Ordinal Logistic Regression to Detect Differential Item Functioning for Gender in the Institutional Integration Scale

ERIC Educational Resources Information Center

Breidenbach, Daniel H.; French, Brian F.

2011-01-01

Many factors can influence a student's decision to withdraw from college. Intervention programs aimed at retention can benefit from understanding the factors related to such decisions, especially in underrepresented groups. The Institutional Integration Scale (IIS) has been suggested as a predictor of student persistence. Accurate prediction of…

Influence of landscape-scale factors in limiting brook trout populations in Pennsylvania streams

USGS Publications Warehouse

Kocovsky, P.M.; Carline, R.F.

2006-01-01

Landscapes influence the capacity of streams to produce trout through their effect on water chemistry and other factors at the reach scale. Trout abundance also fluctuates over time; thus, to thoroughly understand how spatial factors at landscape scales affect trout populations, one must assess the changes in populations over time to provide a context for interpreting the importance of spatial factors. We used data from the Pennsylvania Fish and Boat Commission's fisheries management database to investigate spatial factors that affect the capacity of streams to support brook trout Salvelinus fontinalis and to provide models useful for their management. We assessed the relative importance of spatial and temporal variation by calculating variance components and comparing relative standard errors for spatial and temporal variation. We used binary logistic regression to predict the presence of harvestable-length brook trout and multiple linear regression to assess the mechanistic links between landscapes and trout populations and to predict population density. The variance in trout density among streams was equal to or greater than the temporal variation for several streams, indicating that differences among sites affect population density. Logistic regression models correctly predicted the absence of harvestable-length brook trout in 60% of validation samples. The r 2-value for the linear regression model predicting density was 0.3, indicating low predictive ability. Both logistic and linear regression models supported buffering capacity against acid episodes as an important mechanistic link between landscapes and trout populations. Although our models fail to predict trout densities precisely, their success at elucidating the mechanistic links between landscapes and trout populations, in concert with the importance of spatial variation, increases our understanding of factors affecting brook trout abundance and will help managers and private groups to protect and enhance populations of wild brook trout. ?? Copyright by the American Fisheries Society 2006.

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.

Automatic segmentation and classification of mycobacterium tuberculosis with conventional light microscopy

NASA Astrophysics Data System (ADS)

Xu, Chao; Zhou, Dongxiang; Zhai, Yongping; Liu, Yunhui

2015-12-01

This paper realizes the automatic segmentation and classification of Mycobacterium tuberculosis with conventional light microscopy. First, the candidate bacillus objects are segmented by the marker-based watershed transform. The markers are obtained by an adaptive threshold segmentation based on the adaptive scale Gaussian filter. The scale of the Gaussian filter is determined according to the color model of the bacillus objects. Then the candidate objects are extracted integrally after region merging and contaminations elimination. Second, the shape features of the bacillus objects are characterized by the Hu moments, compactness, eccentricity, and roughness, which are used to classify the single, touching and non-bacillus objects. We evaluated the logistic regression, random forest, and intersection kernel support vector machines classifiers in classifying the bacillus objects respectively. Experimental results demonstrate that the proposed method yields to high robustness and accuracy. The logistic regression classifier performs best with an accuracy of 91.68%.

On the Validity of Validity Scales: The Importance of Defensive Responding in the Prediction of Institutional Misconduct

ERIC Educational Resources Information Center

Edens, John F.; Ruiz, Mark A.

2006-01-01

This study examined the effects of defensive responding on the prediction of institutional misconduct among male inmates (N = 349) who completed the Personality Assessment Inventory (L. C. Morey, 1991). Hierarchical logistic regression analyses demonstrated significant main effects for the Antisocial Features (ANT) scale as well as main effects…

Early warnings for suicide attempt among Chinese rural population.

PubMed

Lyu, Juncheng; Wang, Yingying; Shi, Hong; Zhang, Jie

2018-06-05

This study was to explore the main influencing factors of attempted suicide and establish an early warning model, so as to put forward prevention strategies for attempted suicide. Data came from a large-scale case-control epidemiological survey. A sample of 659 serious suicide attempters was randomly recruited from 13 rural counties in China. Each case was matched by a community control for gender, age, and residence location. Face to face interviews were conducted for all the cases and controls with the same structured questionnaire. Univariate logistic regression was applied to screen the factors and multivariate logistic regression was used to excavate the predictors. There were no statistical differences between suicide attempters and the community controls in gender, age, and residence location. The Cronbach`s coefficients for all the scales used were above 0.675. The multivariate logistic regressions have revealed 12 statistically significant variables predicting attempted suicide, including less education, family history of suicide, poor health, mental problem, aspiration strain, hopelessness, impulsivity, depression, negative life events. On the other hand, social support, coping skills, and healthy community protected the rural residents from suicide attempt. The excavated warning predictors are significant clinical meaning for the clinical psychiatrist. Crisis intervention strategies in rural China should be informed by the findings from this research. Education, social support, healthy community, and strain reduction are all measures to decrease the likelihood of crises. Copyright © 2018. Published by Elsevier B.V.

Association Between Socio-Demographic Background and Self-Esteem of University Students.

PubMed

Haq, Muhammad Ahsan Ul

2016-12-01

The purpose of this study was to scrutinize self-esteem of university students and explore association of self-esteem with academic achievement, gender and other factors. A sample of 346 students was selected from Punjab University, Lahore Pakistan. Rosenberg self-esteem scale with demographic variables was used for data collection. Besides descriptive statistics, binary logistic regression and t test were used for analysing the data. Significant gender difference was observed, self-esteem was significantly higher in males than females. Logistic regression indicates that age, medium of instruction, family income, student monthly expenditures, GPA and area of residence has direct effect on self-esteem; while number of siblings showed an inverse effect.

The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

NASA Astrophysics Data System (ADS)

Ariffin, Syaiba Balqish; Midi, Habshah

2014-06-01

This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity.

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.

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

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

Bramer, L. M.; Rounds, J.; Burleyson, C. D.

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions is examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and datasets were examined. A penalized logistic regression model fit at the operation-zone levelmore » was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at different time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. The methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.« less

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

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

Bramer, Lisa M.; Rounds, J.; Burleyson, C. D.

