posterior temporal horn bilaterally) using a rigid neuroendoscope. Propensity scores for extent of CPC were calculated using age and etiology. Propensity scores were used to perform 1) case-matching comparisons and 2) Cox multivariable regression, adjusting for propensity score in the unmatched cohort. Cox multivariable regression adjusting for age and etiology, but not propensity score was also performed as a third statistical technique. RESULTS Eighty-four patients who underwent ETV/CPC had sufficient data to be included in the analysis. Subtotal CPC was performed in 58 patients (69%) and partial CPC in 26 (31%). The ETV/CPC success rates at 6 and 12 months, respectively, were 49% and 41% for patients undergoing subtotal CPC and 35% and 31% for those undergoing partial CPC. Cox multivariate regression in a 48-patient cohort case-matched by propensity score demonstrated no added effect of increased extent of CPC on ETV/CPC survival (HR 0.868, 95% CI 0.422-1.789, p = 0.702). Cox multivariate regression including all patients, with adjustment for propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.845, 95% CI 0.462-1.548, p = 0.586). Cox multivariate regression including all patients, with adjustment for age and etiology, but not propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.908, 95% CI 0.495-1.664, p = 0.755). CONCLUSIONS Using multiple comparative statistical analyses, no difference in need for subsequent CSF diversion procedure was detected between patients in this cohort who underwent partial versus subtotal CPC. Further investigation regarding whether there is truly no difference between partial versus subtotal extent of CPC in larger patient populations and whether further gain in CPC success can be achieved with complete CPC is warranted.

Assessment of power output in jump tests for applicants to a sports sciences degree.

PubMed

Lara, A J; Abián, J; Alegre, L M; Jiménez, L; Aguado, X

2006-09-01

Our study aimed: 1) to describe the jump performance in a population of male applicants to a Faculty of Sports Sciences, 2) to apply different power equations from the literature to assess their accuracy, and 3) to develop a new regression equation from this population. The push off phases of the counter-movement jumps (CMJ) on a force platform of 161 applicants (age: 19+/-2.9 years; weight: 70.4+/-8.3 kg) to a Spanish Faculty of Sports Sciences were recorded and subsequently analyzed. Their hands had to be placed on the hips and the knee angle during the counter movement was not controlled. Each subject had 2 trials to reach a minimum of 29 cm of jump height, and when 2 jumps were performed the best trial was analyzed. Multiple regression analysis was performed to develop a new regression equation. Mean jump height was 34.6+/-4.3 cm, peak vertical force 1 663.9+/-291.1 N and peak power 3524.4+/-562 W. All the equations underestimated power, from 74% (Lewis) to 8% (Sayers). However, there were high and significant correlations between peak power measured on the force platform, and those assessed by the equations. The results of the present study support the development of power equations for specific populations, to achieve more accurate assessments. The power equation from this study [Power = (62.5 x jump height (cm)) + (50.3 x body mass (kg)) 2184.7] can be used accurately in populations of male physical education students.

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.

Predicting Potential Changes in Suitable Habitat and Distribution by 2100 for Tree Species of the Eastern United States

Treesearch

Louis R Iverson; Anantha M. Prasad; Mark W. Schwartz; Mark W. Schwartz

2005-01-01

We predict current distribution and abundance for tree species present in eastern North America, and subsequently estimate potential suitable habitat for those species under a changed climate with 2 x CO2. We used a series of statistical models (i.e., Regression Tree Analysis (RTA), Multivariate Adaptive Regression Splines (MARS), Bagging Trees (...

The Impact of Letter Grades on Student Effort, Course Selection, and Major Choice: A Regression-Discontinuity Analysis

ERIC Educational Resources Information Center

Main, Joyce B.; Ost, Ben

2014-01-01

The authors apply a regression-discontinuity design to identify the causal impact of letter grades on student effort within a course, subsequent credit hours taken, and the probability of majoring in economics. Their methodology addresses key issues in identifying the causal impact of letter grades: correlation with unobservable factors, such as…

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.

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.

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

Coastal climate is associated with elevated solar irradiance and higher 25(OH)D level.

PubMed

Cherrie, M P C; Wheeler, B W; White, M P; Sarran, C E; Osborne, N J

2015-04-01

There is evidence that populations living close to the coast have improved health and wellbeing. Coastal environments are linked to promotion of physical activity through provision of safe, opportune, aesthetic and accessible spaces for recreation. Exposure to coastal environments may also reduce stress and induce positive mood. We hypothesised that coastal climate may influence the vitamin D status of residents and thus partly explain benefits to health. Ecological and cross-sectional analyses were designed to elucidate the connection between coastal residence and vitamin D status. We divided residential data, from developed land use areas and the Lower Super Output Areas or Data Zones (Scotland) of the 1958 Birth Cohort participants, into the following coastal bands: <1 km, 1-5 km, 5-20 km, 20-50 km and over 50 km. In the ecological analysis we used a multiple regression model to describe the relationship between UV vitd and coastal proximity adjusted for latitude. Subsequently, using the residential information of the participants of the 1958 Birth Cohort we developed a multiple regression model to understand the relationship between serum 25(OH)D (a marker of vitamin D status) and coastal proximity adjusted for several factors related to vitamin D status (e.g. diet, outdoor activity). We found that coastal proximity was associated with solar irradiance; on average a 99.6 (96.1-103.3)J/m(2)/day regression coefficient was recorded for settlements <1 km from the coast compared with those at >50 km. This relationship was modified by latitude with settlements at a lower latitude exhibiting a greater effect. Individuals living closer to the coast in England had higher vitamin D levels than those inland, particularly in autumn. Geographic location may influence biochemistry and health outcomes due to environmental factors. This can provide benefits in terms of vitamin D status but may also pose a risk due to higher skin cancer risk. We provide further evidence in support of the claim that coastal environments can provide opportunities for health and wellbeing. Copyright © 2015 Elsevier Ltd. All rights reserved.

Identification of an Interaction between VWF rs7965413 and Platelet Count as a Novel Risk Marker for Metabolic Syndrome: An Extensive Search of Candidate Polymorphisms in a Case-Control Study

PubMed Central

Nakatochi, Masahiro; Ushida, Yasunori; Yasuda, Yoshinari; Yoshida, Yasuko; Kawai, Shun; Kato, Ryuji; Nakashima, Toru; Iwata, Masamitsu; Kuwatsuka, Yachiyo; Ando, Masahiko; Hamajima, Nobuyuki; Kondo, Takaaki; Oda, Hiroaki; Hayashi, Mutsuharu; Kato, Sawako; Yamaguchi, Makoto; Maruyama, Shoichi; Matsuo, Seiichi; Honda, Hiroyuki

2015-01-01

Although many single nucleotide polymorphisms (SNPs) have been identified to be associated with metabolic syndrome (MetS), there was only a slight improvement in the ability to predict future MetS by the simply addition of SNPs to clinical risk markers. To improve the ability to predict future MetS, combinational effects, such as SNP—SNP interaction, SNP—environment interaction, and SNP—clinical parameter (SNP × CP) interaction should be also considered. We performed a case-control study to explore novel SNP × CP interactions as risk markers for MetS based on health check-up data of Japanese male employees. We selected 99 SNPs that were previously reported to be associated with MetS and components of MetS; subsequently, we genotyped these SNPs from 360 cases and 1983 control subjects. First, we performed logistic regression analyses to assess the association of each SNP with MetS. Of these SNPs, five SNPs were significantly associated with MetS (P < 0.05): LRP2 rs2544390, rs1800592 between UCP1 and TBC1D9, APOA5 rs662799, VWF rs7965413, and rs1411766 between MYO16 and IRS2. Furthermore, we performed multiple logistic regression analyses, including an SNP term, a CP term, and an SNP × CP interaction term for each CP and SNP that was significantly associated with MetS. We identified a novel SNP × CP interaction between rs7965413 and platelet count that was significantly associated with MetS [SNP term: odds ratio (OR) = 0.78, P = 0.004; SNP × CP interaction term: OR = 1.33, P = 0.001]. This association of the SNP × CP interaction with MetS remained nominally significant in multiple logistic regression analysis after adjustment for either the number of MetS components or MetS components excluding obesity. Our results reveal new insight into platelet count as a risk marker for MetS. PMID:25646961

Identification of an interaction between VWF rs7965413 and platelet count as a novel risk marker for metabolic syndrome: an extensive search of candidate polymorphisms in a case-control study.

PubMed

Nakatochi, Masahiro; Ushida, Yasunori; Yasuda, Yoshinari; Yoshida, Yasuko; Kawai, Shun; Kato, Ryuji; Nakashima, Toru; Iwata, Masamitsu; Kuwatsuka, Yachiyo; Ando, Masahiko; Hamajima, Nobuyuki; Kondo, Takaaki; Oda, Hiroaki; Hayashi, Mutsuharu; Kato, Sawako; Yamaguchi, Makoto; Maruyama, Shoichi; Matsuo, Seiichi; Honda, Hiroyuki

2015-01-01

Although many single nucleotide polymorphisms (SNPs) have been identified to be associated with metabolic syndrome (MetS), there was only a slight improvement in the ability to predict future MetS by the simply addition of SNPs to clinical risk markers. To improve the ability to predict future MetS, combinational effects, such as SNP-SNP interaction, SNP-environment interaction, and SNP-clinical parameter (SNP × CP) interaction should be also considered. We performed a case-control study to explore novel SNP × CP interactions as risk markers for MetS based on health check-up data of Japanese male employees. We selected 99 SNPs that were previously reported to be associated with MetS and components of MetS; subsequently, we genotyped these SNPs from 360 cases and 1983 control subjects. First, we performed logistic regression analyses to assess the association of each SNP with MetS. Of these SNPs, five SNPs were significantly associated with MetS (P < 0.05): LRP2 rs2544390, rs1800592 between UCP1 and TBC1D9, APOA5 rs662799, VWF rs7965413, and rs1411766 between MYO16 and IRS2. Furthermore, we performed multiple logistic regression analyses, including an SNP term, a CP term, and an SNP × CP interaction term for each CP and SNP that was significantly associated with MetS. We identified a novel SNP × CP interaction between rs7965413 and platelet count that was significantly associated with MetS [SNP term: odds ratio (OR) = 0.78, P = 0.004; SNP × CP interaction term: OR = 1.33, P = 0.001]. This association of the SNP × CP interaction with MetS remained nominally significant in multiple logistic regression analysis after adjustment for either the number of MetS components or MetS components excluding obesity. Our results reveal new insight into platelet count as a risk marker for MetS.

A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan

DTIC Science & Technology

2012-03-01

Impact of global warming on monsoon variability in Pakistan. J. Anim. Pl. Sci., 21, no. 1, 107–110. Gillies, S., T. Murphree, and D. Meyer, 2012...are generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The...generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The predictands are

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are…

Reduction of shading-derived artifacts in skin chromophore imaging without measurements or assumptions about the shape of the subject

NASA Astrophysics Data System (ADS)

Yoshida, Kenichiro; Nishidate, Izumi; Ojima, Nobutoshi; Iwata, Kayoko

2014-01-01

To quantitatively evaluate skin chromophores over a wide region of curved skin surface, we propose an approach that suppresses the effect of the shading-derived error in the reflectance on the estimation of chromophore concentrations, without sacrificing the accuracy of that estimation. In our method, we use multiple regression analysis, assuming the absorbance spectrum as the response variable and the extinction coefficients of melanin, oxygenated hemoglobin, and deoxygenated hemoglobin as the predictor variables. The concentrations of melanin and total hemoglobin are determined from the multiple regression coefficients using compensation formulae (CF) based on the diffuse reflectance spectra derived from a Monte Carlo simulation. To suppress the shading-derived error, we investigated three different combinations of multiple regression coefficients for the CF. In vivo measurements with the forearm skin demonstrated that the proposed approach can reduce the estimation errors that are due to shading-derived errors in the reflectance. With the best combination of multiple regression coefficients, we estimated that the ratio of the error to the chromophore concentrations is about 10%. The proposed method does not require any measurements or assumptions about the shape of the subjects; this is an advantage over other studies related to the reduction of shading-derived errors.

Quantitative assessment of cervical vertebral maturation using cone beam computed tomography in Korean girls.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.

Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

ERIC Educational Resources Information Center

Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

2012-01-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

PubMed

Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

2016-04-01

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

PubMed

Raj, Retheep; Sivanandan, K S

2017-01-01

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

Glutamatergic and GABAergic gene sets in attention-deficit/hyperactivity disorder: association to overlapping traits in ADHD and autism.

PubMed

Naaijen, J; Bralten, J; Poelmans, G; Glennon, J C; Franke, B; Buitelaar, J K

2017-01-10

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) often co-occur. Both are highly heritable; however, it has been difficult to discover genetic risk variants. Glutamate and GABA are main excitatory and inhibitory neurotransmitters in the brain; their balance is essential for proper brain development and functioning. In this study we investigated the role of glutamate and GABA genetics in ADHD severity, autism symptom severity and inhibitory performance, based on gene set analysis, an approach to investigate multiple genetic variants simultaneously. Common variants within glutamatergic and GABAergic genes were investigated using the MAGMA software in an ADHD case-only sample (n=931), in which we assessed ASD symptoms and response inhibition on a Stop task. Gene set analysis for ADHD symptom severity, divided into inattention and hyperactivity/impulsivity symptoms, autism symptom severity and inhibition were performed using principal component regression analyses. Subsequently, gene-wide association analyses were performed. The glutamate gene set showed an association with severity of hyperactivity/impulsivity (P=0.009), which was robust to correcting for genome-wide association levels. The GABA gene set showed nominally significant association with inhibition (P=0.04), but this did not survive correction for multiple comparisons. None of single gene or single variant associations was significant on their own. By analyzing multiple genetic variants within candidate gene sets together, we were able to find genetic associations supporting the involvement of excitatory and inhibitory neurotransmitter systems in ADHD and ASD symptom severity in ADHD.

Regression Commonality Analysis: A Technique for Quantitative Theory Building

ERIC Educational Resources Information Center

Nimon, Kim; Reio, Thomas G., Jr.

2011-01-01

When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…

Precision Efficacy Analysis for Regression.

ERIC Educational Resources Information Center

Brooks, Gordon P.

When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…

The prediction of intelligence in preschool children using alternative models to regression.

PubMed

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

Reproductive periodicity and steroid hormone profiles in the sex-changing coral-reef fish, Plectropomus leopardus

NASA Astrophysics Data System (ADS)

Frisch, A. J.; McCormick, M. I.; Pankhurst, N. W.

2007-03-01

The reproductive biology of coral trout, Plectropomus leopardus, from the Great Barrier Reef (Australia) was investigated by correlating gonadal condition with plasma levels of gonadal steroids. Female fish were found to be regressed from mid-summer to early spring, after which rapid and cyclical increases in gonado-somatic index ( I G), maximum oocyte diameter (MOD) and plasma concentrations of estradiol-17β and testosterone were detected. Male fish, in contrast, commenced recrudescence slightly earlier in winter and responded with less dramatic increases in both I G and plasma concentrations of testosterone and 11-ketotestosterone. The mode of oocyte development was multiple group-synchronous, and cyclical fluctuations in reproductive parameters ( I G, MOD and gonadal steroid concentrations) were synchronized with new-moon lunar phases. It is likely, therefore, that individual P. leopardus have the capacity to spawn on multiple occasions, with lunar periodicity. However, evidence suggests that early bouts of reproduction may be more important in terms of reproductive investment than subsequent bouts later in the same season. It is concluded that patterns of gametogenesis and steroidogenesis in P. leopardus are similar to the patterns displayed by other tropical groupers, suggesting that management regimes and propagation protocols developed for these fishes may also be appropriate for use with P. leopardus.

Impact of mode of transportation on dyslipidaemia in working people in Beijing.

PubMed

Guo, X; Jia, Z; Zhang, P; Yang, S; Wu, W; Sang, L; Luo, Y; Lu, X; Dai, H; Zeng, Z; Wang, W

2009-12-01

This study aims to investigate the association between mode of transportation to work and dyslipidaemia. During the period between January and February 2006, telephone interviews were conducted with 2506 randomly selected urban residents aged 18 years or older in the 8 districts of Beijing, using a multiple stratified random sampling technique. Of the selected individuals, 1024 (40.86%) members of the workforce were subsequently tested for biomarkers (ie, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C)). Multiple logistic regression modelling was used, adjusted for potential confounders. The probability of dyslipidaemia in workers who travel to work by bus, car or taxi is higher than that of workers who walk to work, with prevalence odds ratios (PORs) of 1.99 (95% CI 1.33 to 2.97) and 2.21 (95% CI 1.28 to 3.84), respectively. There is no significant difference in the risk of experiencing dyslipidaemia when workers who ride bicycles are compared with those who walk to work (POR = 1.22, 95% CI 0.83 to 1.78). These findings indicate that modes of transportation to work are significantly associated with the prevalence of dyslipidaemia. Prevention education should be emphasised among higher-risk people who usually go to work by car, bus or taxi.

Memory Impairment in Multiple Sclerosis is Due to a Core Deficit in Initial Learning

PubMed Central

DeLuca, John; Leavitt, Victoria M.; Chiaravalloti, Nancy; Wylie, Glenn

2013-01-01

Persons with multiple sclerosis (MS) suffer memory impairment, but research on the nature of MS-related memory problems is mixed. Some have argued for a core deficit in retrieval, while others have identified deficient initial learning as the core deficit. We used a selective reminding paradigm to determine whether deficient initial learning or delayed retrieval represents the primary memory deficit in 44 persons with MS. Brain atrophy was measured from high-resolution MRIs. Regression analyses examined the impact of brain atrophy on (a) initial learning and delayed retrieval separately, and then (b) delayed retrieval controlling for initial learning. Brain atrophy was negatively associated with both initial learning and delayed retrieval (ps < .01), but brain atrophy was unrelated to retrieval when controlling for initial learning (p > .05). In addition, brain atrophy was associated with inefficient learning across initial acquisition trials, and brain atrophy was unrelated to delayed recall among MS subjects who successfully acquired the word list (although such learning frequently required many exposures). Taken together, memory deficits in MS are a result of deficits in initial learning; moreover, initial learning mediates the relationship between brain atrophy and subsequent retrieval, thereby supporting the core learning-deficit hypothesis of memory impairment in MS. PMID:23832311

Season of infectious mononucleosis as a risk factor for multiple sclerosis: A UK primary care case-control study.

PubMed

Downham, Christina; Visser, Elizabeth; Vickers, Mark; Counsell, Carl

2017-10-01

Infectious mononucleosis (IM) and vitamin D deficiency are both risk factors for multiple sclerosis (MS). We wished to establish if IM in the winter months when vitamin D levels are low may be a greater risk factor for MS than IM in the summer months. We identified all patients with MS diagnosed aged 16-60 in a large primary care database in the United Kingdom and matched each by age, sex, general practice and observation period with up to six controls. We identified a coded diagnosis of IM prior to the index date (date of diagnosis). Logistic regression was used to calculate the odds ratio for prior IM exposure in cases versus controls and for winter versus summer exposure in cases and controls with prior IM exposure. Based on 9247 cases and 55,033 matched controls (246 and 846 with prior IM respectively), IM was associated with the development of MS (OR 1.77, 95%CI 1.53-2.05) but there was no evidence that IM in the winter as opposed to summer was associated with developing MS (OR 1.09, 95%CI 0.72-1.66). We found no evidence that the season of IM influences the risk of subsequent MS. Copyright © 2017 Elsevier B.V. All rights reserved.

Fetal metabolic influences of neonatal anthropometry and adiposity.

PubMed

Donnelly, Jean M; Lindsay, Karen L; Walsh, Jennifer M; Horan, Mary; Molloy, Eleanor J; McAuliffe, Fionnuala M

2015-11-10

Large for gestational age infants have an increased risk of obesity, cardiovascular and metabolic complications during life. Knowledge of the key predictive factors of neonatal adiposity is required to devise targeted antenatal interventions. Our objective was to determine the fetal metabolic factors that influence regional neonatal adiposity in a cohort of women with previous large for gestational age offspring. Data from the ROLO [Randomised COntrol Trial of LOw Glycaemic Index in Pregnancy] study were analysed in the ROLO Kids study. Neonatal anthropometric and skinfold measurements were compared with fetal leptin and C-peptide results from cord blood in 185 cases. Analyses were performed to examine the association between these metabolic factors and birthweight, anthropometry and markers of central and generalised adiposity. Fetal leptin was found to correlate with birthweight, general adiposity and multiple anthropometric measurements. On multiple regression analysis, fetal leptin remained significantly associated with adiposity, independent of gender, maternal BMI, gestational age or study group assignment, while fetal C-peptide was no longer significant. Fetal leptin may be an important predictor of regional neonatal adiposity. Interventional studies are required to assess the impact of neonatal adiposity on the subsequent risk of childhood obesity and to determine whether interventions which reduce circulating leptin levels have a role to play in improving neonatal adiposity measures.

Optimization of fixture layouts of glass laser optics using multiple kernel regression.

PubMed

Su, Jianhua; Cao, Enhua; Qiao, Hong

2014-05-10

We aim to build an integrated fixturing model to describe the structural properties and thermal properties of the support frame of glass laser optics. Therefore, (a) a near global optimal set of clamps can be computed to minimize the surface shape error of the glass laser optic based on the proposed model, and (b) a desired surface shape error can be obtained by adjusting the clamping forces under various environmental temperatures based on the model. To construct the model, we develop a new multiple kernel learning method and call it multiple kernel support vector functional regression. The proposed method uses two layer regressions to group and order the data sources by the weights of the kernels and the factors of the layers. Because of that, the influences of the clamps and the temperature can be evaluated by grouping them into different layers.

Prediction of anthropometric foot characteristics in children.

PubMed

Morrison, Stewart C; Durward, Brian R; Watt, Gordon F; Donaldson, Malcolm D C

2009-01-01

The establishment of growth reference values is needed in pediatric practice where pathologic conditions can have a detrimental effect on the growth and development of the pediatric foot. This study aims to use multiple regression to evaluate the effects of multiple predictor variables (height, age, body mass, and gender) on anthropometric characteristics of the peripubescent foot. Two hundred children aged 9 to 12 years were recruited, and three anthropometric measurements of the pediatric foot were recorded (foot length, forefoot width, and navicular height). Multiple regression analysis was conducted, and coefficients for gender, height, and body mass all had significant relationships for the prediction of forefoot width and foot length (P < or = .05, r > or = 0.7). The coefficients for gender and body mass were not significant for the prediction of navicular height (P > or = .05), whereas height was (P < or = .05). Normative growth reference values and prognostic regression equations are presented for the peripubescent foot.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method.

PubMed

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2015-11-18

Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available.

Birthweight Related Factors in Northwestern Iran: Using Quantile Regression Method

PubMed Central

Fallah, Ramazan; Kazemnejad, Anoshirvan; Zayeri, Farid; Shoghli, Alireza

2016-01-01

Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Methods: This analytical descriptive study was carried out using pre-registered (March 2010 - March 2012) data of neonates in urban/rural health centers of Zanjan province using multiple-stage cluster sampling. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.2 statistical software. Results: From 8456 newborn baby, 4146 (49%) were female. The mean age of the mothers was 27.1±5.4 years. The mean birthweight of the neonates was 3104 ± 431 grams. Five hundred and seventy-three patients (6.8%) of the neonates were less than 2500 grams. In all quantiles, gestational age of neonates (p<0.05), weight and educational level of the mothers (p<0.05) showed a linear significant relationship with the i of the neonates. However, sex and birth rank of the neonates, mothers age, place of residence (urban/rural) and career were not significant in all quantiles (p>0.05). Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. PMID:26925889

Weighted regression analysis and interval estimators

Treesearch

Donald W. Seegrist

1974-01-01

A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.

Post-infectious irritable bowel syndrome (PI-IBS) after infection with Shiga-like toxin-producing Escherichia coli (STEC) O104:H4: A cohort study with prospective follow-up

PubMed Central

Löwe, Bernd; Broicher, Wiebke; Riegel, Björn; Fraedrich, Katharina; von Wulffen, Moritz; Gappmayer, Kerrin; Wegscheider, Karl; Treszl, András; Rose, Matthias; Layer, Peter; Lohse, Ansgar W

2015-01-01

Background In May/June 2011, the new Shiga-like toxin-producing Escherichia coli (STEC) strain O104:H4 caused the severest outbreak ever recorded of hemorrhagic enterocolitis in 3842 patients in Germany. Objectives As bacterial enterocolitis is an established risk factor of subsequent irritable bowel syndrome (IBS), we aimed to estimate prevalence and incidence of post-infectious (PI)-IBS after six and 12 months in a cohort of STEC O104:H4 patients and to prospectively identify associated somatic and psychometric risk factors. Methods A total of 389 patients were studied prospectively at baseline and at six and 12 months after STEC infection using STEC disease-related questionnaires and validated instruments for IBS (Rome III) and psychological factors. Frequencies and logistic regression models using multiple imputations were applied to assess predictor variables. Results Prevalence of IBS increased from 9.8% prior to STEC infection to 23.6% at six and 25.3% at 12 months after STEC infection. In patients without IBS symptoms prior to STEC infection, incidence of new IBS was 16.9%. Logistic regression models indicated higher somatization and anxiety scores as risk factors for, and mesalazine treatment during, STEC infection as the only significant protective factor against IBS. No other factor analyzed, including disease severity, showed an association. Conclusions PI-IBS rates following this unusually severe STEC outbreak were similar to what has been observed after other infectious gastroenteritis outbreaks. Our findings suggest that mesalazine may have reduced the risk of subsequent PI-IBS. As altered mucosal immune activity is a pivotal pathogenic factor in PI-IBS, our observation of a potential protective effect of mesalazine might be explained by its known modulatory action on mucosal immunity, and may warrant further investigation. PMID:26966532

Estimation of premorbid general fluid intelligence using traditional Chinese reading performance in Taiwanese samples.

PubMed

Chen, Ying-Jen; Ho, Meng-Yang; Chen, Kwan-Ju; Hsu, Chia-Fen; Ryu, Shan-Jin

2009-08-01

The aims of the present study were to (i) investigate if traditional Chinese word reading ability can be used for estimating premorbid general intelligence; and (ii) to provide multiple regression equations for estimating premorbid performance on Raven's Standard Progressive Matrices (RSPM), using age, years of education and Chinese Graded Word Reading Test (CGWRT) scores as predictor variables. Four hundred and twenty-six healthy volunteers (201 male, 225 female), aged 16-93 years (mean +/- SD, 41.92 +/- 18.19 years) undertook the tests individually under supervised conditions. Seventy percent of subjects were randomly allocated to the derivation group (n = 296), and the rest to the validation group (n = 130). RSPM score was positively correlated with CGWRT score and years of education. RSPM and CGWRT scores and years of education were also inversely correlated with age, but the declining trend for RSPM performance against age was steeper than that for CGWRT performance. Separate multiple regression equations were derived for estimating RSPM scores using different combinations of age, years of education, and CGWRT score for both groups. The multiple regression coefficient of each equation ranged from 0.71 to 0.80 with the standard error of estimate between 7 and 8 RSPM points. When fitting the data of one group to the equations derived from its counterpart group, the cross-validation multiple regression coefficients ranged from 0.71 to 0.79. There were no significant differences in the 'predicted-obtained' RSPM discrepancies between any equations. The regression equations derived in the present study may provide a basis for estimating premorbid RSPM performance.

Overall Preference of Running Shoes Can Be Predicted by Suitable Perception Factors Using a Multiple Regression Model.

PubMed

Tay, Cheryl Sihui; Sterzing, Thorsten; Lim, Chen Yen; Ding, Rui; Kong, Pui Wah

2017-05-01

This study examined (a) the strength of four individual footwear perception factors to influence the overall preference of running shoes and (b) whether these perception factors satisfied the nonmulticollinear assumption in a regression model. Running footwear must fulfill multiple functional criteria to satisfy its potential users. Footwear perception factors, such as fit and cushioning, are commonly used to guide shoe design and development, but it is unclear whether running-footwear users are able to differentiate one factor from another. One hundred casual runners assessed four running shoes on a 15-cm visual analogue scale for four footwear perception factors (fit, cushioning, arch support, and stability) as well as for overall preference during a treadmill running protocol. Diagnostic tests showed an absence of multicollinearity between factors, where values for tolerance ranged from .36 to .72, corresponding to variance inflation factors of 2.8 to 1.4. The multiple regression model of these four footwear perception variables accounted for 77.7% to 81.6% of variance in overall preference, with each factor explaining a unique part of the total variance. Casual runners were able to rate each footwear perception factor separately, thus assigning each factor a true potential to improve overall preference for the users. The results also support the use of a multiple regression model of footwear perception factors to predict overall running shoe preference. Regression modeling is a useful tool for running-shoe manufacturers to more precisely evaluate how individual factors contribute to the subjective assessment of running footwear.