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which wasmore » fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. In conclusion, the methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.« less

HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS

PubMed Central

Wang, Shuang; Zhang, Yuchen; Dai, Wenrui; Lauter, Kristin; Kim, Miran; Tang, Yuzhe; Xiong, Hongkai; Jiang, Xiaoqian

2016-01-01

Motivation: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual’s privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. Results: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. Availability and implementation: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ Contact: shw070@ucsd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26446135

Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

DOE PAGES

Bramer, Lisa M.; Rounds, J.; Burleyson, C. D.; ...

2017-09-22

Understanding the conditions associated with stress on the electricity grid is important in the development of contingency plans for maintaining reliability during periods when the grid is stressed. In this paper, heat-related grid stress and the relationship with weather conditions were examined using data from the eastern United States. Penalized logistic regression models were developed and applied to predict stress on the electric grid using weather data. The inclusion of other weather variables, such as precipitation, in addition to temperature improved model performance. Several candidate models and combinations of predictive variables were examined. A penalized logistic regression model which wasmore » fit at the operation-zone level was found to provide predictive value and interpretability. Additionally, the importance of different weather variables observed at various time scales were examined. Maximum temperature and precipitation were identified as important across all zones while the importance of other weather variables was zone specific. In conclusion, the methods presented in this work are extensible to other regions and can be used to aid in planning and development of the electrical grid.« less

A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.

PubMed

Staley, James R; Jones, Edmund; Kaptoge, Stephen; Butterworth, Adam S; Sweeting, Michael J; Wood, Angela M; Howson, Joanna M M

2017-06-01

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP-disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.

The crux of the method: assumptions in ordinary least squares and logistic regression.

PubMed

Long, Rebecca G

2008-10-01

Logistic regression has increasingly become the tool of choice when analyzing data with a binary dependent variable. While resources relating to the technique are widely available, clear discussions of why logistic regression should be used in place of ordinary least squares regression are difficult to find. The current paper compares and contrasts the assumptions of ordinary least squares with those of logistic regression and explains why logistic regression's looser assumptions make it adept at handling violations of the more important assumptions in ordinary least squares.

A secure distributed logistic regression protocol for the detection of rare adverse drug events

PubMed Central

El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-01-01

Background There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. Objective To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. Methods We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. Results The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. Conclusion The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models. PMID:22871397

Measurement of faculty anesthesiologists' quality of clinical supervision has greater reliability when controlling for the leniency of the rating anesthesia resident: a retrospective cohort study.

PubMed

Dexter, Franklin; Ledolter, Johannes; Hindman, Bradley J

2017-06-01

Our department monitors the quality of anesthesiologists' clinical supervision and provides each anesthesiologist with periodic feedback. We hypothesized that greater differentiation among anesthesiologists' supervision scores could be obtained by adjusting for leniency of the rating resident. From July 1, 2013 to December 31, 2015, our department has utilized the de Oliveira Filho unidimensional nine-item supervision scale to assess the quality of clinical supervision provided by faculty as rated by residents. We examined all 13,664 ratings of the 97 anesthesiologists (ratees) by the 65 residents (raters). Testing for internal consistency among answers to questions (large Cronbach's alpha > 0.90) was performed to rule out that one or two questions accounted for leniency. Mixed-effects logistic regression was used to compare ratees while controlling for rater leniency vs using Student t tests without rater leniency. The mean supervision scale score was calculated for each combination of the 65 raters and nine questions. The Cronbach's alpha was very large (0.977). The mean score was calculated for each of the 3,421 observed combinations of resident and anesthesiologist. The logits of the percentage of scores equal to the maximum value of 4.00 were normally distributed (residents, P = 0.24; anesthesiologists, P = 0.50). There were 20/97 anesthesiologists identified as significant outliers (13 with below average supervision scores and seven with better than average) using the mixed-effects logistic regression with rater leniency entered as a fixed effect but not by Student's t test. In contrast, there were three of 97 anesthesiologists identified as outliers (all three above average) using Student's t tests but not by logistic regression with leniency. The 20 vs 3 was significant (P < 0.001). Use of logistic regression with leniency results in greater detection of anesthesiologists with significantly better (or worse) clinical supervision scores than use of Student's t tests (i.e., without adjustment for rater leniency).

A secure distributed logistic regression protocol for the detection of rare adverse drug events.

PubMed

El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-05-01

There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models.

Using Dominance Analysis to Determine Predictor Importance in Logistic Regression

ERIC Educational Resources Information Center

Azen, Razia; Traxel, Nicole

2009-01-01

This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…

Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio.

PubMed

Lloyd-Jones, Luke R; Robinson, Matthew R; Yang, Jian; Visscher, Peter M

2018-04-01

Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure ( e.g. , a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects. Copyright © 2018 by the Genetics Society of America.

Applying Kaplan-Meier to Item Response Data

ERIC Educational Resources Information Center

McNeish, Daniel

2018-01-01

Some IRT models can be equivalently modeled in alternative frameworks such as logistic regression. Logistic regression can also model time-to-event data, which concerns the probability of an event occurring over time. Using the relation between time-to-event models and logistic regression and the relation between logistic regression and IRT, this…

A reconnaissance method for delineation of tracts for regional-scale mineral-resource assessment based on geologic-map data

USGS Publications Warehouse

Raines, G.L.; Mihalasky, M.J.

2002-01-01

The U.S. Geological Survey (USGS) is proposing to conduct a global mineral-resource assessment using geologic maps, significant deposits, and exploration history as minimal data requirements. Using a geologic map and locations of significant pluton-related deposits, the pluton-related-deposit tract maps from the USGS national mineral-resource assessment have been reproduced with GIS-based analysis and modeling techniques. Agreement, kappa, and Jaccard's C correlation statistics between the expert USGS and calculated tract maps of 87%, 40%, and 28%, respectively, have been achieved using a combination of weights-of-evidence and weighted logistic regression methods. Between the experts' and calculated maps, the ranking of states measured by total permissive area correlates at 84%. The disagreement between the experts and calculated results can be explained primarily by tracts defined by geophysical evidence not considered in the calculations, generalization of tracts by the experts, differences in map scales, and the experts' inclusion of large tracts that are arguably not permissive. This analysis shows that tracts for regional mineral-resource assessment approximating those delineated by USGS experts can be calculated using weights of evidence and weighted logistic regression, a geologic map, and the location of significant deposits. Weights of evidence and weighted logistic regression applied to a global geologic map could provide quickly a useful reconnaissance definition of tracts for mineral assessment that is tied to the data and is reproducible. ?? 2002 International Association for Mathematical Geology.

Process model comparison and transferability across bioreactor scales and modes of operation for a mammalian cell bioprocess.

PubMed

Craven, Stephen; Shirsat, Nishikant; Whelan, Jessica; Glennon, Brian

2013-01-01

A Monod kinetic model, logistic equation model, and statistical regression model were developed for a Chinese hamster ovary cell bioprocess operated under three different modes of operation (batch, bolus fed-batch, and continuous fed-batch) and grown on two different bioreactor scales (3 L bench-top and 15 L pilot-scale). The Monod kinetic model was developed for all modes of operation under study and predicted cell density, glucose glutamine, lactate, and ammonia concentrations well for the bioprocess. However, it was computationally demanding due to the large number of parameters necessary to produce a good model fit. The transferability of the Monod kinetic model structure and parameter set across bioreactor scales and modes of operation was investigated and a parameter sensitivity analysis performed. The experimentally determined parameters had the greatest influence on model performance. They changed with scale and mode of operation, but were easily calculated. The remaining parameters, which were fitted using a differential evolutionary algorithm, were not as crucial. Logistic equation and statistical regression models were investigated as alternatives to the Monod kinetic model. They were less computationally intensive to develop due to the absence of a large parameter set. However, modeling of the nutrient and metabolite concentrations proved to be troublesome due to the logistic equation model structure and the inability of both models to incorporate a feed. The complexity, computational load, and effort required for model development has to be balanced with the necessary level of model sophistication when choosing which model type to develop for a particular application. Copyright © 2012 American Institute of Chemical Engineers (AIChE).

Life Satisfaction and Violent Behaviors among Middle School Students

ERIC Educational Resources Information Center

Valois, Robert F.; Paxton, Raheem J.; Zullig, Keith J.; Huebner, E. Scott

2006-01-01

We explored relationships between violent behaviors and perceived life satisfaction among 2,138 middle school students in a southern state using the CDC Middle School Youth Risk Behavior Survey (MSYRBS) and the Brief Multidimensional Student Life Satisfaction Scale (BMSLSS). Logistic regression analyses and multivariate models constructed…

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

NASA Astrophysics Data System (ADS)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Differential Item Functioning Analysis of the "Preschool Language Scale-4" between English-Speaking Hispanic and European American Children from Low-Income Families

ERIC Educational Resources Information Center

Qi, Cathy Huaqing; Marley, Scott C.

2009-01-01

The study examined whether item bias is present in the "Preschool Language Scale-4" (PLS-4). Participants were 440 children (3-5 years old; 86% English-speaking Hispanic and 14% European American) who were enrolled in Head Start programs. The PLS-4 items were analyzed for differential item functioning (DIF) using logistic regression and…

Neural network modeling for surgical decisions on traumatic brain injury patients.

PubMed

Li, Y C; Liu, L; Chiu, W T; Jian, W S

2000-01-01

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeon's decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

Few items in the thyroid-related quality of life instrument ThyPRO exhibited differential item functioning.

PubMed

Watt, Torquil; Groenvold, Mogens; Hegedüs, Laszlo; Bonnema, Steen Joop; Rasmussen, Åse Krogh; Feldt-Rasmussen, Ulla; Bjorner, Jakob Bue

2014-02-01

To evaluate the extent of differential item functioning (DIF) within the thyroid-specific quality of life patient-reported outcome measure, ThyPRO, according to sex, age, education and thyroid diagnosis. A total of 838 patients with benign thyroid diseases completed the ThyPRO questionnaire (84 five-point items, 13 scales). Uniform and nonuniform DIF were investigated using ordinal logistic regression, testing for both statistical significance and magnitude (∆R(2) > 0.02). Scale level was estimated by the sum score, after purification. Twenty instances of DIF in 17 of the 84 items were found. Eight according to diagnosis, where the goiter scale was the one most affected, possibly due to differing perceptions in patients with auto-immune thyroid diseases compared to patients with simple goiter. Eight DIFs according to age were found, of which 5 were in positively worded items, which younger patients were more likely to endorse; one according to gender: women were more likely to report crying, and three according to educational level. The vast majority of DIF had only minor influence on the scale scores (0.1-2.3 points on the 0-100 scales), but two DIF corresponded to a difference of 4.6 and 9.8, respectively. Ordinal logistic regression identified DIF in 17 of 84 items. The potential impact of this on the present scales was low, but items displaying DIF could be avoided when developing abbreviated scales, where the potential impact of DIF (due to fewer items) will be larger.

Clinical Utility of Cancellation on the WISC-IV

ERIC Educational Resources Information Center

Zhu, Jianjun; Chen, Hsinyi

2013-01-01

This study examined empirical evidence for clinical utility of the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV) cancellation subtest by comparing data from 597 clinical and 597 matched control children. The results of dependent t and sequential logistic regression analyses demonstrated that (a) children with intellectual…

Factors associated with fall-related fractures in Parkinson's disease.

PubMed

Cheng, Kuei-Yueh; Lin, Wei-Che; Chang, Wen-Neng; Lin, Tzu-Kong; Tsai, Nai-Wen; Huang, Chih-Cheng; Wang, Hung-Chen; Huang, Yung-Cheng; Chang, Hsueh-Wen; Lin, Yu-Jun; Lee, Lian-Hui; Cheng, Ben-Chung; Kung, Chia-Te; Chang, Ya-Ting; Su, Chih-Min; Chiang, Yi-Fang; Su, Yu-Jih; Lu, Cheng-Hsien

2014-01-01

Fall-related fracture is one of the most disabling features of idiopathic Parkinson's disease (PD). A better understanding of the associated factors is needed to predict PD patients who will require treatment. This prospective study enrolled 100 adult idiopathic PD patients. Stepwise logistic regressions were used to evaluate the relationships between clinical factors and fall-related fracture. Falls occurred in 56 PD patients, including 32 with fall-related fractures. The rate of falls in the study period was 2.2 ± 1.4 per 18 months. The percentage of osteoporosis was 34% (19/56) and 11% in PD patients with and without falls, respectively. Risk factors associated with fall-related fracture were sex, underlying knee osteoarthritis, mean Unified Parkinson's Disease Rating Scale score, mean Morse fall scale, mean Hoehn and Yahr stage, and exercise habit. By stepwise logistic regression, sex and mean Morse fall scale were independently associated with fall-related fracture. Females had an odds ratio of 3.8 compared to males and the cut-off value of the Morse fall scale for predicting fall-related fracture was 72.5 (sensitivity 72% and specificity 70%). Higher mean Morse fall scales (>72.5) and female sex are associated with higher risk of fall-related fractures. Preventing falls in the high-risk PD group is an important safety issue and highly relevant for their quality of life. Copyright © 2013 Elsevier Ltd. All rights reserved.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

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.