A population-based study on the association between rheumatoid arthritis and voice problems.

PubMed

Hah, J Hun; An, Soo-Youn; Sim, Songyong; Kim, So Young; Oh, Dong Jun; Park, Bumjung; Kim, Sung-Gyun; Choi, Hyo Geun

2016-07-01

The objective of this study was to investigate whether rheumatoid arthritis increases the frequency of organic laryngeal lesions and the subjective voice complaint rate in those with no organic laryngeal lesion. We performed a cross-sectional study using the data from 19,368 participants (418 rheumatoid arthritis patients and 18,950 controls) of the 2008-2011 Korea National Health and Nutrition Examination Survey. The associations between rheumatoid arthritis and organic laryngeal lesions/subjective voice complaints were analyzed using simple/multiple logistic regression analysis with complex sample adjusting for confounding factors, including age, sex, smoking status, stress level, and body mass index, which could provoke voice problems. Vocal nodules, vocal polyp, and vocal palsy were not associated with rheumatoid arthritis in a multiple regression analysis, and only laryngitis showed a positive association (adjusted odds ratio, 1.59; 95 % confidence interval, 1.01-2.52; P = 0.047). Rheumatoid arthritis was associated with subjective voice discomfort in a simple regression analysis, but not in a multiple regression analysis. Participants with rheumatoid arthritis were older, more often female, and had higher stress levels than those without rheumatoid arthritis. These factors were associated with subjective voice complaints in both simple and multiple regression analyses. Rheumatoid arthritis was not associated with organic laryngeal diseases except laryngitis. Rheumatoid arthritis did not increase the odds ratio for subjective voice complaints. Voice problems in participants with rheumatoid arthritis originated from the characteristics of the rheumatoid arthritis group (higher mean age, female sex, and stress level) rather than rheumatoid arthritis itself.

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

Multiple-Shrinkage Multinomial Probit Models with Applications to Simulating Geographies in Public Use Data.

PubMed

Burgette, Lane F; Reiter, Jerome P

2013-06-01

Multinomial outcomes with many levels can be challenging to model. Information typically accrues slowly with increasing sample size, yet the parameter space expands rapidly with additional covariates. Shrinking all regression parameters towards zero, as often done in models of continuous or binary response variables, is unsatisfactory, since setting parameters equal to zero in multinomial models does not necessarily imply "no effect." We propose an approach to modeling multinomial outcomes with many levels based on a Bayesian multinomial probit (MNP) model and a multiple shrinkage prior distribution for the regression parameters. The prior distribution encourages the MNP regression parameters to shrink toward a number of learned locations, thereby substantially reducing the dimension of the parameter space. Using simulated data, we compare the predictive performance of this model against two other recently-proposed methods for big multinomial models. The results suggest that the fully Bayesian, multiple shrinkage approach can outperform these other methods. We apply the multiple shrinkage MNP to simulating replacement values for areal identifiers, e.g., census tract indicators, in order to protect data confidentiality in public use datasets.

Subsequent Injuries Are More Common Than Injury Recurrences: An Analysis of 1 Season of Prospectively Collected Injuries in Professional Australian Football.

PubMed

Finch, Caroline F; Cook, Jill; Kunstler, Breanne E; Akram, Muhammad; Orchard, John

2017-07-01

It is known that some people can, and do, sustain >1 injury over a playing season. However, there is currently little high-quality epidemiological evidence about the risk of, and relationships between, multiple and subsequent injuries. To describe the subsequent injuries sustained by Australian Football League (AFL) players over 1 season, including their most common injury diagnoses. Cohort study; Level of evidence, 3. Within-player linked injury data on all date-ordered match-loss injuries sustained by AFL players during 1 full season were obtained. The total number of injuries per player was determined, and in those with >1 injury, the Subsequent Injury Classification (SIC) model was used to code all subsequent injuries based on their Orchard Sports Injury Classification System (OSICS) codes and the dates of injury. There were 860 newly recorded injuries in 543 players; 247 players (45.5%) sustained ≥1 subsequent injuries after an earlier injury, with 317 subsequent injuries (36.9% of all injuries) recorded overall. A subsequent injury generally occurred to a different body region and was therefore superficially unrelated to an index injury. However, 32.2% of all subsequent injuries were related to a previous injury in the same season. Hamstring injuries were the most common subsequent injury. The mean time between injuries decreased with an increasing number of subsequent injuries. When relationships between injuries are taken into account, there is a high level of subsequent (and multiple) injuries leading to missed games in an elite athlete group.

Quantile Regression in the Study of Developmental Sciences

ERIC Educational Resources Information Center

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of…

Maintenance Operations in Mission Oriented Protective Posture Level IV (MOPPIV)

DTIC Science & Technology

1987-10-01

Repair FADAC Printed Circuit Board ............. 6 3. Data Analysis Techniques ............................. 6 a. Multiple Linear Regression... ANALYSIS /DISCUSSION ............................... 12 1. Exa-ple of Regression Analysis ..................... 12 S2. Regression results for all tasks...6 * TABLE 9. Task Grouping for Analysis ........................ 7 "TABXLE 10. Remove/Replace H60A3 Power Pack................. 8 TABLE

Interactions between cadmium and decabrominated diphenyl ether on blood cells count in rats-Multiple factorial regression analysis.

PubMed

Curcic, Marijana; Buha, Aleksandra; Stankovic, Sanja; Milovanovic, Vesna; Bulat, Zorica; Đukić-Ćosić, Danijela; Antonijević, Evica; Vučinić, Slavica; Matović, Vesna; Antonijevic, Biljana

2017-02-01

The objective of this study was to assess toxicity of Cd and BDE-209 mixture on haematological parameters in subacutely exposed rats and to determine the presence and type of interactions between these two chemicals using multiple factorial regression analysis. Furthermore, for the assessment of interaction type, an isobologram based methodology was applied and compared with multiple factorial regression analysis. Chemicals were given by oral gavage to the male Wistar rats weighing 200-240g for 28days. Animals were divided in 16 groups (8/group): control vehiculum group, three groups of rats were treated with 2.5, 7.5 or 15mg Cd/kg/day. These doses were chosen on the bases of literature data and reflect relatively high Cd environmental exposure, three groups of rats were treated with 1000, 2000 or 4000mg BDE-209/kg/bw/day, doses proved to induce toxic effects in rats. Furthermore, nine groups of animals were treated with different mixtures of Cd and BDE-209 containing doses of Cd and BDE-209 stated above. Blood samples were taken at the end of experiment and red blood cells, white blood cells and platelets counts were determined. For interaction assessment multiple factorial regression analysis and fitted isobologram approach were used. In this study, we focused on multiple factorial regression analysis as a method for interaction assessment. We also investigated the interactions between Cd and BDE-209 by the derived model for the description of the obtained fitted isobologram curves. Current study indicated that co-exposure to Cd and BDE-209 can result in significant decrease in RBC count, increase in WBC count and decrease in PLT count, when compared with controls. Multiple factorial regression analysis used for the assessment of interactions type between Cd and BDE-209 indicated synergism for the effect on RBC count and no interactions i.e. additivity for the effects on WBC and PLT counts. On the other hand, isobologram based approach showed slight antagonism for the effects on RBC and WBC while no interactions were proved for the joint effect on PLT count. These results confirm that the assessment of interactions between chemicals in the mixture greatly depends on the concept or method used for this evaluation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Estimation of 1RM for knee extension based on the maximal isometric muscle strength and body composition.

PubMed

Kanada, Yoshikiyo; Sakurai, Hiroaki; Sugiura, Yoshito; Arai, Tomoaki; Koyama, Soichiro; Tanabe, Shigeo

2017-11-01

[Purpose] To create a regression formula in order to estimate 1RM for knee extensors, based on the maximal isometric muscle strength measured using a hand-held dynamometer and data regarding the body composition. [Subjects and Methods] Measurement was performed in 21 healthy males in their twenties to thirties. Single regression analysis was performed, with measurement values representing 1RM and the maximal isometric muscle strength as dependent and independent variables, respectively. Furthermore, multiple regression analysis was performed, with data regarding the body composition incorporated as another independent variable, in addition to the maximal isometric muscle strength. [Results] Through single regression analysis with the maximal isometric muscle strength as an independent variable, the following regression formula was created: 1RM (kg)=0.714 + 0.783 × maximal isometric muscle strength (kgf). On multiple regression analysis, only the total muscle mass was extracted. [Conclusion] A highly accurate regression formula to estimate 1RM was created based on both the maximal isometric muscle strength and body composition. Using a hand-held dynamometer and body composition analyzer, it was possible to measure these items in a short time, and obtain clinically useful results.

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.

Intake of total, animal and plant protein and subsequent changes in weight or waist circumference in European men and women: the Diogenes project.

PubMed

Halkjær, J; Olsen, A; Overvad, K; Jakobsen, M U; Boeing, H; Buijsse, B; Palli, D; Tognon, G; Du, H; van der A, D L; Forouhi, N G; Wareham, N J; Feskens, E J M; Sørensen, T I A; Tjønneland, A

2011-08-01

As protein is considered to increase thermogenesis and satiety more than other macronutrients, it may have beneficial effects on prevention of weight gain and weight maintenance. The objective of this study is to assess the association between the amount and type of dietary protein, and subsequent changes in weight and waist circumference (WC). 89,432 men and women from five countries participating in European Prospective Investigation into Cancer and Nutrition (EPIC) were followed for a mean of 6.5 years. Associations between the intake of protein or subgroups of protein (from animal and plant sources) and changes in weight (g per year) or WC (cm per year) were investigated using gender and centre-specific multiple regression analyses. Adjustments were made for other baseline dietary factors, baseline anthropometrics, demographic and lifestyle factors and follow-up time. We used random effect meta-analyses to obtain pooled estimates across centres. Higher intake of total protein, and protein from animal sources was associated with subsequent weight gain for both genders, strongest among women, and the association was mainly attributable to protein from red and processed meat and poultry rather than from fish and dairy sources. There was no overall association between intake of plant protein and subsequent changes in weight. No clear overall associations between intakes of total protein or any of the subgroups and changes in WC were present. The associations showed some heterogeneity between centres, but pooling of estimates was still considered justified. A high intake of protein was not found associated with lower weight or waist gain in this observational study. In contrast, protein from food items of animal origin, especially meat and poultry, seemed to be positively associated with long-term weight gain. There were no clear associations for waist changes.

Does the presence of placental basal plate myometrial fibres increase the risk of subsequent morbidly adherent placenta: a case-control study.

PubMed

Miller, E S; Linn, R L; Ernst, L M

2016-12-01

Antenatal diagnosis of morbidly adherent placenta has been shown to improve outcomes, but existing predictors lack sensitivity. Our objective was to determine whether the presence of myometrial fibres attached to the placental basal plate (BPMYO) in an antecedent pregnancy is associated with subsequent morbidly adherent placenta. A case-control study. Departments of Obstetrics and Gynecology and Pathology, Northwestern University, Chicago, IL, USA. Women who had at least two pregnancies with placental pathological evaluation. Cases were defined as women with evidence of morbidly adherent placenta (both clinically and pathologically) in their most recent pregnancy whereas women without evidence of morbidly adherent placenta served as controls. Pathological specimens of placentas from previous pregnancies were evaluated for BPMYO. The presence of BPMYO on a previous placenta was evaluated to determine whether it could be used to improve the antenatal diagnosis of morbidly adherent placenta. Of the 25 cases of morbidly adherent placenta, 19 (76%) had BPMYO present on their previous placenta compared with 41 (41%) of controls (odds ratio 4.8, 95% CI 1.8-13.0). Adding BPMYO to a regression including other risk factors for morbidly adherent placenta (i.e. maternal age, number of previous caesarean sections, placenta praevia, previous multiple gestation, any previous curettage, and ultrasonographic suspicion of placenta accreta) significantly improved the sensitivity of antenatal diagnosis of morbidly adherent placenta (61% versus 39%, P < 0.001) without a change in specificity (97% versus 97%, P = 1.00). BPMYO on previous placental pathology is associated with an increased risk of morbidly adherent placenta in a subsequent pregnancy. These findings may shed light on the pathophysiology of accreta and inform future research on predictors of accreta. Previous basal plate myometrium improves the ability to detect subsequent morbidly adherent placenta. © 2015 Royal College of Obstetricians and Gynaecologists.

Clinical, laboratory, and demographic determinants of hospitalization due to dengue in 7613 patients: A retrospective study based on hierarchical models.

PubMed

da Silva, Natal Santos; Undurraga, Eduardo A; da Silva Ferreira, Elis Regina; Estofolete, Cássia Fernanda; Nogueira, Maurício Lacerda

2018-01-01

In Brazil, the incidence of hospitalization due to dengue, as an indicator of severity, has drastically increased since 1998. The objective of our study was to identify risk factors associated with subsequent hospitalization related to dengue. We analyzed 7613 dengue confirmed via serology (ELISA), non-structural protein 1, or polymerase chain reaction amplification. We used a hierarchical framework to generate a multivariate logistic regression based on a variety of risk variables. This was followed by multiple statistical analyses to assess hierarchical model accuracy, variance, goodness of fit, and whether or not this model reliably represented the population. The final model, which included age, sex, ethnicity, previous dengue infection, hemorrhagic manifestations, plasma leakage, and organ failure, showed that all measured parameters, with the exception of previous dengue, were statistically significant. The presence of organ failure was associated with the highest risk of subsequent dengue hospitalization (OR=5·75; CI=3·53-9·37). Therefore, plasma leakage and organ failure were the main indicators of hospitalization due to dengue, although other variables of minor importance should also be considered to refer dengue patients to hospital treatment, which may lead to a reduction in avoidable deaths as well as costs related to dengue. Copyright © 2017 Elsevier B.V. All rights reserved.

Cross Validation of Selection of Variables in Multiple Regression.

DTIC Science & Technology

1979-12-01

55 vii CROSS VALIDATION OF SELECTION OF VARIABLES IN MULTIPLE REGRESSION I Introduction Background Long term DoD planning gcals...028545024 .31109000 BF * SS - .008700618 .0471961 Constant - .70977903 85.146786 55 had adequate predictive capabilities; the other two models (the...71ZCO F111D Control 54 73EGO FlIID Computer, General Purpose 55 73EPO FII1D Converter-Multiplexer 56 73HAO flllD Stabilizer Platform 57 73HCO F1ID

Quantitative Assessment of Cervical Vertebral Maturation Using Cone Beam Computed Tomography in Korean Girls

PubMed Central

Byun, Bo-Ram; Kim, Yong-Il; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6–18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R 2 had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status. PMID:25878721

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

PubMed

NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Adjusted variable plots for Cox's proportional hazards regression model.

PubMed

Hall, C B; Zeger, S L; Bandeen-Roche, K J

1996-01-01

Adjusted variable plots are useful in linear regression for outlier detection and for qualitative evaluation of the fit of a model. In this paper, we extend adjusted variable plots to Cox's proportional hazards model for possibly censored survival data. We propose three different plots: a risk level adjusted variable (RLAV) plot in which each observation in each risk set appears, a subject level adjusted variable (SLAV) plot in which each subject is represented by one point, and an event level adjusted variable (ELAV) plot in which the entire risk set at each failure event is represented by a single point. The latter two plots are derived from the RLAV by combining multiple points. In each point, the regression coefficient and standard error from a Cox proportional hazards regression is obtained by a simple linear regression through the origin fit to the coordinates of the pictured points. The plots are illustrated with a reanalysis of a dataset of 65 patients with multiple myeloma.

Analysis of Binary Adherence Data in the Setting of Polypharmacy: A Comparison of Different Approaches

PubMed Central

Esserman, Denise A.; Moore, Charity G.; Roth, Mary T.

2009-01-01

Older community dwelling adults often take multiple medications for numerous chronic diseases. Non-adherence to these medications can have a large public health impact. Therefore, the measurement and modeling of medication adherence in the setting of polypharmacy is an important area of research. We apply a variety of different modeling techniques (standard linear regression; weighted linear regression; adjusted linear regression; naïve logistic regression; beta-binomial (BB) regression; generalized estimating equations (GEE)) to binary medication adherence data from a study in a North Carolina based population of older adults, where each medication an individual was taking was classified as adherent or non-adherent. In addition, through simulation we compare these different methods based on Type I error rates, bias, power, empirical 95% coverage, and goodness of fit. We find that estimation and inference using GEE is robust to a wide variety of scenarios and we recommend using this in the setting of polypharmacy when adherence is dichotomously measured for multiple medications per person. PMID:20414358

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Genetic Programming Transforms in Linear Regression Situations

NASA Astrophysics Data System (ADS)

Castillo, Flor; Kordon, Arthur; Villa, Carlos

The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.

A Solution to Separation and Multicollinearity in Multiple Logistic Regression

PubMed Central

Shen, Jianzhao; Gao, Sujuan

2010-01-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

PubMed

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Memory complaints are related to Alzheimer disease pathology in older persons.

PubMed

Barnes, L L; Schneider, J A; Boyle, P A; Bienias, J L; Bennett, D A

2006-11-14

To study the relationship between Alzheimer disease (AD) pathology and memory complaints proximate to death. A group of 90 older persons underwent detailed clinical evaluations and brain autopsy at death. The evaluations included administration of questions on subjective memory complaints and clinical classification of dementia and AD. On postmortem examination, neuritic plaques, diffuse plaques, and neurofibrillary tangles in tissue samples from five cortical regions were counted, and a summary measure of overall AD pathology was derived. In addition, amyloid load and tau tangles were quantified in eight regions. In multiple linear regression models adjusted for age, sex, and education, memory complaints were associated with AD pathology, including both amyloid and tau tangles. Subsequent analyses demonstrated that the relationship between memory complaints and AD pathology was present in those with and without dementia, and could not be explained by the potentially confounding effects of depressive symptoms or coexisting common chronic health problems. Memory complaints in older persons may indicate self awareness of a degenerative process.

Predictors of premature termination from psychotherapy for anorexia nervosa: Low treatment credibility, early therapy alliance, and self-transcendence.

PubMed

Jordan, Jennifer; McIntosh, Virginia V W; Carter, Frances A; Joyce, Peter R; Frampton, Christopher M A; Luty, Suzanne E; McKenzie, Janice M; Carter, Janet D; Bulik, Cynthia M

2017-08-01

Failure to complete treatment for anorexia nervosa (AN) is- common, clinically concerning but difficult to predict. This study examines whether therapy-related factors (patient-rated pretreatment credibility and early therapeutic alliance) predict subsequent premature termination of treatment (PTT) alongside self-transcendence (a previously identified clinical predictor) in women with AN. 56 women aged 17-40 years participating in a randomized outpatient psychotherapy trial for AN. Treatment completion was defined as attending 15/20 planned sessions. Measures were the Treatment Credibility, Temperament and Character Inventory, Vanderbilt Therapeutic Alliance Scale and the Vanderbilt Psychotherapy Process Scale. Statistics were univariate tests, correlations, and logistic regression. Treatment credibility and certain early patient and therapist alliance/process subscales predicted PTT. Lower self-transcendence and lower early process accounted for 33% of the variance in predicting PTT. Routine assessment of treatment credibility and early process (comprehensively assessed from multiple perspectives) may help clinicians reduce PTT thereby enhancing treatment outcomes. © 2017 Wiley Periodicals, Inc.

The role of early maturation, perceived popularity, and rumors in the emergence of internalizing symptoms among adolescent girls.

PubMed

Reynolds, Bridget M; Juvonen, Jaana

2011-11-01

Despite the widely reported link between early pubertal timing and internalizing symptoms among girls, less is known about the peer reputation of earlier maturing girls. The current study assesses whether early maturation is associated with perceived popularity and/or rumors, and whether these reputational factors help account for earlier maturing girls' vulnerability to emotional distress. Drawing on three waves of data collected from an ethnically diverse sample of middle school girls (n = 912), hierarchical multiple regression analyses revealed that more advanced development at the start of middle school predicted peer- and teacher-reported popularity as well as increased risk of being targeted for rumors. Mediation analyses suggested that popularity among boys can put earlier developing girls at risk for rumors. Finally, rumors acted as a partial mechanism through which early maturation was associated with subsequent internalizing symptoms. Knowledge of the peer mechanisms putting earlier developing girls at risk for psychosocial maladjustment can inform intervention and prevention efforts aimed at improving adolescent well-being.

A cohort study found that white blood cell count and endocrine markers predicted preterm birth in symptomatic women.

PubMed

Campbell, M Karen; Challis, John R G; DaSilva, Orlando; Bocking, Alan D

2005-03-01

This cohort study investigated potential clinical and biochemical predictors of subsequent preterm birth in women presenting with threatened preterm labor. Subjects were 218 pregnant women admitted to hospital with a diagnosis of threatened preterm labor at 22-36 weeks gestation. Exclusion criteria were multiple pregnancy, fetal anomalies, diabetes mellitus, abruptio placenta, preeclampsia, intrauterine growth restriction, cervical dilatation > 4 cm, and clinical signs of infection. Analyses used logistic regression. The presence of ruptured membranes was the best predictor of birth within 48 hours. Other important predictors were maternal white blood cell count at 22-27 weeks gestation and maternal adrenocorticotropin and corticotropin-releasing hormone concentrations at 28-36 weeks gestation. Subclinical infection may be an important etiologic factor in preterm births of gestational age < 28 weeks. For those at > or = 28 weeks gestation, the findings support the etiologic role of activation of the fetal and/or maternal hypothalamic pituitary adrenal axis leading to preterm birth.

Navigating the cancer information environment: The reciprocal relationship between patient-clinician information engagement and information seeking from nonmedical sources.

PubMed

Moldovan-Johnson, Mihaela; Tan, Andy S L; Hornik, Robert C

2014-01-01

Prior theory has argued and empirical studies have shown that cancer patients rely on information from their health care providers as well as lay sources to understand and make decisions about their disease. However, research on the dynamic and interdependent nature of cancer patients' engagement with different information sources is lacking. This study tested the hypotheses that patient-clinician information engagement and information seeking from nonmedical sources influence one another longitudinally among a representative cohort of 1,293 cancer survivors in Pennsylvania. The study hypotheses were supported in a series of lagged multiple regression analyses. Baseline seeking information from nonmedical sources positively predicted subsequent patient-clinician information engagement at 1-year follow-up. The reverse relationship was also statistically significant; baseline patient-clinician information engagement positively predicted information seeking from nonmedical sources at follow-up. These findings suggest that cancer survivors move between nonmedical and clinician sources in a dynamic way to learn about their disease.

Traumatic Stress, Perceived Global Stress, and Life Events: Prospectively Predicting Quality of Life in Breast Cancer Patients

PubMed Central

Golden-Kreutz, Deanna M.; Thornton, Lisa M.; Gregorio, Sharla Wells-Di; Frierson, Georita M.; Jim, Heather S.; Carpenter, Kristen M.; Shelby, Rebecca A.; Andersen, Barbara L.

2007-01-01

The authors investigated the relationship between stress at initial cancer diagnosis and treatment and subsequent quality of life (QoL). Women (n = 112) randomized to the assessment-only arm of a clinical trial were initially assessed after breast cancer diagnosis and surgery and then reassessed at 4 months (during adjuvant treatment) and 12 months (postadjuvant treatment). There were 3 types of stress measured: number of stressful life events (K. A. Matthews et al., 1997), cancer-related traumatic stress symptoms (M. J. Horowitz, N. Wilner, & W. Alvarez, 1979), and perceived global stress (S. Cohen, T. Kamarck, & R. Mermelstein, 1983). Using hierarchical multiple regressions, the authors found that stress predicted both psychological and physical QoL (J. E. Ware, K. K. Snow, & M. Kosinski, 2000) at the follow-ups (all ps < .03). These findings substantiate the relationship between initial stress and later QoL and underscore the need for timely psychological intervention. PMID:15898865

Statistical optimization of recycled-paper enzymatic hydrolysis for simultaneous saccharification and fermentation via central composite design.

PubMed

Liu, Qing; Cheng, Ke-ke; Zhang, Jian-an; Li, Jin-ping; Wang, Ge-hua

2010-01-01

A central composite design of the response surface methodology (RSM) was employed to study the effects of temperature, enzyme concentration, and stirring rate on recycled-paper enzymatic hydrolysis. Among the three variables, temperature and enzyme concentration significantly affected the conversion efficiency of substrate, whereas stirring rate was not effective. A quadratic polynomial equation was obtained for enzymatic hydrolysis by multiple regression analysis using RSM. The results of validation experiments were coincident with the predicted model. The optimum conditions for enzymatic hydrolysis were temperature, enzyme concentration, and stirring rate of 43.1 degrees C, 20 FPU g(-1) substrate, and 145 rpm, respectively. In the subsequent simultaneous saccharification and fermentation (SSF) experiment under the optimum conditions, the highest 28.7 g ethanol l(-1) was reached in the fed-batch SSF when 5% (w/v) substrate concentration was used initially, and another 5% added after 12 h fermentation. This ethanol output corresponded to 77.7% of the theoretical yield based on the glucose content in the raw material.

The Effects Of Dental Anxiety And Irregular Attendance On Referral For Dental Treatment Under Sedation Within The National Health Service In London

PubMed Central

Milgrom, Peter; Newton, J. T.; Boyle, Carole; Heaton, Lisa J.; Donaldson, Nora

2010-01-01

Objective To investigate whether the relationship between dental anxiety and referral for treatment under sedation is explained by attendance patterns and oral health. Methods Structural Equation Modeling was used on the covariance matrix of the covariates to test hypothesized inter-relationships. Subsequently, we modeled the probability of referral for treatment under sedation with a multiple logistic regression taking into account inter-relationships between the independent variables. Results A direct significant association of referral with dental anxiety and attendance patterns was detected but not with oral health status. However, oral health and anxiety were highly correlated. Also signaled were correlations between age and education and between gender and bad past experience. Conclusion Referral for treatment under sedation appears to be motivated by both fear and irregular patterns of attendance. Coupled with behavioral treatments to address dental fear and attendance, sedation can part of comprehensive care where curative treatments are long or unpleasant for patients. PMID:20545723

Relationship of number of seizures recorded on video-EEG to surgical outcome in refractory medial temporal lobe epilepsy

PubMed Central

Sainju, Rup Kamal; Wolf, Bethany Jacobs; Bonilha, Leonardo; Martz, Gabriel

2014-01-01

Introduction Surgical planning for refractory medial temporal lobe epilepsy (rMTLE) relies on seizure localization by ictal electroencephalography (EEG). Multiple factors impact the number of seizures recorded. We evaluated whether seizure freedom correlated to the number of seizures recorded, and the related factors. Methods We collected data for 32 patients with rMTLE who underwent anterior temporal lobectomy. Primary analysis evaluated number of seizures captured as a predictor of surgical outcome. Subsequent analyses explored factors that may seizure number. Results Number of seizures recorded did not predict seizure freedom. More seizures were recorded with more days of seizure occurrence (p<0.001), seizure clusters (p≤0.011) and poorly localized seizures (PLSz) (p=0.004). Regression modeling showed a trend for subjects with fewer recorded poorly localized seizures to have better surgical outcome (p=0.052). Conclusions Total number of recorded seizures does not predict surgical outcome. Patients with more PLSz may have worse outcome. PMID:22990726

Navigating the cancer information environment: The reciprocal relationship between patient-clinician information engagement and information seeking from nonmedical sources

PubMed Central

Moldovan-Johnson, Mihaela; Tan, Andy SL; Hornik, Robert C

2014-01-01

Prior theory has argued and empirical studies have shown that cancer patients rely on information from their health care providers as well as lay sources to understand and make decisions about their disease. However, research on the dynamic and interdependent nature of cancer patients’ engagement with different information sources is lacking. This study tested the hypotheses that patient-clinician information engagement and information seeking from nonmedical sources influence one another longitudinally among a representative cohort of 1,293 cancer survivors in Pennsylvania. The study hypotheses were supported in a series of lagged multiple regression analyses. Baseline seeking information from nonmedical sources positively predicted subsequent patient-clinician information engagement at one-year follow-up. The reverse relationship was also statistically significant; baseline patient-clinician information engagement positively predicted information seeking from nonmedical sources at follow-up. These findings suggest that cancer survivors move between nonmedical to clinician sources in a dynamic way to learn about their disease. PMID:24359259

Abdominal aortic aneurysm events in the women's health initiative: cohort study.