Propensity score estimation: machine learning and classification methods as alternatives to logistic regression

PubMed Central

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-01-01

Summary Objective Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy. Study Design and Setting We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. Results We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (CART), and meta-classifiers (in particular, boosting). Conclusion While the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and to a lesser extent decision trees (particularly CART) appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. PMID:20630332

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Fungible weights in logistic regression.

PubMed

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Should metacognition be measured by logistic regression?

PubMed

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

Addressing data privacy in matched studies via virtual pooling.

PubMed

Saha-Chaudhuri, P; Weinberg, C R

2017-09-07

Data confidentiality and shared use of research data are two desirable but sometimes conflicting goals in research with multi-center studies and distributed data. While ideal for straightforward analysis, confidentiality restrictions forbid creation of a single dataset that includes covariate information of all participants. Current approaches such as aggregate data sharing, distributed regression, meta-analysis and score-based methods can have important limitations. We propose a novel application of an existing epidemiologic tool, specimen pooling, to enable confidentiality-preserving analysis of data arising from a matched case-control, multi-center design. Instead of pooling specimens prior to assay, we apply the methodology to virtually pool (aggregate) covariates within nodes. Such virtual pooling retains most of the information used in an analysis with individual data and since individual participant data is not shared externally, within-node virtual pooling preserves data confidentiality. We show that aggregated covariate levels can be used in a conditional logistic regression model to estimate individual-level odds ratios of interest. The parameter estimates from the standard conditional logistic regression are compared to the estimates based on a conditional logistic regression model with aggregated data. The parameter estimates are shown to be similar to those without pooling and to have comparable standard errors and confidence interval coverage. Virtual data pooling can be used to maintain confidentiality of data from multi-center study and can be particularly useful in research with large-scale distributed data.

The Outlier Detection for Ordinal Data Using Scalling Technique of Regression Coefficients

NASA Astrophysics Data System (ADS)

Adnan, Arisman; Sugiarto, Sigit

2017-06-01

The aims of this study is to detect the outliers by using coefficients of Ordinal Logistic Regression (OLR) for the case of k category responses where the score from 1 (the best) to 8 (the worst). We detect them by using the sum of moduli of the ordinal regression coefficients calculated by jackknife technique. This technique is improved by scalling the regression coefficients to their means. R language has been used on a set of ordinal data from reference distribution. Furthermore, we compare this approach by using studentised residual plots of jackknife technique for ANOVA (Analysis of Variance) and OLR. This study shows that the jackknifing technique along with the proper scaling may lead us to reveal outliers in ordinal regression reasonably well.

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Intergenerational Transmission of Abuse of Incarcerated Fathers: A Study of the Measurement of Abuse

ERIC Educational Resources Information Center

Ball, Jeremy D.

2009-01-01

Research on the intergenerational transmission of abuse hypothesis often only examined the "existence" of abuse. The current study utilizes retrospective recalls of incarcerated male defendants (N = 414), using questions formulated from the modified Conflict Tactics Scales. Five logistic regression models are run, representing a different physical…

An Investigation of the Variables Predicting Faculty of Education Students' Speaking Anxiety through Ordinal Logistic Regression Analysis

ERIC Educational Resources Information Center

Bozpolat, Ebru

2017-01-01

The purpose of this study is to determine whether Cumhuriyet University Faculty of Education students' levels of speaking anxiety are predicted by the variables of gender, department, grade, such sub-dimensions of "Speaking Self-Efficacy Scale for Pre-Service Teachers" as "public speaking," "effective speaking,"…

Emotional Self-Efficacy and Alcohol and Tobacco Use in Adolescents

ERIC Educational Resources Information Center

Zullig, Keith J.; Teoli, Dac A.; Valois, Robert F.

2014-01-01

This study examined relationships between emotional self-efficacy (ESE) and alcohol and tobacco use in a statewide sample of public high school adolescents (n?=?2,566). The Center for Disease Control Youth Risk Behavior Survey and an adolescent ESE scale were utilized. Logistic regression analyses indicated the presence of any significant race by…

Analyzing Whitebark Pine Distribution in the Northern Rocky Mountains in Support of Grizzly Bear Recovery

NASA Astrophysics Data System (ADS)

Lawrence, R.; Landenburger, L.; Jewett, J.

2007-12-01

Whitebark pine seeds have long been identified as the most significant vegetative food source for grizzly bears in the Greater Yellowstone Ecosystem (GYE) and, hence, a crucial element of suitable grizzly bear habitat. The overall health and status of whitebark pine in the GYE is currently threatened by mountain pine beetle infestations and the spread of whitepine blister rust. Whitebark pine distribution (presence/absence) was mapped for the GYE using Landsat 7 Enhanced Thematic Mapper (ETM+) imagery and topographic data as part of a long-term inter-agency monitoring program. Logistic regression was compared with classification tree analysis (CTA) with and without boosting. Overall comparative classification accuracies for the central portion of the GYE covering three ETM+ images along a single path ranged from 91.6% using logistic regression to 95.8% with See5's CTA algorithm with the maximum 99 boosts. The analysis is being extended to the entire northern Rocky Mountain Ecosystem and extended over decadal time scales. The analysis is being extended to the entire northern Rocky Mountain Ecosystem and extended over decadal time scales.

Calibration power of the Braden scale in predicting pressure ulcer development.