PubMed

Lederle, Frank A; Larson, Joseph C; Margolis, Karen L; Allison, Matthew A; Freiberg, Matthew S; Cochrane, Barbara B; Graettinger, William F; Curb, J David

2008-10-14

To assess the association between potential risk factors and subsequent clinically important abdominal aortic aneurysm events (repairs and ruptures) in women. Large prospective observational cohort study with mean follow-up of 7.8 years. 40 clinical centres across the United States. 161 808 postmenopausal women aged 50-79 enrolled in the women's health initiative. Association of self reported or measured baseline variables with confirmed abdominal aortic aneurysm events assessed with multiple logistic regression. Events occurred in 184 women and were strongly associated with age and smoking. Ever smoking, current smoking, and amount smoked all contributed independent risk. Diabetes showed a negative association (odds ratio 0.29, 95% confidence interval 0.13, 0.68), as did postmenopausal hormone therapy. Positive associations were also seen for height, hypertension, cholesterol lowering treatment, and coronary and peripheral artery disease. Our findings confirm the strong positive associations of clinically important abdominal aortic aneurysm with age and smoking in women and the negative association with diabetes previously reported in men.

Interpreting Epidemiological Evidence in the Presence of Multiple Endpoints: An Alternative Analytic Approach using the 9-Year Follow-up of the Seychelles Child Development Study

PubMed Central

van Wijngaarden, Edwin; Myers, Gary J.; Thurston, Sally W.; Shamlaye, Conrad F.; Davidson, Philip W.

2012-01-01

Purpose The potential for ill-informed causal inference is a major concern in published longitudinal studies evaluating impaired neurological function in children prenatally exposed to background levels of methyl mercury (MeHg). These studies evaluate a large number of developmental tests. We propose an alternative analysis strategy that reduces the number of comparisons tested in these studies. Methods Using data from the 9-year follow-up of 643 children in the Seychelles Child Development Study (SCDS), we grouped 18 individual endpoints into one overall ordinal outcome variable as well as by developmental domains. Subsequently, ordinal logistic regression analyses were performed. Results We did not find an association between prenatal MeHg exposure and developmental outcomes at 9 years of age. Conclusion Our proposed framework is more likely to result in a balanced interpretation of a posteriori associations. In addition, this new strategy should facilitate the use of complex epidemiological data in quantitative risk assessment. PMID:19205720

Interpreting epidemiological evidence in the presence of multiple endpoints: an alternative analytic approach using the 9-year follow-up of the Seychelles child development study.

PubMed

van Wijngaarden, Edwin; Myers, Gary J; Thurston, Sally W; Shamlaye, Conrad F; Davidson, Philip W

2009-08-01

The potential for ill-informed causal inference is a major concern in published longitudinal studies evaluating impaired neurological function in children prenatally exposed to background levels of methyl mercury (MeHg). These studies evaluate a large number of developmental tests. We propose an alternative analysis strategy that reduces the number of comparisons tested in these studies. Using data from the 9-year follow-up of 643 children in the Seychelles child development study, we grouped 18 individual endpoints into one overall ordinal outcome variable as well as by developmental domains. Subsequently, ordinal logistic regression analyses were performed. We did not find an association between prenatal MeHg exposure and developmental outcomes at 9 years of age. Our proposed framework is more likely to result in a balanced interpretation of a posteriori associations. In addition, this new strategy should facilitate the use of complex epidemiological data in quantitative risk assessment.

Utility of respiratory ward-based NIV in acidotic hypercapnic respiratory failure.

PubMed

Dave, Chirag; Turner, Alice; Thomas, Ajit; Beauchamp, Ben; Chakraborty, Biman; Ali, Asad; Mukherjee, Rahul; Banerjee, Dev

2014-11-01

We sought to elicit predictors of in-hospital mortality for first and subsequent admissions with acidotic hypercapnic respiratory failure (AHRF) in a cohort of chronic obstructive pulmonary disease patients who have undergone ward-based non-invasive ventilation (NIV), and identify features associated with long-term survival. Analysis of prospectively collected data at a single centre on patients undergoing NIV for AHRF between 2004 and 2009. Predictors of in-hospital mortality and intubation were sought by logistic regression and predictors of long-term survival by Cox regression. Initial pH exhibited a threshold effect for in-hospital mortality at pH 7.15. This relationship remained in patients undergoing their first episode of AHRF. In both first and subsequent admissions, a pH threshold of 7.25 at 4 h was associated with better prognosis (P = 0.02 and P = 0.04 respectively). In second or subsequent episodes of AHRF, mortality was lower and predicted only by age (P = 0.002) on multivariate analysis. NIV could be used on medical wards for patients with pH 7.16 or greater on their first admission, although more conservative values should continue to be used for those with a second or subsequent episodes of AHRF. © 2014 Asian Pacific Society of Respirology.

A regression technique for evaluation and quantification for water quality parameters from remote sensing data

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.

1979-01-01

The objective of this paper is to define optical physics and/or environmental conditions under which the linear multiple-regression should be applicable. An investigation of the signal-response equations is conducted and the concept is tested by application to actual remote sensing data from a laboratory experiment performed under controlled conditions. Investigation of the signal-response equations shows that the exact solution for a number of optical physics conditions is of the same form as a linearized multiple-regression equation, even if nonlinear contributions from surface reflections, atmospheric constituents, or other water pollutants are included. Limitations on achieving this type of solution are defined.

A Danish diabetes risk score for targeted screening: the Inter99 study.

PubMed

Glümer, Charlotte; Carstensen, Bendix; Sandbaek, Annelli; Lauritzen, Torsten; Jørgensen, Torben; Borch-Johnsen, Knut

2004-03-01

To develop a simple self-administered questionnaire identifying individuals with undiagnosed diabetes with a sensitivity of 75% and minimizing the high-risk group needing subsequent testing. A population-based sample (Inter99 study) of 6,784 individuals aged 30-60 years completed a questionnaire on diabetes-related symptoms and risk factors. The participants underwent an oral glucose tolerance test. The risk score was derived from the first half and validated on the second half of the study population. External validation was performed based on the Danish Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen Detected Diabetes in Primary Care (ADDITION) pilot study. The risk score was developed by stepwise backward multiple logistic regression. The final risk score included age, sex, BMI, known hypertension, physical activity at leisure time, and family history of diabetes, items independently and significantly (P<0.05) associated with the presence of previously undiagnosed diabetes. The area under the receiver operating curve was 0.804 (95% CI 0.765-0.838) for the first half of the Inter99 population, 0.761 (0.720-0.803) for the second half of the Inter99 population, and 0.803 (0.721-0.876) for the ADDITION pilot study. The sensitivity, specificity, and percentage that needed subsequent testing were 76, 72, and 29%, respectively. The false-negative individuals in the risk score had a lower absolute risk of ischemic heart disease compared with the true-positive individuals (11.3 vs. 20.4%; P<0.0001). We developed a questionnaire to be used in a stepwise screening strategy for type 2 diabetes, decreasing the numbers of subsequent tests and thereby possibly minimizing the economical and personal costs of the screening strategy.

RRegrs: an R package for computer-aided model selection with multiple regression models.

PubMed

Tsiliki, Georgia; Munteanu, Cristian R; Seoane, Jose A; Fernandez-Lozano, Carlos; Sarimveis, Haralambos; Willighagen, Egon L

2015-01-01

Predictive regression models can be created with many different modelling approaches. Choices need to be made for data set splitting, cross-validation methods, specific regression parameters and best model criteria, as they all affect the accuracy and efficiency of the produced predictive models, and therefore, raising model reproducibility and comparison issues. Cheminformatics and bioinformatics are extensively using predictive modelling and exhibit a need for standardization of these methodologies in order to assist model selection and speed up the process of predictive model development. A tool accessible to all users, irrespectively of their statistical knowledge, would be valuable if it tests several simple and complex regression models and validation schemes, produce unified reports, and offer the option to be integrated into more extensive studies. Additionally, such methodology should be implemented as a free programming package, in order to be continuously adapted and redistributed by others. We propose an integrated framework for creating multiple regression models, called RRegrs. The tool offers the option of ten simple and complex regression methods combined with repeated 10-fold and leave-one-out cross-validation. Methods include Multiple Linear regression, Generalized Linear Model with Stepwise Feature Selection, Partial Least Squares regression, Lasso regression, and Support Vector Machines Recursive Feature Elimination. The new framework is an automated fully validated procedure which produces standardized reports to quickly oversee the impact of choices in modelling algorithms and assess the model and cross-validation results. The methodology was implemented as an open source R package, available at https://www.github.com/enanomapper/RRegrs, by reusing and extending on the caret package. The universality of the new methodology is demonstrated using five standard data sets from different scientific fields. Its efficiency in cheminformatics and QSAR modelling is shown with three use cases: proteomics data for surface-modified gold nanoparticles, nano-metal oxides descriptor data, and molecular descriptors for acute aquatic toxicity data. The results show that for all data sets RRegrs reports models with equal or better performance for both training and test sets than those reported in the original publications. Its good performance as well as its adaptability in terms of parameter optimization could make RRegrs a popular framework to assist the initial exploration of predictive models, and with that, the design of more comprehensive in silico screening applications.Graphical abstractRRegrs is a computer-aided model selection framework for R multiple regression models; this is a fully validated procedure with application to QSAR modelling.

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Building a new predictor for multiple linear regression technique-based corrective maintenance turnaround time.

PubMed

Cruz, Antonio M; Barr, Cameron; Puñales-Pozo, Elsa

2008-01-01

This research's main goals were to build a predictor for a turnaround time (TAT) indicator for estimating its values and use a numerical clustering technique for finding possible causes of undesirable TAT values. The following stages were used: domain understanding, data characterisation and sample reduction and insight characterisation. Building the TAT indicator multiple linear regression predictor and clustering techniques were used for improving corrective maintenance task efficiency in a clinical engineering department (CED). The indicator being studied was turnaround time (TAT). Multiple linear regression was used for building a predictive TAT value model. The variables contributing to such model were clinical engineering department response time (CE(rt), 0.415 positive coefficient), stock service response time (Stock(rt), 0.734 positive coefficient), priority level (0.21 positive coefficient) and service time (0.06 positive coefficient). The regression process showed heavy reliance on Stock(rt), CE(rt) and priority, in that order. Clustering techniques revealed the main causes of high TAT values. This examination has provided a means for analysing current technical service quality and effectiveness. In doing so, it has demonstrated a process for identifying areas and methods of improvement and a model against which to analyse these methods' effectiveness.

Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales.

PubMed

Pratt, Bethany; Chang, Heejun

2012-03-30

The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R(2) values, fluctuated according to the scale, season, and regression method used. While most wet season water quality parameters are associated with urban land covers, most dry season water quality parameters are related topographic features such as elevation and slope. GWR models, which take into consideration local relations of spatial autocorrelation, had stronger results than OLS regression models. In the multiple regression models, sectioned watershed results were consistently better than the sectioned buffer results, except for dry season pH and stream temperature parameters. This suggests that while riparian land cover does have an effect on water quality, a wider contributing area needs to be included in order to account for distant sources of pollutants. Copyright © 2012 Elsevier B.V. All rights reserved.

Inhibition of serine/threonine phosphatase PP2A enhances cancer chemotherapy by blocking DNA damage induced defense mechanisms.

PubMed

Lu, Jie; Kovach, John S; Johnson, Francis; Chiang, Jeffrey; Hodes, Richard; Lonser, Russell; Zhuang, Zhengping

2009-07-14

A variety of mechanisms maintain the integrity of the genome in the face of cell stress. Cancer cell response to chemotherapeutic and radiation-induced DNA damage is mediated by multiple defense mechanisms including polo-like kinase 1 (Plk-1), protein kinase B (Akt-1), and/or p53 pathways leading to either apoptosis or cell cycle arrest. Subsequently, a subpopulation of arrested viable cancer cells may remain and recur despite aggressive and repetitive therapy. Here, we show that modulation (activation of Akt-1 and Plk-1 and repression of p53) of these pathways simultaneously results in paradoxical enhancement of the effectiveness of cytotoxic chemotherapy. We demonstrate that a small molecule inhibitor, LB-1.2, of protein phosphatase 2A (PP2A) activates Plk-1 and Akt-1 and decreases p53 abundance in tumor cells. Combined with temozolomide (TMZ; a DNA-methylating chemotherapeutic drug), LB-1.2 causes complete regression of glioblastoma multiforme (GBM) xenografts without recurrence in 50% of animals (up to 28 weeks) and complete inhibition of growth of neuroblastoma (NB) xenografts. Treatment with either drug alone results in only short-term inhibition/regression with all xenografts resuming rapid growth. Combined with another widely used anticancer drug, Doxorubicin (DOX, a DNA intercalating agent), LB-1.2 also causes marked GBM xenograft regression, whereas DOX alone only slows growth. Inhibition of PP2A by LB-1.2 blocks cell-cycle arrest and increases progression of cell cycle in the presence of TMZ or DOX. Pharmacologic inhibition of PP2A may be a general method for enhancing the effectiveness of cancer treatments that damage DNA or disrupt components of cell replication.

Analysing Twitter and web queries for flu trend prediction.

PubMed

Santos, José Carlos; Matos, Sérgio

2014-05-07

Social media platforms encourage people to share diverse aspects of their daily life. Among these, shared health related information might be used to infer health status and incidence rates for specific conditions or symptoms. In this work, we present an infodemiology study that evaluates the use of Twitter messages and search engine query logs to estimate and predict the incidence rate of influenza like illness in Portugal. Based on a manually classified dataset of 2704 tweets from Portugal, we selected a set of 650 textual features to train a Naïve Bayes classifier to identify tweets mentioning flu or flu-like illness or symptoms. We obtained a precision of 0.78 and an F-measure of 0.83, based on cross validation over the complete annotated set. Furthermore, we trained a multiple linear regression model to estimate the health-monitoring data from the Influenzanet project, using as predictors the relative frequencies obtained from the tweet classification results and from query logs, and achieved a correlation ratio of 0.89 (p<0.001). These classification and regression models were also applied to estimate the flu incidence in the following flu season, achieving a correlation of 0.72. Previous studies addressing the estimation of disease incidence based on user-generated content have mostly focused on the english language. Our results further validate those studies and show that by changing the initial steps of data preprocessing and feature extraction and selection, the proposed approaches can be adapted to other languages. Additionally, we investigated whether the predictive model created can be applied to data from the subsequent flu season. In this case, although the prediction result was good, an initial phase to adapt the regression model could be necessary to achieve more robust results.

Effect of plasma homocysteine level and urinary monomethylarsonic acid on the risk of arsenic-associated carotid atherosclerosis

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

Wu, M.-M.; Graduate Institute of Medicine, College of Medicine, Fu-Jen Catholic University, Taipei, Taiwan; Chiou, H.-Y.

2006-10-01

Arsenic-contaminated well water has been shown to increase the risk of atherosclerosis. Because of involving S-adenosylmethionine, homocysteine may modify the risk by interfering with the biomethylation of ingested arsenic. In this study, we assessed the effect of plasma homocysteine level and urinary monomethylarsonic acid (MMA{sup V}) on the risk of atherosclerosis associated with arsenic. In total, 163 patients with carotid atherosclerosis and 163 controls were studied. Lifetime cumulative arsenic exposure from well water for study subjects was measured as index of arsenic exposure. Homocysteine level was determined by high-performance liquid chromatography (HPLC). Proportion of MMA{sup V} (MMA%) was calculated bymore » dividing with total arsenic species in urine, including arsenite, arsenate, MMA{sup V}, and dimethylarsinic acid (DMA{sup V}). Results of multiple linear regression analysis show a positive correlation of plasma homocysteine levels to the cumulative arsenic exposure after controlling for atherosclerosis status and nutritional factors (P < 0.05). This correlation, however, did not change substantially the effect of arsenic exposure on the risk of atherosclerosis as analyzed in a subsequent logistic regression model. Logistic regression analyses also show that elevated plasma homocysteine levels did not confer an independent risk for developing atherosclerosis in the study population. However, the risk of having atherosclerosis was increased to 5.4-fold (95% CI, 2.0-15.0) for the study subjects with high MMA% ({>=}16.5%) and high homocysteine levels ({>=}12.7 {mu}mol/l) as compared to those with low MMA% (<9.9%) and low homocysteine levels (<12.7 {mu}mol/l). Elevated homocysteinemia may exacerbate the formation of atherosclerosis related to arsenic exposure in individuals with high levels of MMA% in urine.« less

Transport modeling and multivariate adaptive regression splines for evaluating performance of ASR systems in freshwater aquifers

NASA Astrophysics Data System (ADS)

Forghani, Ali; Peralta, Richard C.

2017-10-01

The study presents a procedure using solute transport and statistical models to evaluate the performance of aquifer storage and recovery (ASR) systems designed to earn additional water rights in freshwater aquifers. The recovery effectiveness (REN) index quantifies the performance of these ASR systems. REN is the proportion of the injected water that the same ASR well can recapture during subsequent extraction periods. To estimate REN for individual ASR wells, the presented procedure uses finely discretized groundwater flow and contaminant transport modeling. Then, the procedure uses multivariate adaptive regression splines (MARS) analysis to identify the significant variables affecting REN, and to identify the most recovery-effective wells. Achieving REN values close to 100% is the desire of the studied 14-well ASR system operator. This recovery is feasible for most of the ASR wells by extracting three times the injectate volume during the same year as injection. Most of the wells would achieve RENs below 75% if extracting merely the same volume as they injected. In other words, recovering almost all the same water molecules that are injected requires having a pre-existing water right to extract groundwater annually. MARS shows that REN most significantly correlates with groundwater flow velocity, or hydraulic conductivity and hydraulic gradient. MARS results also demonstrate that maximizing REN requires utilizing the wells located in areas with background Darcian groundwater velocities less than 0.03 m/d. The study also highlights the superiority of MARS over regular multiple linear regressions to identify the wells that can provide the maximum REN. This is the first reported application of MARS for evaluating performance of an ASR system in fresh water aquifers.

Obesity and hypomagnesemia.

PubMed

Guerrero-Romero, Fernando; Flores-García, Araceli; Saldaña-Guerrero, Stephanie; Simental-Mendía, Luis E; Rodríguez-Morán, Martha

2016-10-01

Whether low serum magnesium is an epiphenomenon related with obesity or, whether obesity per se is cause of hypomagnesemia, remains to be clarified. To examine the relationship between body weight status and hypomagnesemia in apparently healthy subjects. A total of 681 healthy individuals aged 30 to 65years were enrolled in A cross-sectional study. Extreme exercise, chronic diarrhea, alcohol intake, use of diuretics, smoking, oral magnesium supplementation, diabetes, malnutrition, hypertension, liver disease, thyroid disorders, and renal damage were exclusion criteria. Based in the Body Mass Index (BMI), body weight status was defined as follows: normal weight (BMI <25kg/m 2 ); overweight (BMI ≥25<30 BMIkg/m 2 ); and obesity (BMI ≥30kg/m 2 ). Hypomagnesemia was defined by serum magnesium concentration ≤0.74mmol/L. A multiple logistic regression analysis was used to compute the odds ratio (OR) between body weight status (independent variables) and hypomagnesemia (dependent variable). The multivariate logistic regression analysis showed that dietary magnesium intake (OR 2.11; 95%CI 1.4-5.7) but no obesity (OR 1.53; 95%CI 0.9-2.5), overweight (OR 1.40; 95%CI 0.8-2.4), and normal weight (OR 0.78; 95%CI 0.6-2.09) were associated with hypomagnesemia. A subsequent logistic regression analysis adjusted by body mass index, waist circumference, total body fat, systolic and diastolic blood pressure, and triglycerides levels showed that hyperglycemia (2.19; 95%CI 1.1-7.0) and dietary magnesium intake (2.21; 95%CI 1.1-8.9) remained associated with hypomagnesemia. Our results show that body weight status is not associated with hypomagnesemia and that, irrespective of obesity, hyperglycemia is cause of hypomagnesemia in non-diabetic individuals. Copyright © 2016 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Surrogate analysis and index developer (SAID) tool and real-time data dissemination utilities

USGS Publications Warehouse

Domanski, Marian M.; Straub, Timothy D.; Wood, Molly S.; Landers, Mark N.; Wall, Gary R.; Brady, Steven J.

2015-01-01

The use of acoustic and other parameters as surrogates for suspended-sediment concentrations (SSC) in rivers has been successful in multiple applications across the Nation. Critical to advancing the operational use of surrogates are tools to process and evaluate the data along with the subsequent development of regression models from which real-time sediment concentrations can be made available to the public. Recent developments in both areas are having an immediate impact on surrogate research, and on surrogate monitoring sites currently in operation. The Surrogate Analysis and Index Developer (SAID) standalone tool, under development by the U.S. Geological Survey (USGS), assists in the creation of regression models that relate response and explanatory variables by providing visual and quantitative diagnostics to the user. SAID also processes acoustic parameters to be used as explanatory variables for suspended-sediment concentrations. The sediment acoustic method utilizes acoustic parameters from fixed-mount stationary equipment. The background theory and method used by the tool have been described in recent publications, and the tool also serves to support sediment-acoustic-index methods being drafted by the multi-agency Sediment Acoustic Leadership Team (SALT), and other surrogate guidelines like USGS Techniques and Methods 3-C4 for turbidity and SSC. The regression models in SAID can be used in utilities that have been developed to work with the USGS National Water Information System (NWIS) and for the USGS National Real-Time Water Quality (NRTWQ) Web site. The real-time dissemination of predicted SSC and prediction intervals for each time step has substantial potential to improve understanding of sediment-related water-quality and associated engineering and ecological management decisions.

Development of a Multiple Linear Regression Model to Forecast Facility Electrical Consumption at an Air Force Base.

DTIC Science & Technology

1981-09-01

corresponds to the same square footage that consumed the electrical energy. 3. The basic assumptions of multiple linear regres- sion, as enumerated in...7. Data related to the sample of bases is assumed to be representative of bases in the population. Limitations Basic limitations on this research were... Ratemaking --Overview. Rand Report R-5894, Santa Monica CA, May 1977. Chatterjee, Samprit, and Bertram Price. Regression Analysis by Example. New York: John

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

PubMed

David, Ingrid; Garreau, Hervé; Balmisse, Elodie; Billon, Yvon; Canario, Laurianne

2017-01-20

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67). We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2014 CFR

2014-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2011 CFR

2011-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2013 CFR

2013-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

5 CFR 591.219 - How does OPM compute shelter price indexes?

Code of Federal Regulations, 2012 CFR

2012-01-01

... estimates in hedonic regressions (a type of multiple regression) to compute for each COLA survey area the price index for rental and/or rental equivalent units of comparable quality and size between the COLA...

Toward customer-centric organizational science: A common language effect size indicator for multiple linear regressions and regressions with higher-order terms.

PubMed

Krasikova, Dina V; Le, Huy; Bachura, Eric

2018-06-01

To address a long-standing concern regarding a gap between organizational science and practice, scholars called for more intuitive and meaningful ways of communicating research results to users of academic research. In this article, we develop a common language effect size index (CLβ) that can help translate research results to practice. We demonstrate how CLβ can be computed and used to interpret the effects of continuous and categorical predictors in multiple linear regression models. We also elaborate on how the proposed CLβ index is computed and used to interpret interactions and nonlinear effects in regression models. In addition, we test the robustness of the proposed index to violations of normality and provide means for computing standard errors and constructing confidence intervals around its estimates. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Modeling brook trout presence and absence from landscape variables using four different analytical methods

USGS Publications Warehouse

Steen, Paul J.; Passino-Reader, Dora R.; Wiley, Michael J.

2006-01-01

As a part of the Great Lakes Regional Aquatic Gap Analysis Project, we evaluated methodologies for modeling associations between fish species and habitat characteristics at a landscape scale. To do this, we created brook trout Salvelinus fontinalis presence and absence models based on four different techniques: multiple linear regression, logistic regression, neural networks, and classification trees. The models were tested in two ways: by application to an independent validation database and cross-validation using the training data, and by visual comparison of statewide distribution maps with historically recorded occurrences from the Michigan Fish Atlas. Although differences in the accuracy of our models were slight, the logistic regression model predicted with the least error, followed by multiple regression, then classification trees, then the neural networks. These models will provide natural resource managers a way to identify habitats requiring protection for the conservation of fish species.

Schistosomiasis Breeding Environment Situation Analysis in Dongting Lake Area

NASA Astrophysics Data System (ADS)

Li, Chuanrong; Jia, Yuanyuan; Ma, Lingling; Liu, Zhaoyan; Qian, Yonggang

2013-01-01

Monitoring environmental characteristics, such as vegetation, soil moisture et al., of Oncomelania hupensis (O. hupensis)’ spatial/temporal distribution is of vital importance to the schistosomiasis prevention and control. In this study, the relationship between environmental factors derived from remotely sensed data and the density of O. hupensis was analyzed by a multiple linear regression model. Secondly, spatial analysis of the regression residual was investigated by the semi-variogram method. Thirdly, spatial analysis of the regression residual and the multiple linear regression model were both employed to estimate the spatial variation of O. hupensis density. Finally, the approach was used to monitor and predict the spatial and temporal variations of oncomelania of Dongting Lake region, China. And the areas of potential O. hupensis habitats were predicted and the influence of Three Gorges Dam (TGB)project on the density of O. hupensis was analyzed.

Modification of the USLE K factor for soil erodibility assessment on calcareous soils in Iran

NASA Astrophysics Data System (ADS)

Ostovari, Yaser; Ghorbani-Dashtaki, Shoja; Bahrami, Hossein-Ali; Naderi, Mehdi; Dematte, Jose Alexandre M.; Kerry, Ruth

2016-11-01

The measurement of soil erodibility (K) in the field is tedious, time-consuming and expensive; therefore, its prediction through pedotransfer functions (PTFs) could be far less costly and time-consuming. The aim of this study was to develop new PTFs to estimate the K factor using multiple linear regression, Mamdani fuzzy inference systems, and artificial neural networks. For this purpose, K was measured in 40 erosion plots with natural rainfall. Various soil properties including the soil particle size distribution, calcium carbonate equivalent, organic matter, permeability, and wet-aggregate stability were measured. The results showed that the mean measured K was 0.014 t h MJ- 1 mm- 1 and 2.08 times less than the estimated mean K (0.030 t h MJ- 1 mm- 1) using the USLE model. Permeability, wet-aggregate stability, very fine sand, and calcium carbonate were selected as independent variables by forward stepwise regression in order to assess the ability of multiple linear regression, Mamdani fuzzy inference systems and artificial neural networks to predict K. The calcium carbonate equivalent, which is not accounted for in the USLE model, had a significant impact on K in multiple linear regression due to its strong influence on the stability of aggregates and soil permeability. Statistical indices in validation and calibration datasets determined that the artificial neural networks method with the highest R2, lowest RMSE, and lowest ME was the best model for estimating the K factor. A strong correlation (R2 = 0.81, n = 40, p < 0.05) between the estimated K from multiple linear regression and measured K indicates that the use of calcium carbonate equivalent as a predictor variable gives a better estimation of K in areas with calcareous soils.

Analysis and prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool.

PubMed

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-10-01

Traditionally, the multiple linear regression technique has been one of the most widely used models in simulating hydrological time series. However, when the nonlinear phenomenon is significant, the multiple linear will fail to develop an appropriate predictive model. Recently, neuro-fuzzy systems have gained much popularity for calibrating the nonlinear relationships. This study evaluated the potential of a neuro-fuzzy system as an alternative to the traditional statistical regression technique for the purpose of predicting flow from a local source in a river basin. The effectiveness of the proposed identification technique was demonstrated through a simulation study of the river flow time series of the Citarum River in Indonesia. Furthermore, in order to provide the uncertainty associated with the estimation of river flow, a Monte Carlo simulation was performed. As a comparison, a multiple linear regression analysis that was being used by the Citarum River Authority was also examined using various statistical indices. The simulation results using 95% confidence intervals indicated that the neuro-fuzzy model consistently underestimated the magnitude of high flow while the low and medium flow magnitudes were estimated closer to the observed data. The comparison of the prediction accuracy of the neuro-fuzzy and linear regression methods indicated that the neuro-fuzzy approach was more accurate in predicting river flow dynamics. The neuro-fuzzy model was able to improve the root mean square error (RMSE) and mean absolute percentage error (MAPE) values of the multiple linear regression forecasts by about 13.52% and 10.73%, respectively. Considering its simplicity and efficiency, the neuro-fuzzy model is recommended as an alternative tool for modeling of flow dynamics in the study area.