PubMed

Chen, Hong-Lin; Cao, Ying-Juan; Wang, Jing; Huai, Bao-Sha

2016-11-02

Calibration is the degree of correspondence between the estimated probability produced by a model and the actual observed probability. The aim of this study was to investigate the calibration power of the Braden scale in predicting pressure ulcer development (PU). A retrospective analysis was performed among consecutive patients in 2013. The patients were separated into training a group and a validation group. The predicted incidence was calculated using a logistic regression model in the training group and the Hosmer-Lemeshow test was used for assessing the goodness of fit. In the validation cohort, the observed and the predicted incidence were compared by the Chi-square (χ 2 ) goodness of fit test for calibration power. We included 2585 patients in the study, of these 78 patients (3.0%) developed a PU. Between the training and validation groups the patient characteristics were non-significant (p>0.05). In the training group, the logistic regression model for predicting pressure ulcer was Logit(P) = -0.433*Braden score+2.616. The Hosmer-Lemeshow test showed no goodness fit (χ 2 =13.472; p=0.019). In the validation group, the predicted pressure ulcer incidence also did not fit well with the observed incidence (χ 2 =42.154, p=0.000 by Braden scores; and χ 2 =17.223, p=0.001 by Braden scale risk classification). The Braden scale has low calibration power in predicting PU formation.

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

EPA Science Inventory

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

Predicting U.S. Army Reserve Unit Manning Using Market Demographics

DTIC Science & Technology

2015-06-01

develops linear regression , classification tree, and logistic regression models to determine the ability of the location to support manning requirements... logistic regression model delivers predictive results that allow decision-makers to identify locations with a high probability of meeting unit...manning requirements. The recommendation of this thesis is that the USAR implement the logistic regression model. 14. SUBJECT TERMS U.S

Analyzing Student Learning Outcomes: Usefulness of Logistic and Cox Regression Models. IR Applications, Volume 5

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

PubMed

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

PubMed Central

Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

2013-01-01

Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

[Prevalence and risk factors of postpartum depression in Tianhe District of Guangzhou].

PubMed

Deng, Aiwen; Jiang, Tingting; Luo, Yingping; Xiong, Ribo

2014-01-01

To investigate the prevalence and risk factors of postpartum depression (PPD) in Tianhe district of Guangzhou. A total of 1428 postpartum women in 3 hospitals in Tianhe District of Guangzhou were screened with Edinburg Postnatal Depression Scale (EPDS), Social Support Rating Scale (SSRS) and a self-designed questionnaire of PPD-related factors during the period from May to September, 2013. The prevalence of PPD was 20.03% in these women. Unconditional logistic regression analysis showed a significant correlation of PPD with education, delivery mode, only daughter, relationship between mother-in-law and daughter-in-law, newborn gender satisfaction and housing condition (P<0.05). Multivariate logistic regression analysis identified education, delivery mode, only daughter, relationship between mother-in-law and daughter-in-law, and newborn gender satisfaction as the risk factors for PPD, and housing condition was negatively correlated with the incidence of PPD with an OR value of 0.900. Compared with healthy postpartum women, the patients with PPD exhibited significantly reduced total score of social support rating scale, score of objective support, score of subjective support, and social utilization degree. The prevalence of PPD is high in Tianhe District of Guangzhou, and health education and psychosocial intervention should be offered to prevent PPD.

Logistic Regression: Concept and Application

ERIC Educational Resources Information Center

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

PubMed Central

Weiss, Brandi A.; Dardick, William

2015-01-01

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories. PMID:29795897

Logistic regression applied to natural hazards: rare event logistic regression with replications

NASA Astrophysics Data System (ADS)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Large unbalanced credit scoring using Lasso-logistic regression ensemble.

PubMed

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression.

PubMed

Weiss, Brandi A; Dardick, William

2016-12-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data-model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data-model fit to assess how well logistic regression models classify cases into observed categories.

Mental Health Status, Drug Treatment Use, and Needle Sharing among Injection Drug Users

ERIC Educational Resources Information Center

Lundgren, Lena M.; Amodeo, Maryann; Chassler, Deborah

2005-01-01

This study examined the relationship among mental health symptoms, drug treatment use, and needle sharing in a sample of 507 injection drug users (IDUs). Mental health symptoms were measured through the ASI psychiatric scale. A logistic regression model identified that some of the ASI items were associated with needle sharing in an opposing…

Modeling ozone bioindicator injury with microscale and landscape-scale explanatory variables: A logistic regression approach

Treesearch

John W. Coulston

2011-01-01

Tropospheric ozone occurs at phytotoxic levels in the United States (Lefohn and Pinkerton 1988). Several plant species, including commercially important timber species, are sensitive to elevated ozone levels. Exposure to elevated ozone can cause growth reduction and foliar injury and make trees more susceptible to secondary stressors such as insects and pathogens (...

MIS Score: Prediction Model for Minimally Invasive Surgery.

PubMed

Hu, Yuanyuan; Cao, Jingwei; Hou, Xianzeng; Liu, Guangcun

2017-03-01

Reports suggest that patients with spontaneous intracerebral hemorrhage (ICH) can benefit from minimally invasive surgery, but the inclusion criterion for operation is controversial. This article analyzes factors affecting the 30-day prognoses of patients who have received minimally invasive surgery and proposes a simple grading scale that represents clinical operation effectiveness. The records of 101 patients with spontaneous ICH presenting to Qianfoshan Hospital were reviewed. Factors affecting their 30-day prognosis were identified by logistic regression. A clinical grading scale, the MIS score, was developed by weighting the independent predictors based on these factors. Univariate analysis revealed that the factors that affect 30-day prognosis include Glasgow coma scale score (P < 0.01), age ≥80 years (P < 0.05), blood glucose (P < 0.01), ICH volume (P < 0.01), operation time (P < 0.05), and presence of intraventricular hemorrhage (P < 0.001). Logistic regression revealed that the factors that affect 30-day prognosis include Glasgow coma scale score (P < 0.05), age (P < 0.05), ICH volume (P < 0.01), and presence of intraventricular hemorrhage (P < 0.05). The MIS score was developed accordingly; 39 patients with 0-1 MIS scores had favorable prognoses, whereas only 9 patients with 2-5 MIS scores had poor prognoses. The MIS score is a simple grading scale that can be used to select patients who are suited for minimal invasive drainage surgery. When MIS score is 0-1, minimal invasive surgery is strongly recommended for patients with spontaneous cerebral hemorrhage. The scale merits further prospective studies to fully determine its efficacy. Copyright © 2016 Elsevier Inc. All rights reserved.

Dental health services utilization and associated factors in children 6 to 12 years old in a low-income country.