Some properties of negative cloud-to-ground flashes from observations of a local thunderstorm based on accurate-stroke-count studies

NASA Astrophysics Data System (ADS)

Zhu, Baoyou; Ma, Ming; Xu, Weiwei; Ma, Dong

2015-12-01

Properties of negative cloud-to-ground (CG) lightning flashes, in terms of number of strokes per flash, inter-stroke intervals and the relative intensity of subsequent and first strokes, were presented by accurate-stroke-count studies based on all 1085 negative flashes from a local thunderstorm. The percentage of single-stroke flashes and stroke multiplicity evolved significantly during the whole life cycle of the study thunderstorm. The occurrence probability of negative CG flashes decreased exponentially with the increasing number of strokes per flash. About 30.5% of negative CG flashes contained only one stroke and number of strokes per flash averaged 3.3. In a subset of 753 negative multiple-stroke flashes, about 41.4% contained at least one subsequent stroke stronger than the corresponding first stroke. Subsequent strokes tended to decrease in strength with their orders and the ratio of subsequent to first stroke peaks presented a geometric mean value of 0.52. Interestingly, negative CG flashes of higher multiplicity tended to have stronger initial strokes. 2525 inter-stroke intervals showed a more or less log-normal distribution and gave a geometric mean value of 62 ms. For CG flashes of particular multiplicity geometric mean inter-stroke intervals tended to decrease with the increasing number of strokes per flash, while those intervals associated with higher order strokes tended to be larger than those associated with low order strokes.

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.

PubMed

Nguyen, Quynh C; Osypuk, Theresa L; Schmidt, Nicole M; Glymour, M Maria; Tchetgen Tchetgen, Eric J

2015-03-01

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. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Neural network and multiple linear regression to predict school children dimensions for ergonomic school furniture design.

PubMed

Agha, Salah R; Alnahhal, Mohammed J

2012-11-01

The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

PubMed

Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

2009-07-01

Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

Investigative clinical study on prostate cancer part VI: Follicle-stimulating hormone and the pituitary-testicular-prostate axis at the time of initial diagnosis and subsequent cluster selection of the patient population.

PubMed

Porcaro, Antonio B; Migliorini, Filippo; Petrozziello, Aldo; Sava, Teodoro; Romano, Mario; Caruso, Beatrice; Cocco, Claudio; Ghimenton, Claudio; Zecchinini Antoniolli, Stefano; Lacola, Vincenzo; Rubilotta, Emanuele; Monaco, Carmelo; Comunale, Luigi

2012-01-01

To evaluate the physiopathology of follicle-stimulating hormone (FSH) along the pituitary-testicular-prostate axis at the time of initial diagnosis of prostate cancer in relation to the available clinical variables and to the subsequent cluster selection of the patient population. The study included 98 patients who were diagnosed with prostate cancer. Age, percentages of positive cores (P+) at transrectal ultrasound scan biopsy, biopsy Gleason score (bGS), luteinizing hormone (LH), FSH, total testosterone, free testosterone (FT) and prostate-specific antigen (PSA) were the continuous clinical variables. All patients had not previously received hormonal manipulations. FSH correlation and multiple linear analyses were computed in the population. The FSH/PSA ratio was computed and then ranked for clustering the population as groups A (0.13≤FSH/PSA≤0.57), B (0.57

Glutamatergic and GABAergic gene sets in attention-deficit/hyperactivity disorder: association to overlapping traits in ADHD and autism

PubMed Central

Naaijen, J; Bralten, J; Poelmans, G; Faraone, Stephen; Asherson, Philip; Banaschewski, Tobias; Buitelaar, Jan; Franke, Barbara; P Ebstein, Richard; Gill, Michael; Miranda, Ana; D Oades, Robert; Roeyers, Herbert; Rothenberger, Aribert; Sergeant, Joseph; Sonuga-Barke, Edmund; Anney, Richard; Mulas, Fernando; Steinhausen, Hans-Christoph; Glennon, J C; Franke, B; Buitelaar, J K

2017-01-01

Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorders (ASD) often co-occur. Both are highly heritable; however, it has been difficult to discover genetic risk variants. Glutamate and GABA are main excitatory and inhibitory neurotransmitters in the brain; their balance is essential for proper brain development and functioning. In this study we investigated the role of glutamate and GABA genetics in ADHD severity, autism symptom severity and inhibitory performance, based on gene set analysis, an approach to investigate multiple genetic variants simultaneously. Common variants within glutamatergic and GABAergic genes were investigated using the MAGMA software in an ADHD case-only sample (n=931), in which we assessed ASD symptoms and response inhibition on a Stop task. Gene set analysis for ADHD symptom severity, divided into inattention and hyperactivity/impulsivity symptoms, autism symptom severity and inhibition were performed using principal component regression analyses. Subsequently, gene-wide association analyses were performed. The glutamate gene set showed an association with severity of hyperactivity/impulsivity (P=0.009), which was robust to correcting for genome-wide association levels. The GABA gene set showed nominally significant association with inhibition (P=0.04), but this did not survive correction for multiple comparisons. None of single gene or single variant associations was significant on their own. By analyzing multiple genetic variants within candidate gene sets together, we were able to find genetic associations supporting the involvement of excitatory and inhibitory neurotransmitter systems in ADHD and ASD symptom severity in ADHD. PMID:28072412

Memory for found targets interferes with subsequent performance in multiple-target visual search.

PubMed

Cain, Matthew S; Mitroff, Stephen R

2013-10-01

Multiple-target visual searches--when more than 1 target can appear in a given search display--are commonplace in radiology, airport security screening, and the military. Whereas 1 target is often found accurately, additional targets are more likely to be missed in multiple-target searches. To better understand this decrement in 2nd-target detection, here we examined 2 potential forms of interference that can arise from finding a 1st target: interference from the perceptual salience of the 1st target (a now highly relevant distractor in a known location) and interference from a newly created memory representation for the 1st target. Here, we found that removing found targets from the display or making them salient and easily segregated color singletons improved subsequent search accuracy. However, replacing found targets with random distractor items did not improve subsequent search accuracy. Removing and highlighting found targets likely reduced both a target's visual salience and its memory load, whereas replacing a target removed its visual salience but not its representation in memory. Collectively, the current experiments suggest that the working memory load of a found target has a larger effect on subsequent search accuracy than does its perceptual salience. PsycINFO Database Record (c) 2013 APA, all rights reserved.

The Impact of Multiple Malignancies on Patients with Bladder Carcinoma: A Population-Based Study Using the SEER Database

PubMed Central

Ehrlich, Joshua R.; Schwartz, Michael J.; Ng, Casey K.; Kauffman, Eric C.; Scherr, Douglas S.

2009-01-01

Purpose. To date, no study has examined a population-based registry to determine the impact of multiple malignancies on survival of bladder cancer patients. Our experience suggests that bladder cancer patients with multiple malignancies may have relatively positive outcomes. Materials & Methods. We utilized data from the Surveillance Epidemiology and End Results (SEERs) database to examine survival between patients with only bladder cancer (BO) and with bladder cancer and additional cancer(s) antecedent (AB), subsequent (BS), or antecedent and subsequent to bladder cancer (ABS). Results. Analyses demonstrated diminished survival among AB and ABS cohorts. However, when cohorts were substratified by stage, patients in the high-stage BS cohort appeared to have a survival advantage over high-stage BO patients. Conclusions. Bladder cancer patients with multiple malignancies have diminished survival. The survival advantage of high-stage BS patients is likely a statistical phenomenon. Such findings are important to shape future research and to improve our understanding of patients with multiple malignancies. PMID:20069054

Intimate partner violence: associations with low infant birthweight in a South African birth cohort

PubMed Central

Wyatt, Gail E.; Williams, John K.; Zhang, Muyu; Myer, Landon; Zar, Heather J.; Stein, Dan J.

2015-01-01

Violence against women is a global public health problem. Exposure to intimate partner violence (IPV) during pregnancy has been associated with a number of adverse maternal and fetal outcomes, including delivery of a low birthweight (LBW) infant. However, there is a paucity of data from low-middle income countries (LMIC). We examined the association between antenatal IPV and subsequent LBW in a South African birth cohort. This study reports data from the Drakenstein Child Lung Health Study (DCLHS), a multidisciplinary birth cohort investigation of the influence of a number of antecedent risk factors on maternal and infant health outcomes over time. Pregnant women seeking antenatal care were recruited at two different primary care clinics in a low income, semi-rural area outside Cape Town, South Africa. Antenatal trauma exposure was assessed using the Childhood Trauma Questionnaire (CTQ) and an IPV assessment tool specifically designed for the purposes of this study. Potential confounding variables including maternal sociodemographics, pregnancy intention, partner support, biomedical and mental illness, substance use and psychosocial risk were also assessed. Bivariate and multiple regression analyses were performed to determine the association between IPV during pregnancy and delivery of an infant with LBW and/or low weight-for-age z (WAZ) scores. The final study sample comprised 263 mother-infant dyads. In multiple regression analyses, the model run was significant [r2=0.14 (adjusted r2=0.11, F(8, 212) = 4.16, p=0.0001]. Exposure to physical IPV occurring during the past year was found to be significantly associated with LBW [t=−2.04, p=0.0429] when controlling for study site (clinic), maternal height, ethnicity, socioeconomic status, substance use and childhood trauma. A significant association with decreased WAZ scores was not demonstrated. Exposure of pregnant women to IPV may impact newborn health. Further research is needed in this field to assess the relevant underlying mechanisms, to inform public health policies and to develop appropriate trauma IPV interventions for LMIC settings. PMID:24729207

Associations between disaster exposures, peritraumatic distress, and posttraumatic stress responses in Fukushima nuclear plant workers following the 2011 nuclear accident: the Fukushima NEWS Project study.

PubMed

Shigemura, Jun; Tanigawa, Takeshi; Nishi, Daisuke; Matsuoka, Yutaka; Nomura, Soichiro; Yoshino, Aihide

2014-01-01

The 2011 Fukushima Daiichi Nuclear Power Plant accident was the worst nuclear disaster since Chernobyl. The nearby Daini plant also experienced substantial damage but remained intact. Workers for the both plants experienced multiple stressors as disaster victims and workers, as well as the criticism from the public due to their company's post-disaster management. Little is known about the psychological pathway mechanism from nuclear disaster exposures, distress during and immediately after the event (peritraumatic distress; PD), to posttraumatic stress responses (PTSR). A self-report questionnaire was administered to 1,411 plant employees (Daiichi, n = 831; Daini, n = 580) 2-3 months post-disaster (total response rate: 80.2%). The socio-demographic characteristics and disaster-related experiences were assessed as independent variables. PD and PTSR were measured by the Japanese versions of Peritraumatic Distress Inventory and the Impact of Event Scale-Revised, respectively. The analysis was conducted separately for the two groups. Bivariate regression analyses were performed to assess the relationships between independent variables, PD, and PTSR. Significant variables were subsequently entered in the multiple regression analyses to explore the pathway mechanism for development of PTSR. For both groups, PTSR highly associated with PD (Daiichi: adjusted β, 0.66; p<0.001; vs. Daini: adjusted β, 0.67; p<0.001). PTSR also associated with discrimination/slurs experience (Daiichi: 0.11; p<0.001; vs. Daini, 0.09; p = 0.005) and presence of preexisting illness(es) (Daiichi: 0.07; p = 0.005; vs. Daini: 0.15; p<.0001). Other disaster-related variables were likely to be associated with PD than PTSR. Among the Fukushima nuclear plant workers, disaster exposures associated with PD. PTSR was highly affected by PD along with discrimination/slurs experience.

Anxiety and Depression Among Adult Patients With Diabetic Foot: Prevalence and Associated Factors.

PubMed

Ahmad, Ali; Abujbara, Mousa; Jaddou, Hashem; Younes, Nidal A; Ajlouni, Kamel

2018-05-01

Diabetic foot is a frequent complication of diabetes mellitus with subsequent disturbances in the daily life of the patients. The co-existence of depression and anxiety among diabetic foot patients is a common phenomenon and the role of each of them in perpetuating the other is highlighted in the literature. Our study aimed to determine the prevalence rates of anxiety and depression, and to examine the associated risk factors among diabetic foot patients. This is a cross-sectional study. A total of 260 diabetic foot patients in the Diabetic Foot Clinic at the National Center for Diabetes, Endocrinology and Genetics (NCDEG), Amman, Jordan, participated in the study. Sociodemographic and health data were gathered through review of medical charts and a structured questionnaire. Depression and anxiety status were also assessed. The Generalized Anxiety Disorder Scale (GAD-7) was used to screen for anxiety and the Patient Health Questionnaire (PHQ-9) was used to screen for depression. A cutoff of ≥ 10 was used for each scale to identify those who tested positive for anxiety and depression. Prevalence rate of anxiety was 37.7% and that of depression was 39.6%. Multiple logistic regression analysis showed that anxiety is positively associated with duration of diabetes of < 10 years (P = 0.01), with ≥ three comorbid diseases (P = 0.00), and HbA1c level of > 7% (P = 0.03). Multiple logistic regression analysis also showed that depression is positively associated with patients of < 50 years of age (P = 0.03), females (P = 0.01), current smokers (P = 0.01), patients with foot ulcer duration ≥ 7 months (P = 0.00), with ≥ three comorbid diseases (P = 0.00) than their counterparts. Anxiety and depression are widely prevalent among diabetic foot patients. Mental health status of those patients gets even worse among those suffering other comorbid diseases, which was a finding that requires special attention in the management of patients with diabetic foot.

Associations between Disaster Exposures, Peritraumatic Distress, and Posttraumatic Stress Responses in Fukushima Nuclear Plant Workers following the 2011 Nuclear Accident: The Fukushima NEWS Project Study

PubMed Central

Shigemura, Jun; Tanigawa, Takeshi; Nishi, Daisuke; Matsuoka, Yutaka; Nomura, Soichiro; Yoshino, Aihide

2014-01-01

Background The 2011 Fukushima Daiichi Nuclear Power Plant accident was the worst nuclear disaster since Chernobyl. The nearby Daini plant also experienced substantial damage but remained intact. Workers for the both plants experienced multiple stressors as disaster victims and workers, as well as the criticism from the public due to their company's post-disaster management. Little is known about the psychological pathway mechanism from nuclear disaster exposures, distress during and immediately after the event (peritraumatic distress; PD), to posttraumatic stress responses (PTSR). Methods A self-report questionnaire was administered to 1,411 plant employees (Daiichi, n = 831; Daini, n = 580) 2–3 months post-disaster (total response rate: 80.2%). The socio-demographic characteristics and disaster-related experiences were assessed as independent variables. PD and PTSR were measured by the Japanese versions of Peritraumatic Distress Inventory and the Impact of Event Scale-Revised, respectively. The analysis was conducted separately for the two groups. Bivariate regression analyses were performed to assess the relationships between independent variables, PD, and PTSR. Significant variables were subsequently entered in the multiple regression analyses to explore the pathway mechanism for development of PTSR. Results For both groups, PTSR highly associated with PD (Daiichi: adjusted β, 0.66; p<0.001; vs. Daini: adjusted β, 0.67; p<0.001). PTSR also associated with discrimination/slurs experience (Daiichi: 0.11; p<0.001; vs. Daini, 0.09; p = 0.005) and presence of preexisting illness(es) (Daiichi: 0.07; p = 0.005; vs. Daini: 0.15; p<.0001). Other disaster-related variables were likely to be associated with PD than PTSR. Conclusion Among the Fukushima nuclear plant workers, disaster exposures associated with PD. PTSR was highly affected by PD along with discrimination/slurs experience. PMID:24586278

Leukoencephalopathy in childhood hematopoietic neoplasm caused by moderate-dose methotrexate and prophylactic cranial radiotherapy - an MR analysis

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

Matsumoto, Ko; Takahashi, Shoki; Higano, Shuichi

1995-07-15

The main purpose of this study was to determine influential factors related to minor leukoencephalopathy (LEP) caused by moderate-dose methotrexate (MTX) and prophylactic cranial radiotherapy (CRT) in childhood hematopoietic malignancies. We also compared the incidence of LEP following this treatment to that reported in the literature following treatment with high-dose MTX alone. Thirty-eight pediatric patients of hematopoietic malignancies (37 acute lymphoblastic leukemias, 1 non-Hodgkin lymphoma) who were given CRT (18-24 Gy) as well as prophylactic intrathecal and per os MTX were studied for leukoencephalopathy by magnetic resonance (MR) imaging. All the patients were free from grave neuropsychiatric disturbances. The datamore » were examined to elucidate the influential ones of five factors (patients` age, doses of intrathecal and per os MTX, dose of CRT, interval between treatment, and MR study) to develop LEP using multiple regression analysis. To compare the effect of moderate-dose MTX and prophylactic CRT on LEP to that of high-dose MTX alone, we conducted a literature review. Seven out of 38 patients (18%) developed LEP. From multiple regression analysis and partial correlation coefficients, the age and CRT dose seemed influential in the subsequent development of LEP. The incidence of LEP following treatment with moderate-dose MTX and prophylactic CRT appears to be less than that reported in the literature following treatment with intravenous high-dose MTX. However, even moderate-dose MTX in combination with CRT can result in a significant incidence of MR-detectable LEP, particularly in children 6 years of age or younger receiving 24 Gy. Leukoencephalopathy was caused by moderate-dose MTX and prophylactic CRT in pediatric patients, probably less frequently than by high-dose MTX treatment alone. The influential factors were patient`s age and CRT dose. 26 refs., 6 figs., 2 tabs.« less

The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Detection of epistatic effects with logic regression and a classical linear regression model.

PubMed

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

Analysis and Simulation of Disadvantaged Receivers for Multiple-Input Multiple-Output Communications Systems

DTIC Science & Technology

2010-09-01

SIMULATION OF DISADVANTAGED RECEIVERS FOR MULTIPLE-INPUT MULTIPLE- OUTPUT COMMUNICATIONS SYSTEMS by Tracy A. Martin September 2010 Thesis...DATE September 2010 3. REPORT TYPE AND DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE Analysis and Simulation of Disadvantaged Receivers...Channel State Information at the Transmitter (CSIT). A disadvantaged receiver is subsequently introduced to the system lacking the optimization enjoyed

Multiple Primary Cancer Monograph

Cancer.gov

To identify groups of cancer survivors that are at increased risk for multiple primary cancers, investigators led an effort to provide the first comprehensive population-based analysis of the risk of subsequent cancer in the U.S., resulting in a monograph.

Regression in autistic spectrum disorders.

PubMed

Stefanatos, Gerry A

2008-12-01

A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

High Frequency of Neuroimaging Abnormalities Among Pediatric Patients With Sepsis Who Undergo Neuroimaging.

PubMed

Sandquist, Mary K; Clee, Mark S; Patel, Smruti K; Howard, Kelli A; Yunger, Toni; Nagaraj, Usha D; Jones, Blaise V; Fei, Lin; Vadivelu, Sudhakar; Wong, Hector R

2017-07-01

This study was intended to describe and correlate the neuroimaging findings in pediatric patients after sepsis. Retrospective chart review. Single tertiary care PICU. Patients admitted to Cincinnati Children's Hospital Medical Center with a discharge diagnosis of sepsis or septic shock between 2004 and 2013 were crossmatched with patients who underwent neuroimaging during the same time period. All neuroimaging studies that occurred during or subsequent to a septic event were reviewed, and all new imaging findings were recorded and classified. As many patients experienced multiple septic events and/or had multiple neuroimaging studies after sepsis, our statistical analysis utilized the most recent or "final" imaging study available for each patient so that only brain imaging findings that persisted were included. A total of 389 children with sepsis and 1,705 concurrent or subsequent neuroimaging studies were included in the study. Median age at first septic event was 3.4 years (interquartile range, 0.7-11.5). Median time from first sepsis event to final neuroimaging was 157 days (interquartile range, 10-1,054). The most common indications for final imaging were follow-up (21%), altered mental status (18%), and fever/concern for infection (15%). Sixty-three percentage (n = 243) of final imaging studies demonstrated abnormal findings, the most common of which were volume loss (39%) and MRI signal and/or CT attenuation abnormalities (21%). On multivariable logistic regression, highest Pediatric Risk of Mortality score and presence of oncologic diagnosis/organ transplantation were independently associated with any abnormal final neuroimaging study findings (odds ratio, 1.032; p = 0.048 and odds ratio, 1.632; p = 0.041), although early timing of neuroimaging demonstrated a negative association (odds ratio, 0.606; p = 0.039). The most common abnormal finding of volume loss was independently associated with highest Pediatric Risk of Mortality score (odds ratio, 1.037; p = 0.016) and oncologic diagnosis/organ transplantation (odds ratio, 2.207; p = 0.001) and was negatively associated with early timing of neuroimaging (odds ratio, 0.575; p = 0.037). The majority of pediatric patients with sepsis and concurrent or subsequent neuroimaging have abnormal neuroimaging findings. The implications of this high incidence for long-term neurologic outcomes and follow-up require further exploration.

Interpret with caution: multicollinearity in multiple regression of cognitive data.

PubMed

Morrison, Catriona M

2003-08-01

Shibihara and Kondo in 2002 reported a reanalysis of the 1997 Kanji picture-naming data of Yamazaki, Ellis, Morrison, and Lambon-Ralph in which independent variables were highly correlated. Their addition of the variable visual familiarity altered the previously reported pattern of results, indicating that visual familiarity, but not age of acquisition, was important in predicting Kanji naming speed. The present paper argues that caution should be taken when drawing conclusions from multiple regression analyses in which the independent variables are so highly correlated, as such multicollinearity can lead to unreliable output.

STATLIB: NSWC Library of Statistical Programs and Subroutines

DTIC Science & Technology

1989-08-01

Uncorrelated Weighted Polynomial Regression 41 .WEPORC Correlated Weighted Polynomial Regression 45 MROP Multiple Regression Using Orthogonal Polynomials ...could not and should not be con- NSWC TR 89-97 verted to the new general purpose computer (the current CDC 995). Some were designed tu compute...personal computers. They are referred to as SPSSPC+, BMDPC, and SASPC and in general are less comprehensive than their mainframe counterparts. The basic

Return to work outcomes for workers with mental health conditions: A retrospective cohort study.

PubMed

Prang, Khic-Houy; Bohensky, Megan; Smith, Peter; Collie, Alex

2016-01-01

The aims of this study were to describe predictors of sustained return to work (RTW) among a cohort of workers with compensated work-related mental health conditions (MHCs); and to examine predictors of subsequent absences due to the same condition. This study was a retrospective analysis of compensation claims data in Victoria, Australia. We selected workers with an accepted wage replacement claim due to a work-related MHC from 1 January 2002 to 31 December 2009, with two years of follow-up data. We identified 8358 workers meeting our inclusion criteria. The median age of workers was 44 years (Interquartile range (IQR): 36-51) and 56% were female. In a multivariable Cox regression analysis, older age, being from a small organisation, working in some specific industry segments, consulting a psychiatrist or psychologist, using medications, and having a previous claim were all associated with a delayed RTW. Workers experiencing work pressure, assault/workplace violence or other mental stress factors, working in the public administration and safety industry and having a medical incapacity certification between 3-4 days and 5-7 days had a higher rate of multiple RTW attempts. This study identified a number of risk factors associated with a delayed RTW and multiple attempts at RTW. Predictors may help identify high-risk groups and facilitate the RTW process of workers with MHCs. Copyright © 2015 Elsevier Ltd. All rights reserved.

Stigmatizing Reactions to Intimate Partner Violence Disclosure and Depression: Examining the Mediating Role of Coping Strategies

PubMed Central

Overstreet, Nicole M.; Willie, Tiara C.; Sullivan, Tami P.

2017-01-01

Objective Despite increased attention to the relation between negative social reactions to intimate partner violence (IPV) disclosure and poorer mental health outcomes for victims, research has yet to examine whether certain types of negative social reactions are associated with poorer mental health outcomes more so than others. Further, research is scarce on potential mediators of this relationship. To fill these gaps, the current study examines whether stigmatizing reactions to IPV disclosure, such as victim-blaming responses and minimizing experiences of IPV, are a specific type of negative social reaction that exerts greater influence on women’s depressive symptoms than general negative reactions, such as being angry at the perpetrators of IPV. We also examine avoidance coping as a key mediator of this relationship. Methods A cross sectional correlational study was conducted to examine these relationships. Participants were 212 women from an urban northeast community who indicated being physically victimized by their male partner in the past six months. Results Findings from a multiple regression analysis showed that stigmatizing reactions, not general negative reactions, predicted women’s depressive symptoms. In addition, a multiple mediation analysis revealed that avoidance coping strategies, but not approach coping strategies, significantly accounted for the relationship between stigmatizing social reactions and women’s depressive symptoms. Conclusions Findings have implications for improving support from informal and formal sources and subsequently, IPV exposed women’s psychological well-being. PMID:27296052

Patterns and predictors of vaginal bleeding in the first trimester of pregnancy

PubMed Central

Hasan, Reem; Baird, Donna D.; Herring, Amy H.; Olshan, Andrew F.; Jonsson Funk, Michele L.; Hartmann, Katherine E.

2010-01-01

Purpose Although first-trimester vaginal bleeding is an alarming symptom, few studies have investigated the prevalence and predictors of early bleeding. This study characterizes first trimester bleeding, setting aside bleeding that occurs at time of miscarriage. Methods Participants (n=4539) were women ages 18–45 enrolled in Right From the Start, a community-based pregnancy study (2000–2008). Bleeding information included timing, heaviness, duration, color, and associated pain, as well as recurrence risk in subsequent pregnancies. Life table analyses were used to describe gestational timing of bleeding. Factors associated with bleeding were investigated using multiple logistic regression, with multiple imputation for missing data. Results Approximately one-fourth of participants (n=1207) reported bleeding (n=1656 episodes), but only 8% of women with bleeding reported heavy bleeding. Of the spotting and light bleeding episodes (n=1555), 28% were associated with pain. Among heavy episodes (n=100), 54% were associated with pain. Most episodes lasted less than 3 days, and most occurred between gestational weeks 5–8. Twelve percent of women with bleeding and 13% of those without experienced miscarriage. Maternal characteristics associated with bleeding included fibroids and prior miscarriage. Conclusions Consistent with the hypothesis that bleeding is a marker for placental dysfunction, bleeding is most likely to be seen around the time of the luteal-placental shift. PMID:20538195

Adverse childhood experiences and health anxiety in adulthood.

PubMed

Reiser, Sarah J; McMillan, Katherine A; Wright, Kristi D; Asmundson, Gordon J G

2014-03-01

Childhood experiences are thought to predispose a person to the development of health anxiety later in life. However, there is a lack of research investigating the influence of specific adverse experiences (e.g., childhood abuse, household dysfunction) on this condition. The current study examined the cumulative influence of multiple types of childhood adversities on health anxiety in adulthood. Adults 18-59 years of age (N=264) completed a battery of measures to assess adverse childhood experiences, health anxiety, and associated constructs (i.e., negative affect and trait anxiety). Significant associations were observed between adverse childhood experiences, health anxiety, and associated constructs. Hierarchical multiple regression analysis indicted that adverse childhood experiences were predictive of health anxiety in adulthood; however, the unique contribution of these experience were no longer significant following the inclusion of the other variables of interest. Subsequently, mediation analyses indicated that both negative affect and trait anxiety independently mediated the relationship between adverse childhood experiences and health anxiety in adulthood. Increased exposure to adverse childhood experiences is associated with higher levels of health anxiety in adulthood; this relationship is mediated through negative affect and trait anxiety. Findings support the long-term negative impact of cumulative adverse childhood experiences and emphasize the importance of addressing negative affect and trait anxiety in efforts to prevent and treat health anxiety. Copyright © 2013 Elsevier Ltd. All rights reserved.