PubMed

Medina-Solis, Carlo Eduardo; Maupomé, Gerardo; del Socorro, Herrera Miriam; Pérez-Núñez, Ricardo; Avila-Burgos, Leticia; Lamadrid-Figueroa, Hector

2008-01-01

To determine the factors associated with the dental health services utilization among children ages 6 to 12 in León, Nicaragua. A cross-sectional study was carried out in 1,400 schoolchildren. Using a questionnaire, we determined information related to utilization and independent variables in the previous year. Oral health needs were established by means of a dental examination. To identify the independent variables associated with dental health services utilization, two types of multivariate regression models were used, according to the measurement scale of the outcome variable: a) frequency of utilization as (0) none, (1) one, and (2) two or more, analyzed with the ordered logistic regression and b) the type of service utilized as (0) none, (1) preventive services, (2) curative services, and (3) both services, analyzed with the multinomial logistic regression. The proportion of children who received at least one dental service in the 12 months prior to the study was 27.7 percent. The variables associated with utilization in the two models were older age, female sex, more frequent toothbrushing, positive attitude of the mother toward the child's oral health, higher socioeconomic level, and higher oral health needs. Various predisposing, enabling, and oral health needs variables were associated with higher dental health services utilization. As in prior reports elsewhere, these results from Nicaragua confirmed that utilization inequalities exist between socioeconomic groups. The multinomial logistic regression model evidenced the association of different variables depending on the type of service used.

Mapping Shallow Landslide Slope Inestability at Large Scales Using Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Avalon Cullen, C.; Kashuk, S.; Temimi, M.; Suhili, R.; Khanbilvardi, R.

2015-12-01

Rainfall induced landslides are one of the most frequent hazards on slanted terrains. They lead to great economic losses and fatalities worldwide. Most factors inducing shallow landslides are local and can only be mapped with high levels of uncertainty at larger scales. This work presents an attempt to determine slope instability at large scales. Buffer and threshold techniques are used to downscale areas and minimize uncertainties. Four static parameters (slope angle, soil type, land cover and elevation) for 261 shallow rainfall-induced landslides in the continental United States are examined. ASTER GDEM is used as bases for topographical characterization of slope and buffer analysis. Slope angle threshold assessment at the 50, 75, 95, 98, and 99 percentiles is tested locally. Further analysis of each threshold in relation to other parameters is investigated in a logistic regression environment for the continental U.S. It is determined that lower than 95-percentile thresholds under-estimate slope angles. Best regression fit can be achieved when utilizing the 99-threshold slope angle. This model predicts the highest number of cases correctly at 87.0% accuracy. A one-unit rise in the 99-threshold range increases landslide likelihood by 11.8%. The logistic regression model is carried over to ArcGIS where all variables are processed based on their corresponding coefficients. A regional slope instability map for the continental United States is created and analyzed against the available landslide records and their spatial distributions. It is expected that future inclusion of dynamic parameters like precipitation and other proxies like soil moisture into the model will further improve accuracy.

Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

ERIC Educational Resources Information Center

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…

A Methodology for Generating Placement Rules that Utilizes Logistic Regression

ERIC Educational Resources Information Center

Wurtz, Keith

2008-01-01

The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Treesearch

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

PubMed Central

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data. PMID:25706988

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

ERIC Educational Resources Information Center

Weiss, Brandi A.; Dardick, William

2016-01-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify…

What Are the Odds of that? A Primer on Understanding Logistic Regression

ERIC Educational Resources Information Center

Huang, Francis L.; Moon, Tonya R.

2013-01-01

The purpose of this Methodological Brief is to present a brief primer on logistic regression, a commonly used technique when modeling dichotomous outcomes. Using data from the National Education Longitudinal Study of 1988 (NELS:88), logistic regression techniques were used to investigate student-level variables in eighth grade (i.e., enrolled in a…

On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

ERIC Educational Resources Information Center

Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas

2011-01-01

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…

Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.

PubMed

Hinton, Devon E; Chhean, Dara; Pich, Vuth; Um, Khin; Fama, Jeanne M; Pollack, Mark H

2006-01-01

Consecutive Cambodian refugees attending a psychiatric clinic were assessed for the presence and severity of current--i.e., at least one episode in the last month--neck-focused panic. Among the whole sample (N=130), in a logistic regression analysis, the Anxiety Sensitivity Index (ASI; odds ratio=3.70) and the Clinician-Administered PTSD Scale (CAPS; odds ratio=2.61) significantly predicted the presence of current neck panic (NP). Among the neck panic patients (N=60), in the linear regression analysis, NP severity was significantly predicted by NP-associated flashbacks (beta=.42), NP-associated catastrophic cognitions (beta=.22), and CAPS score (beta=.28). Further analysis revealed the effect of the CAPS score to be significantly mediated (Sobel test [Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182]) by both NP-associated flashbacks and catastrophic cognitions. In the care of traumatized Cambodian refugees, NP severity, as well as NP-associated flashbacks and catastrophic cognitions, should be specifically assessed and treated.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

Analyzing the Administration Perception of the Teachers by Means of Logistic Regression According to Values

ERIC Educational Resources Information Center

Ugurlu, Celal Teyyar

2017-01-01

This study aims to analyze the administration perception of the teachers according to values in line with certain parameters. The model of the research is relational screening model. The population is applied to 470 teachers who work in 25 secondary schools at the center of Sivas with scales. 317 questionnaires which had been returned have been…

Landscape evaluation of female black bear habitat effectiveness and capability in the North Cascades, Washington.

Treesearch

William L. Gaines; Andrea L. Lyons; John F. Lehmkuhl; Kenneth J. Raedeke

2005-01-01

We used logistic regression to derive scaled resource selection functions (RSFs) for female black bears at two study areas in the North Cascades Mountains. We tested the hypothesis that the influence of roads would result in potential habitat effectiveness (RSFs without the influence of roads) being greater than realized habitat effectiveness (RSFs with roads). Roads...

Understanding the Gap between Cognitive Abilities and Daily Living Skills in Adolescents with Autism Spectrum Disorders with Average Intelligence

ERIC Educational Resources Information Center

Duncan, Amie W.; Bishop, Somer L.