A county-level cross-sectional analysis of positive deviance to assess multiple population health outcomes in Indiana.

PubMed

Hendryx, Michael; Guerra-Reyes, Lucia; Holland, Benjamin D; McGinnis, Michael Dean; Meanwell, Emily; Middlestadt, Susan E; Yoder, Karen M

2017-10-11

To test a positive deviance method to identify counties that are performing better than statistical expectations on a set of population health indicators. Quantitative, cross-sectional county-level secondary analysis of risk variables and outcomes in Indiana. Data are analysed using multiple linear regression to identify counties performing better or worse than expected given traditional risk indicators, with a focus on 'positive deviants' or counties performing better than expected. Counties in Indiana (n=92) constitute the unit of analysis. Per cent adult obesity, per cent fair/poor health, low birth weight per cent, per cent with diabetes, years of potential life lost, colorectal cancer incidence rate and circulatory disease mortality rate. County performance that outperforms expectations is for the most part outcome specific. But there are a few counties that performed particularly well across most measures. The positive deviance approach provides a means for state and local public health departments to identify places that show better health outcomes despite demographic, social, economic or behavioural disadvantage. These places may serve as case studies or models for subsequent investigations to uncover best practices in the face of adversity and generalise effective approaches to other areas. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Multi-timescale sediment responses across a human impacted river-estuary system

NASA Astrophysics Data System (ADS)

Chen, Yining; Chen, Nengwang; Li, Yan; Hong, Huasheng

2018-05-01

Hydrological processes regulating sediment transport from land to sea have been widely studied. However, anthropogenic factors controlling the river flow-sediment regime and subsequent response of the estuary are still poorly understood. Here we conducted a multi-timescale analysis on flow and sediment discharges during the period 1967-2014 for the two tributaries of the Jiulong River in Southeast China. The long-term flow-sediment relationship remained linear in the North River throughout the period, while the linearity showed a remarkable change after 1995 in the West River, largely due to construction of dams and reservoirs in the upland watershed. Over short timescales, rainstorm events caused the changes of suspended sediment concentration (SSC) in the rivers. Regression analysis using synchronous SSC data in a wet season (2009) revealed a delayed response (average 5 days) of the estuary to river input, and a box-model analysis established a quantitative relationship to further describe the response of the estuary to the river sediment input over multiple timescales. The short-term response is determined by both the vertical SSC-salinity changes and the sediment trapping rate in the estuary. However, over the long term, the reduction of riverine sediment yield increased marine sediments trapped into the estuary. The results of this study indicate that human activities (e.g., dams) have substantially altered sediment delivery patterns and river-estuary interactions at multiple timescales.

Parametric optimization of multiple quality characteristics in laser cutting of Inconel-718 by using hybrid approach of multiple regression analysis and genetic algorithm

NASA Astrophysics Data System (ADS)

Shrivastava, Prashant Kumar; Pandey, Arun Kumar

2018-06-01

Inconel-718 has found high demand in different industries due to their superior mechanical properties. The traditional cutting methods are facing difficulties for cutting these alloys due to their low thermal potential, lower elasticity and high chemical compatibility at inflated temperature. The challenges of machining and/or finishing of unusual shapes and/or sizes in these materials have also faced by traditional machining. Laser beam cutting may be applied for the miniaturization and ultra-precision cutting and/or finishing by appropriate control of different process parameter. This paper present multi-objective optimization the kerf deviation, kerf width and kerf taper in the laser cutting of Incone-718 sheet. The second order regression models have been developed for different quality characteristics by using the experimental data obtained through experimentation. The regression models have been used as objective function for multi-objective optimization based on the hybrid approach of multiple regression analysis and genetic algorithm. The comparison of optimization results to experimental results shows an improvement of 88%, 10.63% and 42.15% in kerf deviation, kerf width and kerf taper, respectively. Finally, the effects of different process parameters on quality characteristics have also been discussed.

Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model.

PubMed

Seaman, Shaun R; Hughes, Rachael A

2018-06-01

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

Imputation method for lifetime exposure assessment in air pollution epidemiologic studies

PubMed Central

2013-01-01

Background Environmental epidemiology, when focused on the life course of exposure to a specific pollutant, requires historical exposure estimates that are difficult to obtain for the full time period due to gaps in the historical record, especially in earlier years. We show that these gaps can be filled by applying multiple imputation methods to a formal risk equation that incorporates lifetime exposure. We also address challenges that arise, including choice of imputation method, potential bias in regression coefficients, and uncertainty in age-at-exposure sensitivities. Methods During time periods when parameters needed in the risk equation are missing for an individual, the parameters are filled by an imputation model using group level information or interpolation. A random component is added to match the variance found in the estimates for study subjects not needing imputation. The process is repeated to obtain multiple data sets, whose regressions against health data can be combined statistically to develop confidence limits using Rubin’s rules to account for the uncertainty introduced by the imputations. To test for possible recall bias between cases and controls, which can occur when historical residence location is obtained by interview, and which can lead to misclassification of imputed exposure by disease status, we introduce an “incompleteness index,” equal to the percentage of dose imputed (PDI) for a subject. “Effective doses” can be computed using different functional dependencies of relative risk on age of exposure, allowing intercomparison of different risk models. To illustrate our approach, we quantify lifetime exposure (dose) from traffic air pollution in an established case–control study on Long Island, New York, where considerable in-migration occurred over a period of many decades. Results The major result is the described approach to imputation. The illustrative example revealed potential recall bias, suggesting that regressions against health data should be done as a function of PDI to check for consistency of results. The 1% of study subjects who lived for long durations near heavily trafficked intersections, had very high cumulative exposures. Thus, imputation methods must be designed to reproduce non-standard distributions. Conclusions Our approach meets a number of methodological challenges to extending historical exposure reconstruction over a lifetime and shows promise for environmental epidemiology. Application to assessment of breast cancer risks will be reported in a subsequent manuscript. PMID:23919666

A kinetic model of municipal sludge degradation during non-catalytic wet oxidation.

PubMed

Prince-Pike, Arrian; Wilson, David I; Baroutian, Saeid; Andrews, John; Gapes, Daniel J

2015-12-15

Wet oxidation is a successful process for the treatment of municipal sludge. In addition, the resulting effluent from wet oxidation is a useful carbon source for subsequent biological nutrient removal processes in wastewater treatment. Owing to limitations with current kinetic models, this study produced a kinetic model which predicts the concentrations of key intermediate components during wet oxidation. The model was regressed from lab-scale experiments and then subsequently validated using data from a wet oxidation pilot plant. The model was shown to be accurate in predicting the concentrations of each component, and produced good results when applied to a plant 500 times larger in size. A statistical study was undertaken to investigate the validity of the regressed model parameters. Finally the usefulness of the model was demonstrated by suggesting optimum operating conditions such that volatile fatty acids were maximised. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of third molar development and eruption models in estimating dental age in Malay sub-adults.

PubMed

Mohd Yusof, Mohd Yusmiaidil Putera; Cauwels, Rita; Deschepper, Ellen; Martens, Luc

2015-08-01

The third molar development (TMD) has been widely utilized as one of the radiographic method for dental age estimation. By using the same radiograph of the same individual, third molar eruption (TME) information can be incorporated to the TMD regression model. This study aims to evaluate the performance of dental age estimation in individual method models and the combined model (TMD and TME) based on the classic regressions of multiple linear and principal component analysis. A sample of 705 digital panoramic radiographs of Malay sub-adults aged between 14.1 and 23.8 years was collected. The techniques described by Gleiser and Hunt (modified by Kohler) and Olze were employed to stage the TMD and TME, respectively. The data was divided to develop three respective models based on the two regressions of multiple linear and principal component analysis. The trained models were then validated on the test sample and the accuracy of age prediction was compared between each model. The coefficient of determination (R²) and root mean square error (RMSE) were calculated. In both genders, adjusted R² yielded an increment in the linear regressions of combined model as compared to the individual models. The overall decrease in RMSE was detected in combined model as compared to TMD (0.03-0.06) and TME (0.2-0.8). In principal component regression, low value of adjusted R(2) and high RMSE except in male were exhibited in combined model. Dental age estimation is better predicted using combined model in multiple linear regression models. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

On the method of Ermakov and Zolotukhin for multiple integration

NASA Technical Reports Server (NTRS)

Cranley, R.; Patterson, T. N. L.

1971-01-01

By introducing the idea of pseudo-implementation, a practical assessment of the method for multiple integration is made. The performance of the method is found to be unimpressive in comparison with a recent regression method.

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R.

1982-01-01

An examination of limitations, requirements, and precision of the linear multiple-regression technique for quantification of marine environmental parameters is conducted. Both environmental and optical physics conditions have been defined for which an exact solution to the signal response equations is of the same form as the multiple regression equation. Various statistical parameters are examined to define a criteria for selection of an unbiased fit when upwelled radiance values contain error and are correlated with each other. Field experimental data are examined to define data smoothing requirements in order to satisfy the criteria of Daniel and Wood (1971). Recommendations are made concerning improved selection of ground-truth locations to maximize variance and to minimize physical errors associated with the remote sensing experiment.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Comparison of Different Risk Perception Measures in Predicting Seasonal Influenza Vaccination among Healthy Chinese Adults in Hong Kong: A Prospective Longitudinal Study

PubMed Central

Liao, Qiuyan; Wong, Wing Sze; Fielding, Richard

2013-01-01

Background Risk perception is a reported predictor of vaccination uptake, but which measures of risk perception best predict influenza vaccination uptake remain unclear. Methodology During the main influenza seasons (between January and March) of 2009 (Wave 1) and 2010 (Wave 2),505 Chinese students and employees from a Hong Kong university completed an online survey. Multivariate logistic regression models were conducted to assess how well different risk perceptions measures in Wave 1 predicted vaccination uptake against seasonal influenza in Wave 2. Principal Findings The results of the multivariate logistic regression models showed that feeling at risk (β = 0.25, p = 0.021) was the better predictor compared with probability judgment while probability judgment (β = 0.25, p = 0.029 ) was better than beliefs about risk in predicting subsequent influenza vaccination uptake. Beliefs about risk and feeling at risk seemed to predict the same aspect of subsequent vaccination uptake because their associations with vaccination uptake became insignificant when paired into the logistic regression model. Similarly, to compare the four scales for assessing probability judgment in predicting vaccination uptake, the 7-point verbal scale remained a significant and stronger predictor for vaccination uptake when paired with other three scales; the 6-point verbal scale was a significant and stronger predictor when paired with the percentage scale or the 2-point verbal scale; and the percentage scale was a significant and stronger predictor only when paired with the 2-point verbal scale. Conclusions/Significance Beliefs about risk and feeling at risk are not well differentiated by Hong Kong Chinese people. Feeling at risk, an affective-cognitive dimension of risk perception predicts subsequent vaccination uptake better than do probability judgments. Among the four scales for assessing risk probability judgment, the 7-point verbal scale offered the best predictive power for subsequent vaccination uptake. PMID:23894292

Comparison of different risk perception measures in predicting seasonal influenza vaccination among healthy Chinese adults in Hong Kong: a prospective longitudinal study.

PubMed

Liao, Qiuyan; Wong, Wing Sze; Fielding, Richard

2013-01-01

Risk perception is a reported predictor of vaccination uptake, but which measures of risk perception best predict influenza vaccination uptake remain unclear. During the main influenza seasons (between January and March) of 2009 (Wave 1) and 2010 (Wave 2),505 Chinese students and employees from a Hong Kong university completed an online survey. Multivariate logistic regression models were conducted to assess how well different risk perceptions measures in Wave 1 predicted vaccination uptake against seasonal influenza in Wave 2. The results of the multivariate logistic regression models showed that feeling at risk (β = 0.25, p = 0.021) was the better predictor compared with probability judgment while probability judgment (β = 0.25, p = 0.029 ) was better than beliefs about risk in predicting subsequent influenza vaccination uptake. Beliefs about risk and feeling at risk seemed to predict the same aspect of subsequent vaccination uptake because their associations with vaccination uptake became insignificant when paired into the logistic regression model. Similarly, to compare the four scales for assessing probability judgment in predicting vaccination uptake, the 7-point verbal scale remained a significant and stronger predictor for vaccination uptake when paired with other three scales; the 6-point verbal scale was a significant and stronger predictor when paired with the percentage scale or the 2-point verbal scale; and the percentage scale was a significant and stronger predictor only when paired with the 2-point verbal scale. Beliefs about risk and feeling at risk are not well differentiated by Hong Kong Chinese people. Feeling at risk, an affective-cognitive dimension of risk perception predicts subsequent vaccination uptake better than do probability judgments. Among the four scales for assessing risk probability judgment, the 7-point verbal scale offered the best predictive power for subsequent vaccination uptake.

Multiscale sagebrush rangeland habitat modeling in southwest Wyoming

USGS Publications Warehouse

Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Coan, Michael J.; Bowen, Zachary H.

2009-01-01

Sagebrush-steppe ecosystems in North America have experienced dramatic elimination and degradation since European settlement. As a result, sagebrush-steppe dependent species have experienced drastic range contractions and population declines. Coordinated ecosystem-wide research, integrated with monitoring and management activities, would improve the ability to maintain existing sagebrush habitats. However, current data only identify resource availability locally, with rigorous spatial tools and models that accurately model and map sagebrush habitats over large areas still unavailable. Here we report on an effort to produce a rigorous large-area sagebrush-habitat classification and inventory with statistically validated products and estimates of precision in the State of Wyoming. This research employs a combination of significant new tools, including (1) modeling sagebrush rangeland as a series of independent continuous field components that can be combined and customized by any user at multiple spatial scales; (2) collecting ground-measured plot data on 2.4-meter imagery in the same season the satellite imagery is acquired; (3) effective modeling of ground-measured data on 2.4-meter imagery to maximize subsequent extrapolation; (4) acquiring multiple seasons (spring, summer, and fall) of an additional two spatial scales of imagery (30 meter and 56 meter) for optimal large-area modeling; (5) using regression tree classification technology that optimizes data mining of multiple image dates, ratios, and bands with ancillary data to extrapolate ground training data to coarser resolution sensors; and (6) employing rigorous accuracy assessment of model predictions to enable users to understand the inherent uncertainties. First-phase results modeled eight rangeland components (four primary targets and four secondary targets) as continuous field predictions. The primary targets included percent bare ground, percent herbaceousness, percent shrub, and percent litter. The four secondary targets included percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and sagebrush height (centimeters). Results were validated by an independent accuracy assessment with root mean square error (RMSE) values ranging from 6.38 percent for bare ground to 2.99 percent for sagebrush at the QuickBird scale and RMSE values ranging from 12.07 percent for bare ground to 6.34 percent for sagebrush at the full Landsat scale. Subsequent project phases are now in progress, with plans to deliver products that improve accuracies of existing components, model new components, complete models over larger areas, track changes over time (from 1988 to 2007), and ultimately model wildlife population trends against these changes. We believe these results offer significant improvement in sagebrush rangeland quantification at multiple scales and offer users products that have been rigorously validated.

Using regression equations built from summary data in the psychological assessment of the individual case: extension to multiple regression.

PubMed

Crawford, John R; Garthwaite, Paul H; Denham, Annie K; Chelune, Gordon J

2012-12-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because (a) not all psychologists are aware that regression equations can be built not only from raw data but also using only basic summary data for a sample, and (b) the computations involved are tedious and prone to error. In an attempt to overcome these barriers, Crawford and Garthwaite (2007) provided methods to build and apply simple linear regression models using summary statistics as data. In the present study, we extend this work to set out the steps required to build multiple regression models from sample summary statistics and the further steps required to compute the associated statistics for drawing inferences concerning an individual case. We also develop, describe, and make available a computer program that implements these methods. Although there are caveats associated with the use of the methods, these need to be balanced against pragmatic considerations and against the alternative of either entirely ignoring a pertinent data set or using it informally to provide a clinical "guesstimate." Upgraded versions of earlier programs for regression in the single case are also provided; these add the point and interval estimates of effect size developed in the present article.

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

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

PubMed

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

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

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

PubMed Central

Carvalho, Carlos; Gomes, Danielo G.; Agoulmine, Nazim; de Souza, José Neuman

2011-01-01

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction. PMID:22346626

Return to work after ill-health retirement in Scottish NHS staff and teachers.

PubMed

Brown, Judith; Gilmour, W Harper; Macdonald, Ewan B

2006-10-01

Most major public and private sector pension schemes have provision for ill-health retirement (IHR) for those who become too ill to continue to work before their normal retirement age. To compare the causes, process and outcomes of IHR in teachers and National Health Service (NHS) staff in Scotland. A total of 537 teachers and 863 NHS staff who retired due to ill-health between April 1998 and March 2000 were mailed an IHR questionnaire by the Scottish Public Pensions Agency. The response rate for teachers was 53% and for NHS staff 49%. The most common cause of IHR was musculoskeletal disorders for NHS staff and mental disorders for teachers. Teachers retired at a younger average age than NHS staff. Ninety-two per cent of NHS staff but only 11% of teachers attended occupational health services (OHS) prior to IHR. Eighteen per cent of NHS staff and 9% of teachers were offered part-time work by their current employer in response to their ill-health. Fifteen per cent of NHS staff and 5% of teachers were offered alternative work prior to retirement. Seventeen per cent of NHS staff and 36% of teachers subsequently found employment. Multiple logistic regression analyses showed the following variables as independent predictors of subsequent employment: occupational group, age group, sex, managerial responsibility and cause of IHR. Return to work after IHR suggests that some IHR could be avoided. Teachers had a higher rate of return to work and much less access to OHS.

Television and music video exposure and risk of adolescent alcohol use.

PubMed

Robinson, T N; Chen, H L; Killen, J D

1998-11-01

Alcohol use is frequently portrayed in television programming and advertising. Exposure to media portrayals of alcohol use may lead to increased drinking. To address this issue, we examined prospectively the associations between media exposure and alcohol use in adolescents. Prospective cohort study. Setting. Six public high schools in San Jose, California. Participants. Ninth-grade students (N = 1533; mean age = 14.6 years). Students reported hours of television, music video, and videotape viewing; computer and video game use; and lifetime and past 30 days' alcohol use at baseline and 18 months later. Associations between baseline media exposure and subsequent alcohol use were examined with multiple logistic regression. During the 18-month follow-up, 36.2% of baseline nondrinkers began drinking and 50.7% of baseline drinkers continued to drink. Onset of drinking was significantly associated with baseline hours of television viewing (odds ratio [OR] = 1.09; 95% confidence interval [95% CI] = 1.01-1.18), music video viewing (OR = 1.31; 95% CI = 1. 17-1.47), and videotape viewing (OR = 0.89; 95% CI = 0.79-0.99), controlling for age, sex, ethnicity, and other media use. Computer and video game use was not significantly associated with the subsequent onset of drinking. Among baseline drinkers, there were no significant associations between baseline media use and maintenance of drinking. Increased television and music video viewing are risk factors for the onset of alcohol use in adolescents. Attempts to prevent adolescent alcohol use should address the adverse influences of alcohol use in the media.

Can parents' concerns predict autism spectrum disorder? A prospective study of high-risk siblings from 6 to 36 months of age.

PubMed

Sacrey, Lori-Ann R; Zwaigenbaum, Lonnie; Bryson, Susan; Brian, Jessica; Smith, Isabel M; Roberts, Wendy; Szatmari, Peter; Roncadin, Caroline; Garon, Nancy; Novak, Christopher; Vaillancourt, Tracy; McCormick, Theresa; MacKinnon, Bonnie; Jilderda, Sanne; Armstrong, Vickie

2015-06-01

This prospective study characterized parents' concerns about infants at high risk for developing autism spectrum disorder (ASD; each with an older sibling with ASD) at multiple time points in the first 2 years, and assessed their relation to diagnostic outcome at 3 years. Parents of low-risk controls (LR) and high-risk infant siblings (HR) reported any concerns that they had regarding their children's development between 6 and 24 months of age regarding sleep, diet, sensory behavior, gross/fine motor skills, repetitive movements, communication, communication regression, social skills, play, and behavioral problems, using a parent concern form designed for this study. At 3 years of age, an independent, gold-standard diagnostic assessment for ASD was conducted for all participants. As predicted, parents of HR children who received an ASD diagnosis reported more concerns than parents of LR and HR children who did not have ASD. The total number of concerns predicted a subsequent diagnosis of ASD as early as 12 months within the HR group. Concerns regarding sensory behavior and motor development predicted a subsequent diagnosis of ASD as early as 6 months, whereas concerns about social communication and repetitive behaviors did not predict diagnosis of ASD until after 12 months. Parent-reported concerns can improve earlier recognition of ASD in HR children. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Association between zolpidem use and glaucoma risk: a Taiwanese population-based case-control study.

PubMed

Ho, Yi-Hao; Chang, Yue-Cune; Huang, Wei-Cheng; Chen, Hsin-Yi; Lin, Che-Chen; Sung, Fung-Chang

2015-01-01

To date, the relationship between zolpidem use and subsequent risk of glaucoma in a Taiwanese population has not been assessed. We used data from the National Health Insurance system to investigate whether zolpidem use was related to glaucoma risk. A 1:4 matched case-control study was conducted. The cases were patients newly diagnosed with glaucoma from 2001 to 2010. The controls were randomly selected non-glaucoma subjects matched by sex and age (± 5 years). Zolpidem exposure and/or the average dosage of zolpidem used (mg/year) were evaluated. Medical comorbidities were considered as confounding factors. Multiple logistic regression models were used to evaluate the potential risk of zolpidem exposure on glaucoma with/without adjustment for the effects of confounding variables. The exposure rate of zolpidem use in the glaucoma group was significantly higher than that of the control group (2.8% vs. 2.0%, P < 0.0001). The adjusted odds ratio (OR) of the risk of glaucoma for those with zolpidem use vs. those without was 1.19 (95% confidence interval [CI], 1.02-1.38). Compared to non-zolpidem users, zolpidem users with an average dose of more than 200 mg/year had significantly increased risk of glaucoma (OR 1.31, 95% CI 1.03-1.68). This study suggests that the use of zolpidem might increase the risk of subsequent glaucoma. Further confirmatory studies are recommended to clarify this important issue.

Association Between Zolpidem Use and Glaucoma Risk: A Taiwanese Population-Based Case-Control Study

PubMed Central

Ho, Yi-Hao; Chang, Yue-Cune; Huang, Wei-Cheng; Chen, Hsin-Yi; Lin, Che-Chen; Sung, Fung-Chang

2015-01-01

Background To date, the relationship between zolpidem use and subsequent risk of glaucoma in a Taiwanese population has not been assessed. Methods We used data from the National Health Insurance system to investigate whether zolpidem use was related to glaucoma risk. A 1:4 matched case-control study was conducted. The cases were patients newly diagnosed with glaucoma from 2001 to 2010. The controls were randomly selected non-glaucoma subjects matched by sex and age (±5 years). Zolpidem exposure and/or the average dosage of zolpidem used (mg/year) were evaluated. Medical comorbidities were considered as confounding factors. Multiple logistic regression models were used to evaluate the potential risk of zolpidem exposure on glaucoma with/without adjustment for the effects of confounding variables. Results The exposure rate of zolpidem use in the glaucoma group was significantly higher than that of the control group (2.8% vs. 2.0%, P < 0.0001). The adjusted odds ratio (OR) of the risk of glaucoma for those with zolpidem use vs. those without was 1.19 (95% confidence interval [CI], 1.02–1.38). Compared to non-zolpidem users, zolpidem users with an average dose of more than 200 mg/year had significantly increased risk of glaucoma (OR 1.31, 95% CI 1.03–1.68). Conclusions This study suggests that the use of zolpidem might increase the risk of subsequent glaucoma. Further confirmatory studies are recommended to clarify this important issue. PMID:25720944

Association Between Zolpidem Use and Glaucoma Risk: A Taiwanese Population-Based Case-Control Study.

PubMed

Ho, Yi-Hao; Chang, Yue-Cune; Huang, Wei-Cheng; Chen, Hsin-Yi; Lin, Che-Chen; Sung, Fung-Chang

2014-08-23

Background: To date, the relationship between zolpidem use and subsequent risk of glaucoma in a Taiwanese population has not been assessed.Methods: We used data from the National Health Insurance system to investigate whether zolpidem use was related to glaucoma risk. A 1:4 matched case-control study was conducted. The cases were patients newly diagnosed with glaucoma from 2001 to 2010. The controls were randomly selected non-glaucoma subjects matched by sex and age (±5 years). Zolpidem exposure and/or the average dosage of zolpidem used (mg/year) were evaluated. Medical comorbidities were considered as confounding factors. Multiple logistic regression models were used to evaluate the potential risk of zolpidem exposure on glaucoma with/without adjustment for the effects of confounding variables.Results: The exposure rate of zolpidem use in the glaucoma group was significantly higher than that of the control group (2.8% vs. 2.0%, P < 0.0001). The adjusted odds ratio (OR) of the risk of glaucoma for those with zolpidem use vs. those without was 1.19 (95% confidence interval [CI], 1.02-1.38). Compared to non-zolpidem users, zolpidem users with an average dose of more than 200 mg/year had significantly increased risk of glaucoma (OR 1.31, 95% CI 1.03-1.68).Conclusions: This study suggests that the use of zolpidem might increase the risk of subsequent glaucoma. Further confirmatory studies are recommended to clarify this important issue.

Does substantiated childhood maltreatment lead to poor quality of life in young adulthood? Evidence from an Australian birth cohort study.

PubMed

Abajobir, Amanuel Alemu; Kisely, Steve; Williams, Gail; Strathearn, Lane; Clavarino, Alexandra; Najman, Jake Moses

2017-07-01

To examine the independent effect of single and multiple forms of substantiated childhood maltreatment (CM) on quality of life (QoL), controlling for selected potential confounders and/or covariates, and concurrent depressive symptoms. We used data from a prospective pre-birth cohort of 8556 mothers recruited consecutively during their first antenatal clinic visit at the Mater Hospital from 1981 to 1983 in Brisbane, Australia. The data were linked to substantiated cases of CM reported to the child protection government agency up to the age of 14 years. The sample consisted of 3730 (49.7% female) young adults for whom there were complete data on QoL at the 21-year follow-up. The mean age of participants was 20.6 years. Logistic regression models were used to assess the association between CM and QoL measured at the 21-year follow-up. There were statistically significant associations between exposure to substantiated CM and poorer QoL. This also applied to the subcategories of childhood physical abuse, childhood emotional abuse (CEA), and neglect. These associations were generally stable after adjusting for confounders/covariates and concurrent depressive symptoms, except physical abuse. CEA with or without neglect significantly and particularly predicted worse subsequent QoL. Exposure to any substantiated maltreatment substantially contributed to worse QoL in young adulthood, with a particular association with CEA and neglect. Prior experiences of CM may have a substantial association with subsequent poorer QoL.

Multiple regression analysis in nomogram development for myopic wavefront laser in situ keratomileusis: Improving astigmatic outcomes.

PubMed

Allan, Bruce D; Hassan, Hala; Ieong, Alvin

2015-05-01

To describe and evaluate a new multiple regression-derived nomogram for myopic wavefront laser in situ keratomileusis (LASIK). Moorfields Eye Hospital, London, United Kingdom. Prospective comparative case series. Multiple regression modeling was used to derive a simplified formula for adjusting attempted spherical correction in myopic LASIK. An adaptation of Thibos' power vector method was then applied to derive adjustments to attempted cylindrical correction in eyes with 1.0 diopter (D) or more of preoperative cylinder. These elements were combined in a new nomogram (nomogram II). The 3-month refractive results for myopic wavefront LASIK (spherical equivalent ≤11.0 D; cylinder ≤4.5 D) were compared between 299 consecutive eyes treated using the earlier nomogram (nomogram I) in 2009 and 2010 and 414 eyes treated using nomogram II in 2011 and 2012. There was no significant difference in treatment accuracy (variance in the postoperative manifest refraction spherical equivalent error) between nomogram I and nomogram II (P = .73, Bartlett test). Fewer patients treated with nomogram II had more than 0.5 D of residual postoperative astigmatism (P = .0001, Fisher exact test). There was no significant coupling between adjustments to the attempted cylinder and the achieved sphere (P = .18, t test). Discarding marginal influences from a multiple regression-derived nomogram for myopic wavefront LASIK had no clinically significant effect on treatment accuracy. Thibos' power vector method can be used to guide adjustments to the treatment cylinder alongside nomograms designed to optimize postoperative spherical equivalent results in myopic LASIK. mentioned. Copyright © 2015 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

The relationship between quality of work life and turnover intention of primary health care nurses in Saudi Arabia.