2015-01-01

Daily living skills standard scores on the Vineland Adaptive Behavior Scales-2nd edition were examined in 417 adolescents from the Simons Simplex Collection. All participants had at least average intelligence and a diagnosis of autism spectrum disorder. Descriptive statistics and binary logistic regressions were used to examine the prevalence and…

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

PubMed

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

2014-06-17

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

Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards: Smoke Detection Using an Autologistic Regression Classifier.

PubMed

Wolters, Mark A; Dean, C B

2017-01-01

Remote sensing images from Earth-orbiting satellites are a potentially rich data source for monitoring and cataloguing atmospheric health hazards that cover large geographic regions. A method is proposed for classifying such images into hazard and nonhazard regions using the autologistic regression model, which may be viewed as a spatial extension of logistic regression. The method includes a novel and simple approach to parameter estimation that makes it well suited to handling the large and high-dimensional datasets arising from satellite-borne instruments. The methodology is demonstrated on both simulated images and a real application to the identification of forest fire smoke.

A three-item scale for the early prediction of stroke recovery.

PubMed

Baird, A E; Dambrosia, J; Janket, S; Eichbaum, Q; Chaves, C; Silver, B; Barber, P A; Parsons, M; Darby, D; Davis, S; Caplan, L R; Edelman, R E; Warach, S

2001-06-30

Accurate assessment of prognosis in the first hours of stroke is desirable for best patient management. We aimed to assess whether the extent of ischaemic brain injury on magnetic reasonance diffusion-weighted imaging (MR DWI) could provide additional prognostic information to clinical factors. In a three-phase study we studied 66 patients from a North American teaching hospital who had: MR DWI within 36 h of stroke onset; the National Institutes of Health Stroke Scale (NIHSS) score measured at the time of scanning; and the Barthel Index measured no later than 3 months after stroke. We used logistic regression to derive a predictive model for good recovery. This logistic regression model was applied to an independent series of 63 patients from an Australian teaching hospital, and we then developed a three-item scale for the early prediction of stroke recovery. Combined measurements of the NIHSS score (p=0.01), time in hours from stroke onset to MR DWI (p=0.02), and the volume of ischaemic brain tissue on MR DWI (p=0.04) gave the best prediction of stroke recovery. The model was externally validated on the Australian sample with 0.77 sensitivity and 0.88 specificity. Three likelihood levels for stroke recovery-low (0-2), medium (3-4), and high (5-7)-were identified on the three-item scale. The combination of clinical and MR DWI factors provided better prediction of stroke recovery than any factor alone, shortly after admission to hospital. This information was incorporated into a three-item scale for clinical use.

Logistic regression for risk factor modelling in stuttering research.

PubMed

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. 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.

Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

DTIC Science & Technology

2015-03-19

learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but much more...classifier, Generative learning, Discriminative learning, Naïve Bayes, Feature selection, Logistic regression , higher order attribute independence 16...discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but

A review of logistic regression models used to predict post-fire tree mortality of western North American conifers

Treesearch

Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen Fitzgerald

2012-01-01

Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...

Preserving Institutional Privacy in Distributed binary Logistic Regression.

PubMed

Wu, Yuan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

Privacy is becoming a major concern when sharing biomedical data across institutions. Although methods for protecting privacy of individual patients have been proposed, it is not clear how to protect the institutional privacy, which is many times a critical concern of data custodians. Built upon our previous work, Grid Binary LOgistic REgression (GLORE)1, we developed an Institutional Privacy-preserving Distributed binary Logistic Regression model (IPDLR) that considers both individual and institutional privacy for building a logistic regression model in a distributed manner. We tested our method using both simulated and clinical data, showing how it is possible to protect the privacy of individuals and of institutions using a distributed strategy.

Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

PubMed Central

Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.

2014-01-01

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438

Differentially private distributed logistic regression using private and public data.

PubMed

Ji, Zhanglong; Jiang, Xiaoqian; Wang, Shuang; Xiong, Li; Ohno-Machado, Lucila

2014-01-01

Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules

PubMed Central

Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules’ 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively. PMID:29228030

Logistic regression analysis of conventional ultrasonography, strain elastosonography, and contrast-enhanced ultrasound characteristics for the differentiation of benign and malignant thyroid nodules.

PubMed

Pang, Tiantian; Huang, Leidan; Deng, Yingyuan; Wang, Tianfu; Chen, Siping; Gong, Xuehao; Liu, Weixiang

2017-01-01

The aim of the study is to screen the significant sonographic features by logistic regression analysis and fit a model to diagnose thyroid nodules. A total of 525 pathological thyroid nodules were retrospectively analyzed. All the nodules underwent conventional ultrasonography (US), strain elastosonography (SE), and contrast -enhanced ultrasound (CEUS). Those nodules' 12 suspicious sonographic features were used to assess thyroid nodules. The significant features of diagnosing thyroid nodules were picked out by logistic regression analysis. All variables that were statistically related to diagnosis of thyroid nodules, at a level of p < 0.05 were embodied in a logistic regression analysis model. The significant features in the logistic regression model of diagnosing thyroid nodules were calcification, suspected cervical lymph node metastasis, hypoenhancement pattern, margin, shape, vascularity, posterior acoustic, echogenicity, and elastography score. According to the results of logistic regression analysis, the formula that could predict whether or not thyroid nodules are malignant was established. The area under the receiver operating curve (ROC) was 0.930 and the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 83.77%, 89.56%, 87.05%, 86.04%, and 87.79% respectively.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

PubMed

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p < 0.05). Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.

Negative Life Events and Antenatal Depression among Pregnant Women in Rural China: The Role of Negative Automatic Thoughts.

PubMed

Wang, Yang; Wang, Xiaohua; Liu, Fangnan; Jiang, Xiaoning; Xiao, Yun; Dong, Xuehan; Kong, Xianglei; Yang, Xuemei; Tian, Donghua; Qu, Zhiyong

2016-01-01

Few studies have looked at the relationship between psychological and the mental health status of pregnant women in rural China. The current study aims to explore the potential mediating effect of negative automatic thoughts between negative life events and antenatal depression. Data were collected in June 2012 and October 2012. 495 rural pregnant women were interviewed. Depressive symptoms were measured by the Edinburgh postnatal depression scale, stresses of pregnancy were measured by the pregnancy pressure scale, negative automatic thoughts were measured by the automatic thoughts questionnaire, and negative life events were measured by the life events scale for pregnant women. We used logistic regression and path analysis to test the mediating effect. The prevalence of antenatal depression was 13.7%. In the logistic regression, the only socio-demographic and health behavior factor significantly related to antenatal depression was sleep quality. Negative life events were not associated with depression in the fully adjusted model. Path analysis showed that the eventual direct and general effects of negative automatic thoughts were 0.39 and 0.51, which were larger than the effects of negative life events. This study suggested that there was a potentially significant mediating effect of negative automatic thoughts. Pregnant women who had lower scores of negative automatic thoughts were more likely to suffer less from negative life events which might lead to antenatal depression.