PubMed

Almalki, Mohammed J; FitzGerald, Gerry; Clark, Michele

2012-09-12

Quality of work life (QWL) has been found to influence the commitment of health professionals, including nurses. However, reliable information on QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. A cross-sectional survey was used in this study. Data were collected using Brooks' survey of Quality of Nursing Work Life, the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan Region, Saudi Arabia, completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression, and hierarchical multiple regression were applied for analysis using SPSS v17 for Windows. Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using standard multiple regression, 26% of the variance in turnover intention was explained by QWL, p < 0.001, with R2 = .263. Further analysis using hierarchical multiple regression found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes.

The relationship between quality of work life and turnover intention of primary health care nurses in Saudi Arabia

PubMed Central

2012-01-01

Background Quality of work life (QWL) has been found to influence the commitment of health professionals, including nurses. However, reliable information on QWL and turnover intention of primary health care (PHC) nurses is limited. The aim of this study was to examine the relationship between QWL and turnover intention of PHC nurses in Saudi Arabia. Methods A cross-sectional survey was used in this study. Data were collected using Brooks’ survey of Quality of Nursing Work Life, the Anticipated Turnover Scale and demographic data questions. A total of 508 PHC nurses in the Jazan Region, Saudi Arabia, completed the questionnaire (RR = 87%). Descriptive statistics, t-test, ANOVA, General Linear Model (GLM) univariate analysis, standard multiple regression, and hierarchical multiple regression were applied for analysis using SPSS v17 for Windows. Results Findings suggested that the respondents were dissatisfied with their work life, with almost 40% indicating a turnover intention from their current PHC centres. Turnover intention was significantly related to QWL. Using standard multiple regression, 26% of the variance in turnover intention was explained by QWL, p < 0.001, with R2 = .263. Further analysis using hierarchical multiple regression found that the total variance explained by the model as a whole (demographics and QWL) was 32.1%, p < 0.001. QWL explained an additional 19% of the variance in turnover intention, after controlling for demographic variables. Conclusions Creating and maintaining a healthy work life for PHC nurses is very important to improve their work satisfaction, reduce turnover, enhance productivity and improve nursing care outcomes. PMID:22970764

Risk factors for autistic regression: results of an ambispective cohort study.

PubMed

Zhang, Ying; Xu, Qiong; Liu, Jing; Li, She-chang; Xu, Xiu

2012-08-01

A subgroup of children diagnosed with autism experience developmental regression featured by a loss of previously acquired abilities. The pathogeny of autistic regression is unknown, although many risk factors likely exist. To better characterize autistic regression and investigate the association between autistic regression and potential influencing factors in Chinese autistic children, we conducted an ambispective study with a cohort of 170 autistic subjects. Analyses by multiple logistic regression showed significant correlations between autistic regression and febrile seizures (OR = 3.53, 95% CI = 1.17-10.65, P = .025), as well as with a family history of neuropsychiatric disorders (OR = 3.62, 95% CI = 1.35-9.71, P = .011). This study suggests that febrile seizures and family history of neuropsychiatric disorders are correlated with autistic regression.

Relationship between accumulated heat stress during the dry period, body condition score, and reproduction parameters of Holstein cows in tropical conditions.

PubMed

Avendaño-Reyes, Leonel; Fuquay, John W; Moore, Reuben B; Liu, Zhanglin; Clark, Bruce L; Vierhout, C

2010-02-01

To estimate the relationship between heat stress during the last 60 days prepartum, body condition score and certain reproductive traits in the subsequent lactation of Holstein cows, 564 multiparous cows and 290 primiparous cows from four dairy herds were used in a hot, humid region. Maximum prepartum degree days were estimated to quantify the degree of heat stress. Multiple regressions analyses and logistic regression analysis were performed to determine the effect of prepartum heat stress and body condition change on reproductive parameters, which were obtained from DHIA forms at the end of the lactation. Multiparous and primiparous cows which gained body condition score from calving to 60 d postpartum exhibited 28 and 27 fewer days open (P < 0.05), respectively, than cows not gaining. There was no effect (P > 0.05) of heat stress measurement on days open or services per conception in either multiparous or primiparous cows. During hotter months of calving, multiparous cows showed higher services per conception and primiparous cows showed higher days open and services per conception (P < 0.05). Maximum prepartum degree-days were positively associated (P < 0.05) with calving difficulty score. Multiparous cows with high body condition score at calving were 1.47 times more likely to present a very difficult calving than cows that calved in October (P < 0.05). Collectively, these results suggest that reproductive performance was not affected by cumulative prepartum heat stress although it was associated with very difficult calving score.

Factors associated with Iowa rural hospitals' decision to convert to critical access hospital status.

PubMed

Li, Pengxiang; Ward, Marcia M; Schneider, John E

2009-01-01

The Balanced Budget Act (BBA) of 1997 allowed some rural hospitals meeting certain requirements to convert to Critical Access Hospitals (CAHs) and changed their Medicare reimbursement from prospective to cost-based. Some subsequent CAH-related laws reduced restrictions and increased payments, and the number of CAHs grew rapidly. To examine factors related to hospitals' decisions to convert and time to CAH conversion. Eighty-nine rural hospitals in Iowa were characterized and observed from 1998 to 2005. Cox proportional hazards models were used to identify the determinants of time to CAH conversion. T-test and one-covariate Cox regression indicated that, in 1998, Iowa rural hospitals with more staffed beds, discharges, and acute inpatient days, higher operating margin, lower skilled swing bed days relative to acute days, and located in relatively high density counties were more likely to convert later or not convert before 2006. Multiple Cox regression with baseline covariates indicated that lower number of discharges and average length of stay (ALOS) were significant after controlling all other covariates. Iowa rural hospitals' decisions regarding CAH conversion were influenced by hospital size, financial condition, skilled swing bed days relative to acute days, length of stay, proportion of Medicare acute days, and geographic factors. Although financial concerns are often cited in surveys as the main reason for conversion, lower number of discharges and ALOS are the most prominent factors affecting rural hospitals' decision on when to convert.

A fully traits-based approach to modeling global vegetation distribution.

PubMed

van Bodegom, Peter M; Douma, Jacob C; Verheijen, Lieneke M

2014-09-23

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.

The impact of workplace spirituality dimensions on organisational citizenship behaviour among nurses with the mediating effect of affective organisational commitment.

PubMed

Kazemipour, Farahnaz; Mohd Amin, Salmiah

2012-12-01

To investigate the relationship between workplace spirituality dimensions and organisational citizenship behaviour (OCB) among nurses through the mediating effect of affective organisational commitment. Nurses' OCB has been considered recently to improve the quality of services to patients and subsequently, their performance. As an influential attitude, affective organisational commitment has been recognized to influence OCB, and ultimately, organisational performance. Meanwhile, workplace spirituality is introduced as a new organisational behaviour concept to increase affective commitment influencing employees' OCB. The cross-sectional study and the respective data were collected with a questionnaire-based survey. The questionnaires were distributed to 305 nurses employed in four public and general Iranian hospitals. To analyse the data, descriptive statistics, Pearson coefficient, simple regression, multiple regression and path analyses were also conducted. The results indicated that workplace spirituality dimensions including meaningful work, a sense of community and an alignment with organisational values have a significant positive relationship with OCB. Moreover, affective organisational commitment mediated the impact of workplace spirituality on OCB. The concept of workplace spirituality through its dimensions predicts nurses' OCB, and affective organisational commitment partially mediated the relationship between workplace spirituality and OCB. Nurses' managers should consider the potentially positive influence of workplace spirituality on OCB and affective commitment among their nurses. With any plan to increase workplace spirituality, the respective managers can improve nurses' performance and would be of considerable importance in the healthcare system. © 2012 Blackwell Publishing Ltd.

Early Parallel Activation of Semantics and Phonology in Picture Naming: Evidence from a Multiple Linear Regression MEG Study

PubMed Central

Miozzo, Michele; Pulvermüller, Friedemann; Hauk, Olaf

2015-01-01

The time course of brain activation during word production has become an area of increasingly intense investigation in cognitive neuroscience. The predominant view has been that semantic and phonological processes are activated sequentially, at about 150 and 200–400 ms after picture onset. Although evidence from prior studies has been interpreted as supporting this view, these studies were arguably not ideally suited to detect early brain activation of semantic and phonological processes. We here used a multiple linear regression approach to magnetoencephalography (MEG) analysis of picture naming in order to investigate early effects of variables specifically related to visual, semantic, and phonological processing. This was combined with distributed minimum-norm source estimation and region-of-interest analysis. Brain activation associated with visual image complexity appeared in occipital cortex at about 100 ms after picture presentation onset. At about 150 ms, semantic variables became physiologically manifest in left frontotemporal regions. In the same latency range, we found an effect of phonological variables in the left middle temporal gyrus. Our results demonstrate that multiple linear regression analysis is sensitive to early effects of multiple psycholinguistic variables in picture naming. Crucially, our results suggest that access to phonological information might begin in parallel with semantic processing around 150 ms after picture onset. PMID:25005037

An Investigation of the Fit of Linear Regression Models to Data from an SAT[R] Validity Study. Research Report 2011-3

ERIC Educational Resources Information Center

Kobrin, Jennifer L.; Sinharay, Sandip; Haberman, Shelby J.; Chajewski, Michael

2011-01-01

This study examined the adequacy of a multiple linear regression model for predicting first-year college grade point average (FYGPA) using SAT[R] scores and high school grade point average (HSGPA). A variety of techniques, both graphical and statistical, were used to examine if it is possible to improve on the linear regression model. The results…

Determining Sample Size for Accurate Estimation of the Squared Multiple Correlation Coefficient.

ERIC Educational Resources Information Center

Algina, James; Olejnik, Stephen

2000-01-01

Discusses determining sample size for estimation of the squared multiple correlation coefficient and presents regression equations that permit determination of the sample size for estimating this parameter for up to 20 predictor variables. (SLD)

Comparison of two-concentration with multi-concentration linear regressions: Retrospective data analysis of multiple regulated LC-MS bioanalytical projects.

PubMed

Musuku, Adrien; Tan, Aimin; Awaiye, Kayode; Trabelsi, Fethi

2013-09-01

Linear calibration is usually performed using eight to ten calibration concentration levels in regulated LC-MS bioanalysis because a minimum of six are specified in regulatory guidelines. However, we have previously reported that two-concentration linear calibration is as reliable as or even better than using multiple concentrations. The purpose of this research is to compare two-concentration with multiple-concentration linear calibration through retrospective data analysis of multiple bioanalytical projects that were conducted in an independent regulated bioanalytical laboratory. A total of 12 bioanalytical projects were randomly selected: two validations and two studies for each of the three most commonly used types of sample extraction methods (protein precipitation, liquid-liquid extraction, solid-phase extraction). When the existing data were retrospectively linearly regressed using only the lowest and the highest concentration levels, no extra batch failure/QC rejection was observed and the differences in accuracy and precision between the original multi-concentration regression and the new two-concentration linear regression are negligible. Specifically, the differences in overall mean apparent bias (square root of mean individual bias squares) are within the ranges of -0.3% to 0.7% and 0.1-0.7% for the validations and studies, respectively. The differences in mean QC concentrations are within the ranges of -0.6% to 1.8% and -0.8% to 2.5% for the validations and studies, respectively. The differences in %CV are within the ranges of -0.7% to 0.9% and -0.3% to 0.6% for the validations and studies, respectively. The average differences in study sample concentrations are within the range of -0.8% to 2.3%. With two-concentration linear regression, an average of 13% of time and cost could have been saved for each batch together with 53% of saving in the lead-in for each project (the preparation of working standard solutions, spiking, and aliquoting). Furthermore, examples are given as how to evaluate the linearity over the entire concentration range when only two concentration levels are used for linear regression. To conclude, two-concentration linear regression is accurate and robust enough for routine use in regulated LC-MS bioanalysis and it significantly saves time and cost as well. Copyright © 2013 Elsevier B.V. All rights reserved.

Beliefs about Child Support Modification Following Remarriage and Subsequent Childbirth

ERIC Educational Resources Information Center

Hans, Jason D.

2009-01-01

Framed by equity theory, fairness beliefs regarding child support modification to account for the financial impact of remarriage and subsequent childbirth were assessed. Based on a random sample of 407 Kentucky residents using a multiple segment factorial vignette approach, modification was supported by 57% of respondents following remarriage, but…

Flood characteristics of Alaskan streams

USGS Publications Warehouse

Lamke, R.D.

1979-01-01

Peak discharge data for Alaskan streams are summarized and analyzed. Multiple-regression equations relating peak discharge magnitude and frequency to climatic and physical characteristics of 260 gaged basins were determined in order to estimate average recurrence interval of floods at ungaged sites. These equations are for 1.25-, 2-, 5-, 10-, 25-, and 50-year average recurrence intervals. In this report, Alaska was divided into two regions, one having a maritime climate with fall and winter rains and floods, the other having spring and summer floods of a variety or combinations of causes. Average standard errors of the six multiple-regression equations for these two regions were 48 and 74 percent, respectively. Maximum recorded floods at more than 400 sites throughout Alaska are tabulated. Maps showing lines of equal intensity of the principal climatic variables found to be significant (mean annual precipitation and mean minimum January temperature), and location of the 260 sites used in the multiple-regression analyses are included. Little flood data have been collected in western and arctic Alaska, and the predictive equations are therefore less reliable for those areas. (Woodard-USGS)

Estimation of nutrients and organic matter in Korean swine slurry using multiple regression analysis of physical and chemical properties.

PubMed

Suresh, Arumuganainar; Choi, Hong Lim

2011-10-01

Swine waste land application has increased due to organic fertilization, but excess application in an arable system can cause environmental risk. Therefore, in situ characterizations of such resources are important prior to application. To explore this, 41 swine slurry samples were collected from Korea, and wide differences were observed in the physico-biochemical properties. However, significant (P<0.001) multiple property correlations (R²) were obtained between nutrients with specific gravity (SG), electrical conductivity (EC), total solids (TS) and pH. The different combinations of hydrometer, EC meter, drying oven and pH meter were found useful to estimate Mn, Fe, Ca, K, Al, Na, N and 5-day biochemical oxygen demands (BOD₅) at improved R² values of 0.83, 0.82, 0.77, 0.75, 0.67, 0.47, 0.88 and 0.70, respectively. The results from this study suggest that multiple property regressions can facilitate the prediction of micronutrients and organic matter much better than a single property regression for livestock waste. Copyright © 2011 Elsevier Ltd. All rights reserved.

Theory of mind and executive function: working-memory capacity and inhibitory control as predictors of false-belief task performance.

PubMed

Mutter, Brigitte; Alcorn, Mark B; Welsh, Marilyn

2006-06-01

This study of the relationship between theory of mind and executive function examined whether on the false-belief task age differences between 3 and 5 ears of age are related to development of working-memory capacity and inhibitory processes. 72 children completed tasks measuring false belief, working memory, and inhibition. Significant age effects were observed for false-belief and working-memory performance, as well as for the false-alarm and perseveration measures of inhibition. A simultaneous multiple linear regression specified the contribution of age, inhibition, and working memory to the prediction of false-belief performance. This model was significant, explaining a total of 36% of the variance. To examine the independent contributions of the working-memory and inhibition variables, after controlling for age, two hierarchical multiple linear regressions were conducted. These multiple regression analyses indicate that working memory and inhibition make small, overlapping contributions to false-belief performance after accounting for age, but that working memory, as measured in this study, is a somewhat better predictor of false-belief understanding than is inhibition.

Mapping diffuse photosynthetically active radiation from satellite data in Thailand

NASA Astrophysics Data System (ADS)

Choosri, P.; Janjai, S.; Nunez, M.; Buntoung, S.; Charuchittipan, D.

2017-12-01

In this paper, calculation of monthly average hourly diffuse photosynthetically active radiation (PAR) using satellite data is proposed. Diffuse PAR was analyzed at four stations in Thailand. A radiative transfer model was used for calculating the diffuse PAR for cloudless sky conditions. Differences between the diffuse PAR under all sky conditions obtained from the ground-based measurements and those from the model are representative of cloud effects. Two models are developed, one describing diffuse PAR only as a function of solar zenith angle, and the second one as a multiple linear regression with solar zenith angle and satellite reflectivity acting linearly and aerosol optical depth acting in logarithmic functions. When tested with an independent data set, the multiple regression model performed best with a higher coefficient of variance R2 (0.78 vs. 0.70), lower root mean square difference (RMSD) (12.92% vs. 13.05%) and the same mean bias difference (MBD) of -2.20%. Results from the multiple regression model are used to map diffuse PAR throughout the country as monthly averages of hourly data.

Clifford support vector machines for classification, regression, and recurrence.

PubMed

Bayro-Corrochano, Eduardo Jose; Arana-Daniel, Nancy

2010-11-01

This paper introduces the Clifford support vector machines (CSVM) as a generalization of the real and complex-valued support vector machines using the Clifford geometric algebra. In this framework, we handle the design of kernels involving the Clifford or geometric product. In this approach, one redefines the optimization variables as multivectors. This allows us to have a multivector as output. Therefore, we can represent multiple classes according to the dimension of the geometric algebra in which we work. We show that one can apply CSVM for classification and regression and also to build a recurrent CSVM. The CSVM is an attractive approach for the multiple input multiple output processing of high-dimensional geometric entities. We carried out comparisons between CSVM and the current approaches to solve multiclass classification and regression. We also study the performance of the recurrent CSVM with experiments involving time series. The authors believe that this paper can be of great use for researchers and practitioners interested in multiclass hypercomplex computing, particularly for applications in complex and quaternion signal and image processing, satellite control, neurocomputation, pattern recognition, computer vision, augmented virtual reality, robotics, and humanoids.

A general equation to obtain multiple cut-off scores on a test from multinomial logistic regression.

PubMed

Bersabé, Rosa; Rivas, Teresa

2010-05-01

The authors derive a general equation to compute multiple cut-offs on a total test score in order to classify individuals into more than two ordinal categories. The equation is derived from the multinomial logistic regression (MLR) model, which is an extension of the binary logistic regression (BLR) model to accommodate polytomous outcome variables. From this analytical procedure, cut-off scores are established at the test score (the predictor variable) at which an individual is as likely to be in category j as in category j+1 of an ordinal outcome variable. The application of the complete procedure is illustrated by an example with data from an actual study on eating disorders. In this example, two cut-off scores on the Eating Attitudes Test (EAT-26) scores are obtained in order to classify individuals into three ordinal categories: asymptomatic, symptomatic and eating disorder. Diagnoses were made from the responses to a self-report (Q-EDD) that operationalises DSM-IV criteria for eating disorders. Alternatives to the MLR model to set multiple cut-off scores are discussed.

Application of Partial Least Square (PLS) Regression to Determine Landscape-Scale Aquatic Resources Vulnerability in the Ozark Mountains

EPA Science Inventory

Partial least squares (PLS) analysis offers a number of advantages over the more traditionally used regression analyses applied in landscape ecology, particularly for determining the associations among multiple constituents of surface water and landscape configuration. Common dat...

Cluster Analysis of Campylobacter jejuni Genotypes Isolated from Small and Medium-Sized Mammalian Wildlife and Bovine Livestock from Ontario Farms.

PubMed

Viswanathan, M; Pearl, D L; Taboada, E N; Parmley, E J; Mutschall, S K; Jardine, C M

2017-05-01

Using data collected from a cross-sectional study of 25 farms (eight beef, eight swine and nine dairy) in 2010, we assessed clustering of molecular subtypes of C. jejuni based on a Campylobacter-specific 40 gene comparative genomic fingerprinting assay (CGF40) subtypes, using unweighted pair-group method with arithmetic mean (UPGMA) analysis, and multiple correspondence analysis. Exact logistic regression was used to determine which genes differentiate wildlife and livestock subtypes in our study population. A total of 33 bovine livestock (17 beef and 16 dairy), 26 wildlife (20 raccoon (Procyon lotor), five skunk (Mephitis mephitis) and one mouse (Peromyscus spp.) C. jejuni isolates were subtyped using CGF40. Dendrogram analysis, based on UPGMA, showed distinct branches separating bovine livestock and mammalian wildlife isolates. Furthermore, two-dimensional multiple correspondence analysis was highly concordant with dendrogram analysis showing clear differentiation between livestock and wildlife CGF40 subtypes. Based on multilevel logistic regression models with a random intercept for farm of origin, we found that isolates in general, and raccoons more specifically, were significantly more likely to be part of the wildlife branch. Exact logistic regression conducted gene by gene revealed 15 genes that were predictive of whether an isolate was of wildlife or bovine livestock isolate origin. Both multiple correspondence analysis and exact logistic regression revealed that in most cases, the presence of a particular gene (13 of 15) was associated with an isolate being of livestock rather than wildlife origin. In conclusion, the evidence gained from dendrogram analysis, multiple correspondence analysis and exact logistic regression indicates that mammalian wildlife carry CGF40 subtypes of C. jejuni distinct from those carried by bovine livestock. Future studies focused on source attribution of C. jejuni in human infections will help determine whether wildlife transmit Campylobacter jejuni directly to humans. © 2016 Blackwell Verlag GmbH.

Estimation of lung tumor position from multiple anatomical features on 4D-CT using multiple regression analysis.

PubMed

Ono, Tomohiro; Nakamura, Mitsuhiro; Hirose, Yoshinori; Kitsuda, Kenji; Ono, Yuka; Ishigaki, Takashi; Hiraoka, Masahiro

2017-09-01

To estimate the lung tumor position from multiple anatomical features on four-dimensional computed tomography (4D-CT) data sets using single regression analysis (SRA) and multiple regression analysis (MRA) approach and evaluate an impact of the approach on internal target volume (ITV) for stereotactic body radiotherapy (SBRT) of the lung. Eleven consecutive lung cancer patients (12 cases) underwent 4D-CT scanning. The three-dimensional (3D) lung tumor motion exceeded 5 mm. The 3D tumor position and anatomical features, including lung volume, diaphragm, abdominal wall, and chest wall positions, were measured on 4D-CT images. The tumor position was estimated by SRA using each anatomical feature and MRA using all anatomical features. The difference between the actual and estimated tumor positions was defined as the root-mean-square error (RMSE). A standard partial regression coefficient for the MRA was evaluated. The 3D lung tumor position showed a high correlation with the lung volume (R = 0.92 ± 0.10). Additionally, ITVs derived from SRA and MRA approaches were compared with ITV derived from contouring gross tumor volumes on all 10 phases of the 4D-CT (conventional ITV). The RMSE of the SRA was within 3.7 mm in all directions. Also, the RMSE of the MRA was within 1.6 mm in all directions. The standard partial regression coefficient for the lung volume was the largest and had the most influence on the estimated tumor position. Compared with conventional ITV, average percentage decrease of ITV were 31.9% and 38.3% using SRA and MRA approaches, respectively. The estimation accuracy of lung tumor position was improved by the MRA approach, which provided smaller ITV than conventional ITV. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

Occurrence of Multiple Subsequent Neoplasms in Long-Term Survivors of Childhood Cancer: A Report From the Childhood Cancer Survivor Study

PubMed Central

Armstrong, Gregory T.; Liu, Wei; Leisenring, Wendy; Yasui, Yutaka; Hammond, Sue; Bhatia, Smita; Neglia, Joseph P.; Stovall, Marilyn; Srivastava, Deokumar; Robison, Leslie L.

2011-01-01

Purpose Childhood cancer survivors experience an increased incidence of subsequent neoplasms (SNs). Those surviving the first SN (SN1) remain at risk to develop multiple SNs. Because SNs are a common cause of late morbidity and mortality, characterization of rates of multiple SNs is needed. Patients and Methods In a total of 14,358 5-year survivors of childhood cancer diagnosed between 1970 and 1986, analyses were carried out among 1,382 survivors with an SN1. Cumulative incidence of second subsequent neoplasm (SN2), either malignant or benign, was calculated. Results A total of 1,382 survivors (9.6%) developed SN1, of whom 386 (27.9%) developed SN2. Of those with SN2, 153 (39.6%) developed more than two SNs. Cumulative incidence of SN2 was 46.9% (95% CI, 41.6% to 52.2%) at 20 years after SN1. The cumulative incidence of SN2 among radiation-exposed survivors was 41.3% (95% CI, 37.2% to 45.4%) at 15 years compared with 25.7% (95% CI, 16.5% to 34.9%) for those not treated with radiation. Radiation-exposed survivors who developed an SN1 of nonmelanoma skin cancer (NMSC) had a cumulative incidence of subsequent malignant neoplasm (SMN; ie, malignancies excluding NMSC) of 20.3% (95% CI, 13.0% to 27.6%) at 15 years compared with only 10.7% (95% CI, 7.2% to 14.2%) for those who were exposed to radiation and whose SN1 was an invasive SMN (excluding NMSC). Conclusion Multiple SNs are common among aging survivors of childhood cancer. SN1 of NMSC identifies a population at high risk for invasive SMN. Survivors not exposed to radiation who develop multiple SNs represent a population of interest for studying genetic susceptibility to neoplasia. PMID:21709189

Comparison of Different Shrinkage Formulas in Estimating Population Multiple Correlation Coefficients.

ERIC Educational Resources Information Center

Carter, David S.

1979-01-01

There are a variety of formulas for reducing the positive bias which occurs in estimating R squared in multiple regression or correlation equations. Five different formulas are evaluated in a Monte Carlo study, and recommendations are made. (JKS)

Variety in Parental Use of "Want" Relates to Subsequent Growth in Children's Theory of Mind

ERIC Educational Resources Information Center

Ruffman, Ted; Puri, Aastha; Galloway, Olivia; Su, Japher; Taumoepeau, Mele

2018-01-01

In 2 cross-lagged, longitudinal studies we contrasted parental talk about want in a single context versus multiple contexts. Study 1 examined thirty-two 2 year olds, with mothers describing pictures to children. Mothers could use want in zero, one, or multiple contexts. Children whose mothers used want in multiple contexts experienced a…

Estimating Optimal Transformations for Multiple Regression and Correlation.

DTIC Science & Technology

1982-07-01

S w.EECTli1Z"", , J OCT 0 11982 u! !for Public its... .. . ESTIMATING OPTIMAL TRANSFORMATIONS FOR MULTIPLE REGRESSION AND CORRELATION by Leo...in the plot lb of *(yk) versus 1 < k < 200. Figure lc is a plot of $*(xk) versus xk. These plots clearly suggest the transformati " s 6(y) = log(y) and...direct .814 .022 ACE .808 .031 -13- Figure la6L ’ ’ I . . . S " ’ ’ . . I ’ 6- - - .4...... Co o • . o ’ 0 0.2 0.4 0.5 0.8 1 Fi gure lb2 2 2 // II / / -/

Bark analysis as a guide to cassava nutrition in Sierra Leone

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

Godfrey-Sam-Aggrey, W.; Garber, M.J.

1979-01-01

Cassava main stem barks from two experiments in which similar fertilizers were applied directly in a 2/sup 5/ confounded factorial design were analyzed and the bark nutrients used as a guide to cassava nutrition. The application of multiple regression analysis to the respective root yields and bark nutrient concentrations enable nutrient levels and optimum adjusted root yields to be derived. Differences in bark nutrient concentrations reflected soil fertility levels. Bark analysis and the application of multiple regression analysis to root yields and bark nutrients appear to be useful tools for predicting fertilizer recommendations for cassava production.

Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

NASA Astrophysics Data System (ADS)

Shastri, Niket; Pathak, Kamlesh

2018-05-01

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

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.

Scanning sequences after Gibbs sampling to find multiple occurrences of functional elements

PubMed Central

Tharakaraman, Kannan; Mariño-Ramírez, Leonardo; Sheetlin, Sergey L; Landsman, David; Spouge, John L

2006-01-01

Background Many DNA regulatory elements occur as multiple instances within a target promoter. Gibbs sampling programs for finding DNA regulatory elements de novo can be prohibitively slow in locating all instances of such an element in a sequence set. Results We describe an improvement to the A-GLAM computer program, which predicts regulatory elements within DNA sequences with Gibbs sampling. The improvement adds an optional "scanning step" after Gibbs sampling. Gibbs sampling produces a position specific scoring matrix (PSSM). The new scanning step resembles an iterative PSI-BLAST search based on the PSSM. First, it assigns an "individual score" to each subsequence of appropriate length within the input sequences using the initial PSSM. Second, it computes an E-value from each individual score, to assess the agreement between the corresponding subsequence and the PSSM. Third, it permits subsequences with E-values falling below a threshold to contribute to the underlying PSSM, which is then updated using the Bayesian calculus. A-GLAM iterates its scanning step to convergence, at which point no new subsequences contribute to the PSSM. After convergence, A-GLAM reports predicted regulatory elements within each sequence in order of increasing E-values, so users have a statistical evaluation of the predicted elements in a convenient presentation. Thus, although the Gibbs sampling step in A-GLAM finds at most one regulatory element per input sequence, the scanning step can now rapidly locate further instances of the element in each sequence. Conclusion Datasets from experiments determining the binding sites of transcription factors were used to evaluate the improvement to A-GLAM. Typically, the datasets included several sequences containing multiple instances of a regulatory motif. The improvements to A-GLAM permitted it to predict the multiple instances. PMID:16961919

Regression Analysis with Dummy Variables: Use and Interpretation.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Oliver, J. Dale

1986-01-01

Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

REGRESSION MODELS THAT RELATE STREAMS TO WATERSHEDS: COPING WITH NUMEROUS, COLLINEAR PEDICTORS

EPA Science Inventory

GIS efforts can produce a very large number of watershed variables (climate, land use/land cover and topography, all defined for multiple areas of influence) that could serve as candidate predictors in a regression model of reach-scale stream features. Invariably, many of these ...

Identifying the Factors That Influence Change in SEBD Using Logistic Regression Analysis

ERIC Educational Resources Information Center

Camilleri, Liberato; Cefai, Carmel

2013-01-01

Multiple linear regression and ANOVA models are widely used in applications since they provide effective statistical tools for assessing the relationship between a continuous dependent variable and several predictors. However these models rely heavily on linearity and normality assumptions and they do not accommodate categorical dependent…

A Constrained Linear Estimator for Multiple Regression

ERIC Educational Resources Information Center

Davis-Stober, Clintin P.; Dana, Jason; Budescu, David V.

2010-01-01

"Improper linear models" (see Dawes, Am. Psychol. 34:571-582, "1979"), such as equal weighting, have garnered interest as alternatives to standard regression models. We analyze the general circumstances under which these models perform well by recasting a class of "improper" linear models as "proper" statistical models with a single predictor. We…

Regression of a vaginal leiomyoma after ovariohysterectomy in a dog: a case report.

PubMed

Sathya, Suresh; Linn, Kathleen

2014-01-01

An 11 yr old female mixed-breed Siberian husky was presented with a history of sanguineous vaginal discharge, swelling of the perineal area, decreased appetite, and lethargy. A single, large vaginal leiomyoma and multiple mammary tumors were diagnosed. Mastectomy and ovariohysterectomy were performed. The vaginal leiomyoma regressed completely after ovariohysterectomy. This is the first reported case of spontaneous regression of a vaginal leiomyoma after ovariohysterectomy in a dog.

Craving and subsequent opioid use among opioid dependent patients who initiate treatment with buprenorphine

PubMed Central

Tsui, Judith I.; Anderson, Bradley J.; Strong, David R.; Stein, Michael D.

2016-01-01

Background Few studies have directly assessed associations between craving and subsequent opioid use among treated patients. Our objective was to prospectively evaluate the relative utility of two craving questionnaires to predict opioid use among opioid dependent patients in treatment. Method Opioid dependent patients (n=147) initiating buprenorphine treatment were assessed for three months. Craving was measured using: 1) the Desires for Drug Questionnaire (DDQ) and 2) the Penn Alcohol-Craving Scale adapted for opioid craving (PCS) for this study. Multi-level logistic regression models estimated the effects of craving on the likelihood of opioid use after adjusting for gender, age, ethnicity, education, opioid of choice, frequency of use, pain and depression. In these analyses craving assessed at time t was entered as a time-varying predictor of opioid use at time t+1. Results In adjusted regression models, a 1-point increase in PCS scores (on a 7-point scale) was associated with a significant increase in the odds of opioid use at the subsequent assessment (OR = 1.27, 95% CI 1.08; 1.49, p < .01). The odds of opioid use at the subsequent follow-up assessment increased significantly as DDQ desire and intention scores increased (OR = 1.25, 95%CI 1.03; 1.51, p< .05), but was not associated significantly with DDQ negative reinforcement (OR = 1.01, 95%CI 0.88; 1.17, p > .05) or DDQ control (OR = 0.97, 95%CI 0.85; 1.11, p > .05) scores. Conclusion Self-reported craving for opioids was associated with subsequent lapse to opioid use among a cohort of patients treated with buprenorphine. PMID:24521036

Selected contribution: redistribution of pulmonary perfusion during weightlessness and increased gravity

NASA Technical Reports Server (NTRS)

Glenny, R. W.; Lamm, W. J.; Bernard, S. L.; An, D.; Chornuk, M.; Pool, S. L.; Wagner, W. W. Jr; Hlastala, M. P.; Robertson, H. T.

2000-01-01

To compare the relative contributions of gravity and vascular structure to the distribution of pulmonary blood flow, we flew with pigs on the National Aeronautics and Space Administration KC-135 aircraft. A series of parabolas created alternating weightlessness and 1.8-G conditions. Fluorescent microspheres of varying colors were injected into the pulmonary circulation to mark regional blood flow during different postural and gravitational conditions. The lungs were subsequently removed, air dried, and sectioned into approximately 2 cm(3) pieces. Flow to each piece was determined for the different conditions. Perfusion heterogeneity did not change significantly during weightlessness compared with normal and increased gravitational forces. Regional blood flow to each lung piece changed little despite alterations in posture and gravitational forces. With the use of multiple stepwise linear regression, the contributions of gravity and vascular structure to regional perfusion were separated. We conclude that both gravity and the geometry of the pulmonary vascular tree influence regional pulmonary blood flow. However, the structure of the vascular tree is the primary determinant of regional perfusion in these animals.

Interpersonal polyvictimization and mental health in males.

PubMed

Burns, Carol Rhonda; Lagdon, Susan; Boyda, David; Armour, Cherie

2016-05-01

A consistent conclusion within the extant literature is that victimization and in particular polyvictimization leads to adverse mental health outcomes. A large body of literature exists as it pertains to the association between victimisation and mental health in studies utilising samples of childhood victims, female only victims, and samples of male and female victims; less research exists as it relates to males victims of interpersonal violence. The aim of the current study was therefore to identify profiles of interpersonal victimizations in an exclusively male sample and to assess their differential impact on a number of adverse mental health outcomes. Using data from 14,477 adult males from Wave 2 of the NESARC, we identified interpersonal victimization profiles via Latent Class Analysis. Multinomial Logistic Regression was subsequently utilized to establish risk across mental health disorders. A 4-class solution was optimal. Victimisation profiles showed elevated odds ratios for the presence of mental health disorders; suggesting that multiple life-course victimisation typologies exists, and that victimization is strongly associated with psychopathology. Several additional notable findings are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Characterization and classification of Japanese consumer perceptions for beef tenderness using descriptive texture characteristics assessed by a trained sensory panel.

PubMed

Sasaki, Keisuke; Motoyama, Michiyo; Narita, Takumi; Hagi, Tatsuro; Ojima, Koichi; Oe, Mika; Nakajima, Ikuyo; Kitsunai, Katsuhiro; Saito, Yosuke; Hatori, Hikari; Muroya, Susumu; Nomura, Masaru; Miyaguchi, Yuji; Chikuni, Koichi

2014-02-01

Meat tenderness is an important characteristic in terms of consumer preference and satisfaction. However, each consumer may have his/her own criteria to judge meat tenderness, because consumers are neither selected nor trained like an expert sensory panel. This study aimed to characterize consumer tenderness using descriptive texture profiles such as chewiness and hardness assessed by a trained panel. Longissimus muscles cooked at four different end-point temperatures were subjected to a trained sensory panel (n=18) and consumer (n=107) tenderness tests. Multiple regression analysis showed that consumer tenderness was characterized as 'low-chewiness and low hardness texture.' Subsequently, consumers were divided into two groups by cluster analysis according to tenderness perceptions in each participant, and the two groups were characterized as 'tenderness is mainly low-chewiness' and 'tenderness is mainly low-hardness' for tenderness perception, respectively. These results demonstrate objective characteristics and variability of consumer meat tenderness, and provide new information regarding the evaluation and management of meat tenderness for meat manufacturers. © 2013.

Reference equation for prediction of a total distance during six-minute walk test using Indonesian anthropometrics.

PubMed

Nusdwinuringtyas, Nury; Widjajalaksmi; Yunus, Faisal; Alwi, Idrus

2014-04-01

to develop a reference equation for prediction of the total distance walk using Indonesian anthropometrics of sedentary healthy subjects. Subsequently, the prediction obtained was compared to those calculated by the Caucasian-based Enright prediction equation. the cross-sectional study was conducted among 123 healthy Indonesian adults with sedentary life style (58 male and 65 female subjects in an age range between 18 and 50 years). Heart rate was recorded using Polar with expectation in the sub-maximal zone (120-170 beats per minute). The subjects performed two six-minute walk tests, the first one on a 15-meter track according to the protocol developed by the investigator. The second walk was carried out on Biodex®gait trainer as gold standard. an average total distance of 547±54.24 m was found, not significantly different from the gold standard of 544.72±54.11 m (p>0.05). Multiple regression analysis was performed to develop the new equation. the reference equation for prediction of the total distance using Indonesian anthropometrics is more applicable in Indonesia.

Optimization of pressurized liquid extraction of inositols from pine nuts (Pinus pinea L.).

PubMed

Ruiz-Aceituno, L; Rodríguez-Sánchez, S; Sanz, J; Sanz, M L; Ramos, L

2014-06-15

Pressurized liquid extraction (PLE) has been used for the first time to extract bioactive inositols from pine nuts. The influence of extraction time, temperature and cycles of extraction in the yield and composition of the extract was studied. A quadratic lineal model using multiple linear regression in the stepwise mode was used to evaluate possible trends in the process. Under optimised PLE conditions (50°C, 18 min, 3 cycles of 1.5 mL water each one) at 10 MPa, a noticeable reduction in extraction time and solvent volume, compared with solid-liquid extraction (SLE; room temperature, 2h, 2 cycles of 5 mL water each one) was achieved; 5.7 mg/g inositols were extracted by PLE, whereas yields of only 3.7 mg/g were obtained by SLE. Subsequent incubation of PLE extracts with Saccharomyces cerevisiae (37°C, 5h) allowed the removal of other co-extracted low molecular weight carbohydrates which may interfere in the bioactivity of inositols. Copyright © 2014 Elsevier Ltd. All rights reserved.

Molecular Predictors of 3D Morphogenesis by Breast Cancer Cell Lines in 3D Culture

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

Han, Ju; Chang, Hang; Giricz, Orsi

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associationsmore » with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPAR? has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPAR? has been validated through two supporting biological assays.« less

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

Mitchell, Hugh D.; Eisfeld, Amie J.; Sims, Amy

Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel “crowd-based” approach to identify and combine ranking metricsmore » that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse ‘omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.« less

A new look at the Lake Superior biomass size spectrum

USGS Publications Warehouse

Yurista, Peder M.; Yule, Daniel L.; Balge, Matt; VanAlstine, Jon D.; Thompson, Jo A.; Gamble, Allison E.; Hrabik, Thomas R.; Kelly, John R.; Stockwell, Jason D.; Vinson, Mark

2014-01-01

We synthesized data from multiple sampling programs and years to describe the Lake Superior pelagic biomass size structure. Data consisted of Coulter counts for phytoplankton, optical plankton counts for zooplankton, and acoustic surveys for pelagic prey fish. The size spectrum was stable across two time periods separated by 5 years. The primary scaling or overall slope of the normalized biomass size spectra for the combined years was −1.113, consistent with a previous estimate for Lake Superior (−1.10). Periodic dome structures within the overall biomass size structure were fit to polynomial regressions based on the observed sub-domes within the classical taxonomic positions (algae, zooplankton, and fish). This interpretation of periodic dome delineation was aligned more closely with predator–prey size relationships that exist within the zooplankton (herbivorous, predacious) and fish (planktivorous, piscivorous) taxonomic positions. Domes were spaced approximately every 3.78 log10 units along the axis and with a decreasing peak magnitude of −4.1 log10 units. The relative position of the algal and herbivorous zooplankton domes predicted well the subsequent biomass domes for larger predatory zooplankton and planktivorous prey fish.

Social Support in Newly Diagnosed People living With HIV: Expectations and Satisfaction Along Time, Predictors, and Mental Health Correlates.

PubMed

Garrido-Hernansaiz, Helena; Alonso-Tapia, Jesús

Social support usually decreases following HIV diagnosis, and decreased support is related to worsening mental health. We investigated the evolution of social support after HIV diagnosis and its relationship to anxiety, depression, and resilience, and sought to develop a social support prediction model. There were 119 newly diagnosed Spanish speakers who participated in this longitudinal study, completing measures of social support, internalized stigma, disclosure concerns, degree of disclosure, coping, anxiety, depression, and resilience. Bivariate associations and multiple regression analyses were performed. Results showed that the highest levels of support arose from friends, health care providers, and partners, and that social support decreased following diagnosis. Subsequent social support was negatively predicted by avoidance coping and positively by approach coping, steady partnership, and disclosure. It was significantly associated with decreased anxiety and depression and higher resilience. Interventions should seek to promote mental health in people living with HIV by increasing social support. Copyright © 2017 Association of Nurses in AIDS Care. Published by Elsevier Inc. All rights reserved.

Towards decadal soil salinity mapping using Landsat time series data

NASA Astrophysics Data System (ADS)

Fan, Xingwang; Weng, Yongling; Tao, Jinmei

2016-10-01

Salinization is one of the major soil problems around the world. However, decadal variation in soil salinization has not yet been extensively reported. This study exploited thirty years (1985-2015) of Landsat sensor data, including Landsat-4/5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operational Land Imager), for monitoring soil salinity of the Yellow River Delta, China. The data were initially corrected for atmospheric effects, and then matched the spectral bands of EO-1 (Earth Observing One) ALI (Advanced Land Imager). Subsequently, soil salinity maps were derived with a previously developed PLSR (Partial Least Square Regression) model. On intra-annual scale, the retrievals showed that soil salinity increased in February, stabilized in March, and decreased in April. On inter-annual scale, soil salinity decreased within 1985-2000 (-0.74 g kg-1/10a, p < 0.001), and increased within 2000-2015 (0.79 g kg-1/10a, p < 0.001). Our study presents a new perspective for use of multiple Landsat data in soil salinity retrieval, and further the understanding of soil salinization development over the Yellow River Delta.

Interpersonal difficulties as a risk factor for athletes' eating psychopathology.

PubMed

Shanmugam, V; Jowett, S; Meyer, C

2014-04-01

The present study sought to determine the predictive role of interpersonal difficulties on eating psychopathology among competitive British athletes (ranging from university to international competition level). A total of 122 athletes (36 males and 86 females) with a mean age of 21.22 years (SD = 4.02), completed a multisection questionnaire that measured eating psychopathology, attachment styles, and quality of relationships with parents, coaches and teammate over a 6-month period. Partial correlations revealed that when controlling for baseline eating psychopathology, only the quality of the relationship with coach and closest teammate were related to athletes' eating psychopathology 6 months later. Subsequent hierarchical multiple regression analyses demonstrated that athletes' eating psychopathology was only predicted by perceived levels of interpersonal conflict with the coach. The current findings provide evidence to suggest that conflict within the coach-athlete relationship is a potential risk factor for eating disorders among athletes and thus it would seem appropriate to raise awareness for its potentially toxic role in athletes' eating psychopathology. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Organisational stressors, coping, and outcomes in competitive sport.

PubMed

Arnold, Rachel; Fletcher, David; Daniels, Kevin

2017-04-01

Organisational stressors are associated with positive and negative outcomes in extant literature; however, little is known about which demands predict which outcomes. Extant theory and literature also suggests that coping style may influence an individual's resilience or vulnerability to stressors and, subsequently, their psychological responses and outcomes. The purpose of this study was, therefore, to examine the main effects of organisational stressors and coping styles on various outcomes (e.g., positive and negative affect, performance satisfaction). Sport performers (n = 414) completed measures of organisational stressors, coping styles, positive and negative affect, and performance satisfaction. Multiple regression analyses revealed positive relationships of both goals and development stressors (duration and intensity) and team and culture stressors (frequency and intensity) on negative affect. Furthermore, problem-focused coping was positively related to positive affect, and emotion-focused coping was positively related to negative affect. This study furthers theoretical knowledge regarding the associations that both organisational stressors (and their dimensions) and coping styles can have with various outcomes, and practical understanding regarding the optimal design of stress management interventions.

Stress as a mediator between work-family conflict and psychological health among the nursing staff: Moderating role of emotional intelligence.

PubMed

Sharma, Jyoti; Dhar, Rajib Lochan; Tyagi, Akansha

2016-05-01

The study examined the extent to which work-family conflicts cause stress among nursing staff and its subsequent impact on their psychological health. It also examined if the emotional intelligence level of the nursing staff acted as a moderator between their level of stress and psychological health. A survey was carried out on 693 nursing staff associated with 33 healthcare institutions in Uttarakhand, India. A hierarchical multiple regression analysis was carried out to understand the relationships shared by independent (work-family conflicts) and dependent (psychological health) constructs with the mediator (stress) as well as the moderator (emotional intelligence). The results revealed that stress acted as a mediator between work-family conflict of the nursing staff and their psychological health. However, their emotional intelligence level acted as a moderator between their stress level and psychological health. To conclude, the crucial roles of emotional intelligence in controlling the impact of stress on psychological health along with the practical as well as theoretical implications are also discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Resident performance on the in-training and board examinations in obstetrics and gynecology: implications for the ACGME Outcome Project.

PubMed

Withiam-Leitch, Matthew; Olawaiye, Alexander

2008-01-01

The Accreditation Council on Graduate Medical Education (ACGME) Outcomes Project has endorsed the in-training examination (ITE) as an example of a multiple-choice question examination that is a valid measure of a resident's attainment of medical knowledge. An outcome measure for performance on the ITE would be the subsequent performance on the board certification examination. However, there are few reports that attempt to correlate a resident's performance on the ITE to subsequent performance on the board certification examination. The Council on Resident Education in Obstetrics and Gynecology (CREOG) has administered the ITE annually since 1970. This study tested the hypothesis that the CREOG-ITE score is a valid assessment tool to predict performance on the American Board of Obstetrics and Gynecology (ABOG) written examination. CREOG-ITE and ABOG written board examination results were collected for 69 resident graduates between the years 1998 and 2005. Logistic regression and receiver operating characteristic analyses were used to estimate the relationship between a resident's score on the CREOG-ITE and subsequent performance on the ABOG written examination. Fifty-seven resident graduates passed (82.6%) and 12 graduates failed (17.4%) the ABOG written examination. The correlation between the CREOG-ITE overall score and performance on the ABOG examination was weak (correlation coefficient =.38, p =.001). Receiver operating characteristic analysis for the CREOG-ITE overall scores and the probability of passing or failing the ABOG examination revealed moderate accuracy (area under the curve = 0.77, 95% CI = 0.62-0.92) with a CREOG-ITE score of 187.5 yielding the best trade-off between specificity (0.79) and sensitivity (0.75). At the cutoff value of 187.5 there was a weak positive predictive value of 43% (i.e., 43% of residents with a score less than 187.5 will fail the ABOG exam) and a strong negative predictive value of 94% (i.e., 94% of the residents with a score above 187.5 will pass the ABOG exam). Logistic regression analysis also revealed a statistically significant relationship between the CREOG-ITE overall score and performance on the ABOG written examination (p = .003). Similar to other specialties, resident performance on the CREOG-ITE is a weak assessment tool to predict the probability of a resident failing the ABOG written examination. Our study highlights the need, in the spirit of the ACGME Outcome Project, for residency and board specialty organizations to coordinate efforts to develop more reliable and correlative measures of a resident's medical knowledge and ability to pass the boards.

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

Zawisza, I; Yan, H; Yin, F

Purpose: To assure that tumor motion is within the radiation field during high-dose and high-precision radiosurgery, real-time imaging and surrogate monitoring are employed. These methods are useful in providing real-time tumor/surrogate motion but no future information is available. In order to anticipate future tumor/surrogate motion and track target location precisely, an algorithm is developed and investigated for estimating surrogate motion multiple-steps ahead. Methods: The study utilized a one-dimensional surrogate motion signal divided into three components: (a) training component containing the primary data including the first frame to the beginning of the input subsequence; (b) input subsequence component of the surrogatemore » signal used as input to the prediction algorithm: (c) output subsequence component is the remaining signal used as the known output of the prediction algorithm for validation. The prediction algorithm consists of three major steps: (1) extracting subsequences from training component which best-match the input subsequence according to given criterion; (2) calculating weighting factors from these best-matched subsequence; (3) collecting the proceeding parts of the subsequences and combining them together with assigned weighting factors to form output. The prediction algorithm was examined for several patients, and its performance is assessed based on the correlation between prediction and known output. Results: Respiratory motion data was collected for 20 patients using the RPM system. The output subsequence is the last 50 samples (∼2 seconds) of a surrogate signal, and the input subsequence was 100 (∼3 seconds) frames prior to the output subsequence. Based on the analysis of correlation coefficient between predicted and known output subsequence, the average correlation is 0.9644±0.0394 and 0.9789±0.0239 for equal-weighting and relative-weighting strategies, respectively. Conclusion: Preliminary results indicate that the prediction algorithm is effective in estimating surrogate motion multiple-steps in advance. Relative-weighting method shows better prediction accuracy than equal-weighting method. More parameters of this algorithm are under investigation.« less

The effect of dose and type of proton pump inhibitor use on risk of fractures and osteoporosis treatment in older Australian women: A prospective cohort study.

PubMed

van der Hoorn, Mariëlle M C; Tett, Susan E; de Vries, Oscar J; Dobson, Annette J; Peeters, G M E E Geeske

2015-12-01

Proton pump inhibitors (PPIs) are among the most prescribed medications worldwide, however, there is growing concern regarding potential negative effects on bone health. The aim was to examine the effect of dose and type of PPI use on subsequent use of osteoporosis medication and fractures in older Australian women. Data were included from 4432 participants (born 1921-26) in the 2002 survey of the Australian Longitudinal Study on Women's Health. Medication data were from the national pharmaceutical administrative database (2003-2012, inclusive). Fractures were sourced from linked hospital datasets available for four major States of Australia. Competing risk regression models used PPI exposure as a time-dependent covariate and either time to first osteoporosis medication prescription or fracture as the outcome, with death as a competing risk. Of the 2328 PPI users and 2104 PPI non-users, 827 (36%) and 550 (26%) became users of osteoporosis medication, respectively. PPI use was associated with an increased risk of subsequent use of osteoporosis medication (adjusted sub-hazard ratio [SHR]=1.28; 95% confidence interval [CI]=1.13-1.44) and subsequent fracture (SHR=1.29, CI=1.08-1.55). Analysis with PPI categorized according to defined daily dose (DDD), showed some evidence for a dose-response effect (osteoporosis medication: <400 DDD: SHR=1.23, CI=1.06-1.42 and ≥400 DDD: SHR=1.39, CI=1.17-1.65, compared with non-users; SHRs were in the same range for fractures). Esomeprazole was the most common PPI prescribed (22.9%). Analysis by type of PPI use showed an increased subsequent risk for: (1) use of osteoporosis medication for rabeprazole (SHR=1.51, CI=1.08-2.10) and esomeprazole (SHR=1.48, CI=1.17-1.88); and (2) fractures for rabeprazole (SHR=2.06, CI=1.37-3.10). Users of multiple types of PPI also had increased risks for use of osteoporosis medication and fractures. An appropriate benefit/risk assessment should be made when prescribing PPIs, especially for esomeprazole and rabeprazole, as osteoporosis and fracture risks were increased in this cohort of elderly females subsequent to PPI prescription. Copyright © 2015 Elsevier Inc. All rights reserved.

Major stressful life events in adulthood and risk of multiple sclerosis.

PubMed

Nielsen, Nete Munk; Bager, Peter; Simonsen, Jacob; Hviid, Anders; Stenager, Egon; Brønnum-Hansen, Henrik; Koch-Henriksen, Nils; Frisch, Morten

2014-10-01

It is unclear whether psychological stress is associated with increased risk of multiple sclerosis (MS). We studied the association between major stressful life events and MS in a nationwide cohort study using death of a child or a spouse or marital dissolution as indicators of severe stress. We created two study cohorts based on all Danish men and women born 1950-1992. One cohort consisted of all persons who became parents between 1968 and 2010, and another cohort consisted of all persons who married between 1968 and 2010. Members of both cohorts were followed for MS between 1982 and 2010 using data from the National Multiple Sclerosis Registry. Associations between major stressful life events and risk of MS were evaluated by means of MS incidence rate ratios (RR) with 95% confidence interval (CI) obtained in Poisson regression analyses. During approximately 30 million person-years of follow-up, bereaved parents experienced no unusual risk of MS compared with parents who did not lose a child (RR=1.12 (95% CI 0.89 to 1.38)). Likewise, neither divorced (RR=0.98 (95% CI 0.89 to 1.06)) nor widowed (RR=0.98 (95% CI 0.71 to 1.32) persons were at any unusual risk of MS compared with married persons of the same sex. Our national cohort study provides little evidence for a causal association between major stressful life events (as exemplified by divorce or the loss of a child or a spouse) and subsequent MS risk. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Bronchoscopy with endobronchial ultrasound guided transbronchial needle aspiration vs. transthoracic needle aspiration in lung cancer diagnosis and staging.

PubMed

Munoz, Mark L; Lechtzin, Noah; Li, Qing Kay; Wang, KoPen; Yarmus, Lonny B; Lee, Hans J; Feller-Kopman, David J

2017-07-01

In evaluating patients with suspected lung cancer, it is important to not only obtain a tissue diagnosis, but also to obtain enough tissue for both histologic and molecular analysis in order to appropriately stage the patient with a safe and efficient strategy. The diagnostic approach may often be dependent on local resources and practice patterns rather than current guidelines. We Describe lung cancer staging at two large academic medical centers to identify the impact different procedural approaches have on patient outcomes. We conducted a retrospective cohort study of all patients undergoing a lung cancer diagnostic evaluation at two multidisciplinary centers during a 1-year period. Identifying complication rates and the need for multiple biopsies as our primary outcomes, we developed a multivariate regression model to determine features associated with complications and need for multiple biopsies. Of 830 patients, 285 patients were diagnosed with lung cancers during the study period. Those staged at the institution without an endobronchial ultrasound (EBUS) program were more likely to require multiple biopsies (OR 3.62, 95% CI: 1.71-7.67, P=0.001) and suffer complications associated with the diagnostic procedure (OR 10.2, 95% CI: 3.08-33.58, P<0.001). Initial staging with transthoracic needle aspiration (TTNA) and conventional bronchoscopy were associated with greater need for subsequent biopsies (OR 8.05 and 14.00, 95% CI: 3.43-18.87 and 5.17-37.86, respectively) and higher complication rates (OR 37.75 and 7.20, 95% CI: 10.33-137.96 and 1.36-37.98, respectively). Lung cancer evaluation at centers with a dedicated EBUS program results in fewer biopsies and complications than at multidisciplinary counterparts without an EBUS program.

A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels.

PubMed

Heino, Jani; Melo, Adriano S; Bini, Luis Mauricio; Altermatt, Florian; Al-Shami, Salman A; Angeler, David G; Bonada, Núria; Brand, Cecilia; Callisto, Marcos; Cottenie, Karl; Dangles, Olivier; Dudgeon, David; Encalada, Andrea; Göthe, Emma; Grönroos, Mira; Hamada, Neusa; Jacobsen, Dean; Landeiro, Victor L; Ligeiro, Raphael; Martins, Renato T; Miserendino, María Laura; Md Rawi, Che Salmah; Rodrigues, Marciel E; Roque, Fabio de Oliveira; Sandin, Leonard; Schmera, Denes; Sgarbi, Luciano F; Simaika, John P; Siqueira, Tadeu; Thompson, Ross M; Townsend, Colin R

2015-03-01

The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low.

Identification of multiple sclerosis patients at highest risk of cognitive impairment using an integrated brain magnetic resonance imaging assessment approach.

PubMed

Uher, T; Vaneckova, M; Sormani, M P; Krasensky, J; Sobisek, L; Dusankova, J Blahova; Seidl, Z; Havrdova, E; Kalincik, T; Benedict, R H B; Horakova, D

2017-02-01

While impaired cognitive performance is common in multiple sclerosis (MS), it has been largely underdiagnosed. Here a magnetic resonance imaging (MRI) screening algorithm is proposed to identify patients at highest risk of cognitive impairment. The objective was to examine whether assessment of lesion burden together with whole brain atrophy on MRI improves our ability to identify cognitively impaired MS patients. Of the 1253 patients enrolled in the study, 1052 patients with all cognitive, volumetric MRI and clinical data available were included in the analysis. Brain MRI and neuropsychological assessment with the Brief International Cognitive Assessment for Multiple Sclerosis were performed. Multivariable logistic regression and individual prediction analysis were used to investigate the associations between MRI markers and cognitive impairment. The results of the primary analysis were validated at two subsequent time points (months 12 and 24). The prevalence of cognitive impairment was greater in patients with low brain parenchymal fraction (BPF) (<0.85) and high T2 lesion volume (T2-LV) (>3.5 ml) than in patients with high BPF (>0.85) and low T2-LV (<3.5 ml), with an odds ratio (OR) of 6.5 (95% CI 4.4-9.5). Low BPF together with high T2-LV identified in 270 (25.7%) patients predicted cognitive impairment with 83% specificity, 82% negative predictive value, 51% sensitivity and 75% overall accuracy. The risk of confirmed cognitive decline over the follow-up was greater in patients with high T2-LV (OR 2.1; 95% CI 1.1-3.8) and low BPF (OR 2.6; 95% CI 1.4-4.7). The integrated MRI assessment of lesion burden and brain atrophy may improve the stratification of MS patients who may benefit from cognitive assessment. © 2016 EAN.

Association of Emotional Labor and Occupational Stressors with Depressive Symptoms among Women Sales Workers at a Clothing Shopping Mall in the Republic of Korea: A Cross-Sectional Study

PubMed Central

Chung, Yuh-Jin; Jung, Woo-Chul

2017-01-01

In the distribution service industry, sales people often experience multiple occupational stressors such as excessive emotional labor, workplace mistreatment, and job insecurity. The present study aimed to explore the associations of these stressors with depressive symptoms among women sales workers at a clothing shopping mall in Korea. A cross sectional study was conducted on 583 women who consist of clothing sales workers and manual workers using a structured questionnaire to assess demographic factors, occupational stressors, and depressive symptoms. Multiple regression analyses were performed to explore the association of these stressors with depressive symptoms. Scores for job stress subscales such as job demand, job control, and job insecurity were higher among sales workers than among manual workers (p < 0.01). The multiple regression analysis revealed the association between occupation and depressive symptoms after controlling for age, educational level, cohabiting status, and occupational stressors (sβ = 0.08, p = 0.04). A significant interaction effect between occupation and social support was also observed in this model (sβ = −0.09, p = 0.02). The multiple regression analysis stratified by occupation showed that job demand, job insecurity, and workplace mistreatment were significantly associated with depressive symptoms in both occupations (p < 0.05), although the strength of statistical associations were slightly different. We found negative associations of social support (sβ = −0.22, p < 0.01) and emotional effort (sβ = −0.17, p < 0.01) with depressive symptoms in another multiple regression model for sales workers. Emotional dissonance (sβ = 0.23, p < 0.01) showed positive association with depressive symptoms in this model. The result of this study indicated that reducing occupational stressors would be effective for women sales workers to prevent depressive symptoms. In particular, promoting social support could be the most effective way to promote women sales workers’ mental health. PMID:29168777

Association of Emotional Labor and Occupational Stressors with Depressive Symptoms among Women Sales Workers at a Clothing Shopping Mall in the Republic of Korea: A Cross-Sectional Study.

PubMed

Chung, Yuh-Jin; Jung, Woo-Chul; Kim, Hyunjoo; Cho, Seong-Sik

2017-11-23

In the distribution service industry, sales people often experience multiple occupational stressors such as excessive emotional labor, workplace mistreatment, and job insecurity. The present study aimed to explore the associations of these stressors with depressive symptoms among women sales workers at a clothing shopping mall in Korea. A cross sectional study was conducted on 583 women who consist of clothing sales workers and manual workers using a structured questionnaire to assess demographic factors, occupational stressors, and depressive symptoms. Multiple regression analyses were performed to explore the association of these stressors with depressive symptoms. Scores for job stress subscales such as job demand, job control, and job insecurity were higher among sales workers than among manual workers ( p < 0.01). The multiple regression analysis revealed the association between occupation and depressive symptoms after controlling for age, educational level, cohabiting status, and occupational stressors (sβ = 0.08, p = 0.04). A significant interaction effect between occupation and social support was also observed in this model (sβ = -0.09, p = 0.02). The multiple regression analysis stratified by occupation showed that job demand, job insecurity, and workplace mistreatment were significantly associated with depressive symptoms in both occupations ( p < 0.05), although the strength of statistical associations were slightly different. We found negative associations of social support (sβ = -0.22, p < 0.01) and emotional effort (sβ = -0.17, p < 0.01) with depressive symptoms in another multiple regression model for sales workers. Emotional dissonance (sβ = 0.23, p < 0.01) showed positive association with depressive symptoms in this model. The result of this study indicated that reducing occupational stressors would be effective for women sales workers to prevent depressive symptoms. In particular, promoting social support could be the most effective way to promote women sales workers' mental health.

When Law Students Read Multiple Documents about Global Warming: Examining the Role of Topic-Specific Beliefs about the Nature of Knowledge and Knowing

ERIC Educational Resources Information Center

Braten, Ivar; Stromso, Helge I.

2010-01-01

In this study, law students (n = 49) read multiple authentic documents presenting conflicting information on the topic of climate change and responded to verification tasks assessing their superficial as well as their deeper-level within- and across-documents comprehension. Hierarchical multiple regression analyses showed that even after variance…

Concordance of somatic mutational profile in multiple primary melanomas.

PubMed

Adler, Nikki R; McLean, Catriona A; Wolfe, Rory; Kelly, John W; McArthur, Grant A; Haydon, Andrew; Tra, Thien; Cummings, Nicholas; Mar, Victoria J

2018-03-30

This study aimed to determine the frequency and concordance of BRAF and NRAS mutation in tumours arising in patients with multiple primary melanoma (MPM). Patients with MPM managed at one of three tertiary referral centres in Melbourne, Australia, from 2010 to 2015 were included. Incident and subsequent melanomas underwent mutation testing. Cohen's kappa (κ) coefficient assessed agreement between incident and subsequent primary melanomas for both BRAF and NRAS mutation status (mutant versus wild-type). Mutation testing of at least two primary tumours from 64 patients was conducted. There was poor agreement for both BRAF and NRAS mutation status between incident and subsequent melanomas (κ = 0.10, 95% CI -0.10 to 0.42; κ = 0.06, 95% CI -0.10 to 0.57, respectively). In view of the low concordance in BRAF mutation status between incident and subsequent melanomas, mutational analysis of metastatic tissue, rather than of a primary melanoma, in patients with MPM should be used to guide targeted therapy. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Depressive disorder in pregnant Latin women: does intimate partner violence matter?

PubMed

Fonseca-Machado, Mariana de Oliveira; Alves, Lisiane Camargo; Monteiro, Juliana Cristina Dos Santos; Stefanello, Juliana; Nakano, Ana Márcia Spanó; Haas, Vanderlei José; Gomes-Sponholz, Flávia

2015-05-01

To identify the association of antenatal depressive symptoms with intimate partner violence during the current pregnancy in Brazilian women. Intimate partner violence is an important risk factor for antenatal depression. To the authors' knowledge, there has been no study to date that assessed the association between intimate partner violence during pregnancy and antenatal depressive symptoms among Brazilian women. Cross-sectional study. Three hundred and fifty-eight pregnant women were enrolled in the study. The Edinburgh Postnatal Depression Scale and an adapted version of the instrument used in the World Health Organization Multi-country Study on Women's Health and Domestic Violence were used to measure antenatal depressive symptoms and psychological, physical and sexual acts of intimate partner violence during the current pregnancy respectively. Multiple logistic regression and multiple linear regression were used for data analysis. The prevalence of antenatal depressive symptoms, as determined by the cut-off score of 12 in the Edinburgh Postnatal Depression Scale, was 28·2% (101). Of the participants, 63 (17·6%) reported some type of intimate partner violence during pregnancy. Among them, 60 (95·2%) reported suffering psychological violence, 23 (36·5%) physical violence and one (1·6%) sexual violence. Multiple logistic regression and multiple linear regression indicated that antenatal depressive symptoms are extremely associated with intimate partner violence during pregnancy. Among Brazilian women, exposure to intimate partner violence during pregnancy increases the chances of experiencing antenatal depressive symptoms. Clinical nurses and nurses midwifes should pay attention to the particularities of Brazilian women, especially with regard to the occurrence of intimate partner violence, whose impacts on the mental health of this population are extremely significant, both during the gestational period and postpartum. © 2015 John Wiley & Sons Ltd.

Simple to complex modeling of breathing volume using a motion sensor.

PubMed

John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-06-01

To compare simple and complex modeling techniques to estimate categories of low, medium, and high ventilation (VE) from ActiGraph™ activity counts. Vertical axis ActiGraph™ GT1M activity counts, oxygen consumption and VE were measured during treadmill walking and running, sports, household chores and labor-intensive employment activities. Categories of low (<19.3 l/min), medium (19.3 to 35.4 l/min) and high (>35.4 l/min) VEs were derived from activity intensity classifications (light <2.9 METs, moderate 3.0 to 5.9 METs and vigorous >6.0 METs). We examined the accuracy of two simple techniques (multiple regression and activity count cut-point analyses) and one complex (random forest technique) modeling technique in predicting VE from activity counts. Prediction accuracy of the complex random forest technique was marginally better than the simple multiple regression method. Both techniques accurately predicted VE categories almost 80% of the time. The multiple regression and random forest techniques were more accurate (85 to 88%) in predicting medium VE. Both techniques predicted the high VE (70 to 73%) with greater accuracy than low VE (57 to 60%). Actigraph™ cut-points for light, medium and high VEs were <1381, 1381 to 3660 and >3660 cpm. There were minor differences in prediction accuracy between the multiple regression and the random forest technique. This study provides methods to objectively estimate VE categories using activity monitors that can easily be deployed in the field. Objective estimates of VE should provide a better understanding of the dose-response relationship between internal exposure to pollutants and disease. Copyright © 2013 Elsevier B.V. All rights reserved.

Functional capacity following univentricular repair--midterm outcome.

PubMed

Sen, Supratim; Bandyopadhyay, Biswajit; Eriksson, Peter; Chattopadhyay, Amitabha

2012-01-01

Previous studies have seldom compared functional capacity in children following Fontan procedure alongside those with Glenn operation as destination therapy. We hypothesized that Fontan circulation enables better midterm submaximal exercise capacity as compared to Glenn physiology and evaluated this using the 6-minute walk test. Fifty-seven children aged 5-18 years with Glenn (44) or Fontan (13) operations were evaluated with standard 6-minute walk protocols. Baseline SpO(2) was significantly lower in Glenn patients younger than 10 years compared to Fontan counterparts and similar in the two groups in older children. Postexercise SpO(2) fell significantly in Glenn patients compared to the Fontan group. There was no statistically significant difference in baseline, postexercise, or postrecovery heart rates (HRs), or 6-minute walk distances in the two groups. Multiple regression analysis revealed lower resting HR, higher resting SpO(2) , and younger age at latest operation to be significant determinants of longer 6-minute walk distance. Multiple regression analysis also established that younger age at operation, higher resting SpO(2) , Fontan operation, lower resting HR, and lower postexercise HR were significant determinants of higher postexercise SpO(2) . Younger age at operation and exercise, lower resting HR and postexercise HR, higher resting SpO(2) and postexercise SpO(2) , and dominant ventricular morphology being left ventricular or indeterminate/mixed had significant association with better 6-minute work on multiple regression analysis. Lower resting HR had linear association with longer 6-minute walk distances in the Glenn patients. Compared to Glenn physiology, Fontan operation did not have better submaximal exercise capacity assessed by walk distance or work on multiple regression analysis. Lower resting HR, higher resting SpO(2) , and younger age at operation were factors uniformly associated with better submaximal exercise capacity. © 2012 Wiley Periodicals, Inc.

Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes

NASA Astrophysics Data System (ADS)

Mekanik, F.; Imteaz, M. A.; Gato-Trinidad, S.; Elmahdi, A.

2013-10-01

In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO-IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation (r) and Willmott index of agreement (d). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of -0.99 to -0.90 compared to ANN with correlation coefficients of 0.42-0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68-0.85 and 0.58-0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria (r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

HRCT findings of collagen vascular disease-related interstitial pneumonia (CVD-IP): a comparative study among individual underlying diseases.

PubMed

Tanaka, N; Kunihiro, Y; Kubo, M; Kawano, R; Oishi, K; Ueda, K; Gondo, T

2018-05-29

To identify characteristic high-resolution computed tomography (CT) findings for individual collagen vascular disease (CVD)-related interstitial pneumonias (IPs). The HRCT findings of 187 patients with CVD, including 55 patients with rheumatoid arthritis (RA), 50 with systemic sclerosis (SSc), 46 with polymyositis/dermatomyositis (PM/DM), 15 with mixed connective tissue disease, 11 with primary Sjögren's syndrome, and 10 with systemic lupus erythematosus, were evaluated. Lung parenchymal abnormalities were compared among CVDs using χ 2 test, Kruskal-Wallis test, and multiple logistic regression analysis. A CT-pathology correlation was performed in 23 patients. In RA-IP, honeycombing was identified as the significant indicator based on multiple logistic regression analyses. Traction bronchiectasis (81.8%) was further identified as the most frequent finding based on χ 2 test. In SSc IP, lymph node enlargement and oesophageal dilatation were identified as the indicators based on multiple logistic regression analyses, and ground-glass opacity (GGO) was the most extensive based on Kruskal-Wallis test, which reflects the higher frequency of the pathological nonspecific interstitial pneumonia (NSIP) pattern present in the CT-pathology correlation. In PM/DM IP, airspace consolidation and the absence of honeycombing were identified as the indicators based on multiple logistic regression analyses, and predominance of consolidation over GGO (32.6%) and predominant subpleural distribution of GGO/consolidation (41.3%) were further identified as the most frequent findings based on χ 2 test, which reflects the higher frequency of the pathological NSIP and/or the organising pneumonia patterns present in the CT-pathology correlation. Several characteristic high-resolution CT findings with utility for estimating underlying CVD were identified. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Construction of multiple linear regression models using blood biomarkers for selecting against abdominal fat traits in broilers.

PubMed

Dong, J Q; Zhang, X Y; Wang, S Z; Jiang, X F; Zhang, K; Ma, G W; Wu, M Q; Li, H; Zhang, H

2018-01-01

Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P < 0.01), and correlated significantly with AF traits (P < 0.05). The best multiple linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.

Role of Snf5 Mutations in Schwannomatosis Pain

DTIC Science & Technology

2016-10-01

National Multiple Sclerosis Society • Name: • Fatima Banine • Project Role: • Staff Scientist • Researcher Identifier (e.g. ORCID ID...and the subsequent data analysis and validation assays (e.g. qPCR) • Funding Support: • National Multiple Sclerosis Society • Name: • Scott...40,000 individuals worldwide. The disease is characterized by multiple peripheral nerve tumors, called schwannomas, and a predisposition to other

Automating approximate Bayesian computation by local linear regression.

PubMed

Thornton, Kevin R

2009-07-07

In several biological contexts, parameter inference often relies on computationally-intensive techniques. "Approximate Bayesian Computation", or ABC, methods based on summary statistics have become increasingly popular. A particular flavor of ABC based on using a linear regression to approximate the posterior distribution of the parameters, conditional on the summary statistics, is computationally appealing, yet no standalone tool exists to automate the procedure. Here, I describe a program to implement the method. The software package ABCreg implements the local linear-regression approach to ABC. The advantages are: 1. The code is standalone, and fully-documented. 2. The program will automatically process multiple data sets, and create unique output files for each (which may be processed immediately in R), facilitating the testing of inference procedures on simulated data, or the analysis of multiple data sets. 3. The program implements two different transformation methods for the regression step. 4. Analysis options are controlled on the command line by the user, and the program is designed to output warnings for cases where the regression fails. 5. The program does not depend on any particular simulation machinery (coalescent, forward-time, etc.), and therefore is a general tool for processing the results from any simulation. 6. The code is open-source, and modular.Examples of applying the software to empirical data from Drosophila melanogaster, and testing the procedure on simulated data, are shown. In practice, the ABCreg simplifies implementing ABC based on local-linear regression.

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

PubMed

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel.

PubMed

Brown, Angus M

2006-04-01

The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.

Regression-based pediatric norms for the brief visuospatial memory test: revised and the symbol digit modalities test.

PubMed

Smerbeck, A M; Parrish, J; Yeh, E A; Hoogs, M; Krupp, Lauren B; Weinstock-Guttman, B; Benedict, R H B

2011-04-01

The Brief Visuospatial Memory Test - Revised (BVMTR) and the Symbol Digit Modalities Test (SDMT) oral-only administration are known to be sensitive to cerebral disease in adult samples, but pediatric norms are not available. A demographically balanced sample of healthy control children (N = 92) ages 6-17 was tested with the BVMTR and SDMT. Multiple regression analysis (MRA) was used to develop demographically controlled normative equations. This analysis provided equations that were then used to construct demographically adjusted z-scores for the BVMTR Trial 1, Trial 2, Trial 3, Total Learning, and Delayed Recall indices, as well as the SDMT total correct score. To demonstrate the utility of this approach, a comparison group of children with acute disseminated encephalomyelitis (ADEM) or multiple sclerosis (MS) were also assessed. We find that these visual processing tests discriminate neurological patients from controls. As the tests are validated in adult multiple sclerosis, they are likely to be useful in monitoring pediatric onset multiple sclerosis patients as they transition into adulthood.

Arteriopathy after transarterial chemo-lipiodolization for hepatocellular carcinoma.

PubMed

Matsui, Y; Figi, A; Horikawa, M; Jahangiri Noudeh, Y; Tomozawa, Y; Hashimoto, K; Kaufman, J A; Farsad, K

2017-12-01

The purpose of this study was to investigate the incidence of and the risk factors for arteriopathy in hepatic arteries after transarterial chemo-lipiodolization in patients with hepatocellular carcinoma and the subsequent treatment strategy changes due to arteriopathy. A total of 365 arteries in 167 patients (126 men and 41 women; mean age, 60.4±15.0 [SD] years [range: 18-87 years]) were evaluated for the development of arteriopathy after chemo-lipiodolization with epirubicin- or doxorubicin-Lipiodol ® emulsion. The development of arteriopathy after chemo-lipiodolization was assessed on arteriograms performed during subsequent transarterial treatments. The treatment strategy changes due to arteriopathy, including change in the chemo-lipiodolization method and the application of alternative therapies was also investigated. Univariate and multivariate binary logistic regression models were used to identify risk factors for arteriopathy and subsequent treatment strategy change. One hundred two (27.9%) arteriopathies were detected in 62/167 (37.1%) patients (45 men, 17 women) with a mean age of 63.3±7.1 [SD] years (age range, 50-86 years). The incidence of arteriopathy was highly patient dependent, demonstrating significant correlation in a fully-adjusted multivariate regression model (P<0.0001). Multivariate-adjusted regression analysis with adjustment for the patient effect showed a statistically significant association of super-selective chemo-lipiodolization (P=0.003) with the incidence of arteriopathy. Thirty of the 102 arteriopathies (29.4%) caused a change in treatment strategy. No factors were found to be significantly associated with the treatment strategy change. The incidence of arteriopathy after chemo-lipiodolization is 27.9%. Among them, 29.4% result in a change in treatment strategy. Copyright © 2017 Editions françaises de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Tax amnesties, justice perceptions, and filing behavior: a simulation study.

PubMed

Rechberger, Silvia; Hartner, Martina; Kirchler, Erich; Hämmerle, Franziska

2010-04-01

A simulation study demonstrates the influence of perceived justice of a tax amnesty on subsequent tax compliance. In addition, it investigates how the amnesty is perceived to serve the punishment objectives retribution (i.e., giving offenders what they "deserve") and value restoration (i.e., restoring the values violated by tax evasion). Hierarchical regression analysis revealed the expected positive influence of justice on subsequent tax compliance. However, when the influence of punishment objectives was controlled for, the influence of justice disappeared, while retribution and value restoration showed positive effects on post-amnesty tax compliance.

Confidence Intervals for Squared Semipartial Correlation Coefficients: The Effect of Nonnormality

ERIC Educational Resources Information Center

Algina, James; Keselman, H. J.; Penfield, Randall D.

2010-01-01

The increase in the squared multiple correlation coefficient ([delta]R[superscript 2]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. Algina, Keselman, and Penfield found that intervals based on asymptotic principles were typically very inaccurate, even though the sample size…

Generalized and synthetic regression estimators for randomized branch sampling

Treesearch

David L. R. Affleck; Timothy G. Gregoire

2015-01-01

In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (...

Does Bootstrap Procedure Provide Biased Estimates? An Empirical Examination for a Case of Multiple Regression.

ERIC Educational Resources Information Center

Fan, Xitao

This paper empirically and systematically assessed the performance of bootstrap resampling procedure as it was applied to a regression model. Parameter estimates from Monte Carlo experiments (repeated sampling from population) and bootstrap experiments (repeated resampling from one original bootstrap sample) were generated and compared. Sample…

Progressive and Regressive Aspects of Information Technology in Society: A Third Sector Perspective

ERIC Educational Resources Information Center

Miller, Kandace R.

2009-01-01

This dissertation explores the impact of information technology on progressive and regressive values in society from the perspective of one international foundation and four of its technology-related programs. Through a critical interpretive approach employing an instrumental multiple-case method, a framework to help explain the influence of…

Correlation Weights in Multiple Regression

ERIC Educational Resources Information Center

Waller, Niels G.; Jones, Jeff A.

2010-01-01

A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…

No Evidence of Reaction Time Slowing in Autism Spectrum Disorder

ERIC Educational Resources Information Center

Ferraro, F. Richard

2016-01-01

A total of 32 studies comprising 238 simple reaction time and choice reaction time conditions were examined in individuals with autism spectrum disorder (n?=?964) and controls (n?=?1032). A Brinley plot/multiple regression analysis was performed on mean reaction times, regressing autism spectrum disorder performance onto the control performance as…

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R. (Principal Investigator)

1982-01-01

Data analysis procedures for quantification of water quality parameters that are already identified and are known to exist within the water body are considered. The liner multiple-regression technique was examined as a procedure for defining and calibrating data analysis algorithms for such instruments as spectrometers and multispectral scanners.

An Empirical Study of Eight Nonparametric Tests in Hierarchical Regression.

ERIC Educational Resources Information Center

Harwell, Michael; Serlin, Ronald C.

When normality does not hold, nonparametric tests represent an important data-analytic alternative to parametric tests. However, the use of nonparametric tests in educational research has been limited by the absence of easily performed tests for complex experimental designs and analyses, such as factorial designs and multiple regression analyses,…

Multiple Logistic Regression Analysis of Cigarette Use among High School Students

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2011-01-01

A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

The Development and Demonstration of Multiple Regression Models for Operant Conditioning Questions.

ERIC Educational Resources Information Center

Fanning, Fred; Newman, Isadore

Based on the assumption that inferential statistics can make the operant conditioner more sensitive to possible significant relationships, regressions models were developed to test the statistical significance between slopes and Y intercepts of the experimental and control group subjects. These results were then compared to the traditional operant…

How Many Subjects Does It Take to Do a Regression Analysis?

ERIC Educational Resources Information Center

Green, Samuel B.

1991-01-01

An evaluation of the rules-of-thumb used to determine the minimum number of subjects required to conduct multiple regression analyses suggests that researchers who use a rule of thumb rather than power analyses trade simplicity of use for accuracy and specificity of response. Insufficient power is likely to result. (SLD)

Hierarchical Multiple Regression in Counseling Research: Common Problems and Possible Remedies.

ERIC Educational Resources Information Center

Petrocelli, John V.

2003-01-01

A brief content analysis was conducted on the use of hierarchical regression in counseling research published in the "Journal of Counseling Psychology" and the "Journal of Counseling & Development" during the years 1997-2001. Common problems are cited and possible remedies are described. (Contains 43 references and 3 tables.) (Author)

Assistive Technologies for Second-Year Statistics Students Who Are Blind

ERIC Educational Resources Information Center

Erhardt, Robert J.; Shuman, Michael P.

2015-01-01

At Wake Forest University, a student who is blind enrolled in a second course in statistics. The course covered simple and multiple regression, model diagnostics, model selection, data visualization, and elementary logistic regression. These topics required that the student both interpret and produce three sets of materials: mathematical writing,…

Testing a single regression coefficient in high dimensional linear models

PubMed Central

Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2017-01-01

In linear regression models with high dimensional data, the classical z-test (or t-test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z-test to assess the significance of each covariate. Based on the p-value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively. PMID:28663668

Testing a single regression coefficient in high dimensional linear models.

PubMed

Lan, Wei; Zhong, Ping-Shou; Li, Runze; Wang, Hansheng; Tsai, Chih-Ling

2016-11-01

In linear regression models with high dimensional data, the classical z -test (or t -test) for testing the significance of each single regression coefficient is no longer applicable. This is mainly because the number of covariates exceeds the sample size. In this paper, we propose a simple and novel alternative by introducing the Correlated Predictors Screening (CPS) method to control for predictors that are highly correlated with the target covariate. Accordingly, the classical ordinary least squares approach can be employed to estimate the regression coefficient associated with the target covariate. In addition, we demonstrate that the resulting estimator is consistent and asymptotically normal even if the random errors are heteroscedastic. This enables us to apply the z -test to assess the significance of each covariate. Based on the p -value obtained from testing the significance of each covariate, we further conduct multiple hypothesis testing by controlling the false discovery rate at the nominal level. Then, we show that the multiple hypothesis testing achieves consistent model selection. Simulation studies and empirical examples are presented to illustrate the finite sample performance and the usefulness of the proposed method, respectively.

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data.

PubMed

Spelman, Tim; Gray, Orla; Lucas, Robyn; Butzkueven, Helmut

2015-12-09

This report describes a novel Stata-based application of trigonometric regression modelling to 55 years of multiple sclerosis relapse data from 46 clinical centers across 20 countries located in both hemispheres. Central to the success of this method was the strategic use of plot analysis to guide and corroborate the statistical regression modelling. Initial plot analysis was necessary for establishing realistic hypotheses regarding the presence and structural form of seasonal and latitudinal influences on relapse probability and then testing the performance of the resultant models. Trigonometric regression was then necessary to quantify these relationships, adjust for important confounders and provide a measure of certainty as to how plausible these associations were. Synchronization of graphing techniques with regression modelling permitted a systematic refinement of models until best-fit convergence was achieved, enabling novel inferences to be made regarding the independent influence of both season and latitude in predicting relapse onset timing in MS. These methods have the potential for application across other complex disease and epidemiological phenomena suspected or known to vary systematically with season and/or geographic location.

Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.

PubMed

Testa, S Marc; Winicki, Jessica M; Pearlson, Godfrey D; Gordon, Barry; Schretlen, David J

2009-11-01

Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Understanding logistic regression analysis.

PubMed

Sperandei, Sandro

2014-01-01

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.

Two SPSS programs for interpreting multiple regression results.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo

2010-02-01

When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.

Fast determination of total ginsenosides content in ginseng powder by near infrared reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Hua-cai; Chen, Xing-dan; Lu, Yong-jun; Cao, Zhi-qiang

2006-01-01

Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm~1880 nm and 2230nm~2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR), principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm~2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.