Minimal intervention dentistry for early childhood caries and child dental anxiety: a randomized controlled trial.

PubMed

Arrow, P; Klobas, E

2017-06-01

To compare changes in child dental anxiety after treatment for early childhood caries (ECC) using two treatment approaches. Children with ECC were randomized to test (atraumatic restorative treatment (ART)-based approach) or control (standard care approach) groups. Children aged 3 years or older completed a dental anxiety scale at baseline and follow up. Changes in child dental anxiety from baseline to follow up were tested using the chi-squared statistic, Wilcoxon rank sum test, McNemar's test and multinomial logistic regression. Two hundred and fifty-four children were randomized (N = 127 test, N = 127 control). At baseline, 193 children completed the dental anxiety scale, 211 at follow up and 170 completed the scale on both occasions. Children who were anxious at baseline (11%) were no longer anxious at follow up, and 11% non-anxious children became anxious. Multinomial logistic regression found each increment in the number of visits increased the odds of worsening dental anxiety (odds ratio (OR), 2.2; P < 0.05), whereas each increment in the number of treatments lowered the odds of worsening anxiety (OR, 0.50; P = 0.05). The ART-based approach to managing ECC resulted in similar levels of dental anxiety to the standard treatment approach and provides a valuable alternative approach to the management of ECC in a primary dental care setting. © 2016 Australian Dental Association.

Neuroimaging Characteristics of Small-Vessel Disease in Older Adults with Normal Cognition, Mild Cognitive Impairment, and Alzheimer Disease.

PubMed

Mimenza-Alvarado, Alberto; Aguilar-Navarro, Sara G; Yeverino-Castro, Sara; Mendoza-Franco, César; Ávila-Funes, José Alberto; Román, Gustavo C

2018-01-01

Cerebral small-vessel disease (SVD) represents the most frequent type of vascular brain lesions, often coexisting with Alzheimer disease (AD). By quantifying white matter hyperintensities (WMH) and hippocampal and parietal atrophy, we aimed to describe the prevalence and severity of SVD among older adults with normal cognition (NC), mild cognitive impairment (MCI), and probable AD and to describe associated risk factors. This study included 105 older adults evaluated with magnetic resonance imaging and clinical and neuropsychological tests. We used the Fazekas scale (FS) for quantification of WMH, the Scheltens scale (SS) for hippocampal atrophy, and the Koedam scale (KS) for parietal atrophy. Logistic regression models were performed to determine the association between FS, SS, and KS scores and the presence of NC, MCI, or probable AD. Compared to NC subjects, SVD was more prevalent in MCI and probable AD subjects. After adjusting for confounding factors, logistic regression showed a positive association between higher scores on the FS and probable AD (OR = 7.6, 95% CI 2.7-20, p < 0.001). With the use of the SS and KS (OR = 4.5, 95% CI 3.5-58, p = 0.003 and OR = 8.9, 95% CI 1-72, p = 0.04, respectively), the risk also remained significant for probable AD. These results suggest an association between severity of vascular brain lesions and neurodegeneration.

Data-Science Analysis of the Macro-scale Features Governing the Corrosion to Crack Transition in AA7050-T7451

NASA Astrophysics Data System (ADS)

Co, Noelle Easter C.; Brown, Donald E.; Burns, James T.

2018-05-01

This study applies data science approaches (random forest and logistic regression) to determine the extent to which macro-scale corrosion damage features govern the crack formation behavior in AA7050-T7451. Each corrosion morphology has a set of corresponding predictor variables (pit depth, volume, area, diameter, pit density, total fissure length, surface roughness metrics, etc.) describing the shape of the corrosion damage. The values of the predictor variables are obtained from white light interferometry, x-ray tomography, and scanning electron microscope imaging of the corrosion damage. A permutation test is employed to assess the significance of the logistic and random forest model predictions. Results indicate minimal relationship between the macro-scale corrosion feature predictor variables and fatigue crack initiation. These findings suggest that the macro-scale corrosion features and their interactions do not solely govern the crack formation behavior. While these results do not imply that the macro-features have no impact, they do suggest that additional parameters must be considered to rigorously inform the crack formation location.

Logistic regression for dichotomized counts.

PubMed

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

Presence and absence of bats across habitat scales in the Upper Coastal Plain of South Carolina

Treesearch

W. Mark Ford; Jennifer M. Menzel; Michael A. Menzel; John W. Edwards; John C. Kilgo

2006-01-01

During 2001, we used active acoustical sampling (Anabat II) to survey foraging habitat relationships of bats on the Savannah River Site (SRS) in the upper Coastal Plain of South Carolina. Using an a priori information-theoretic approach, we conducted logistic regression analysis to examine presence of individual bat species relative to a suite of microhabitat, stand,...

Research and absence of bats across habitat scales in the upper coastal plain of South Carolina

Treesearch

W. Mark Ford; Jennifer M. Menzel; Michael A. Menzel; John W. Edwards; John C. Kilgo

2006-01-01

During 2001, we used active acoustical sampling (Anabat 11) to survey foraging habitat relationships of bats on the Savannah River Site (SRS) in the upper Coastal Plain of South Carolina. Using an a priori information-theoretic approach, we conducted logistic regression analysis to examine presence of individual bat species relative to a suite of microhabitat, stand,...

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.

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L

2017-02-06

Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.

Explicit criteria for prioritization of cataract surgery

PubMed Central

Ma Quintana, José; Escobar, Antonio; Bilbao, Amaia

2006-01-01

Background Consensus techniques have been used previously to create explicit criteria to prioritize cataract extraction; however, the appropriateness of the intervention was not included explicitly in previous studies. We developed a prioritization tool for cataract extraction according to the RAND method. Methods Criteria were developed using a modified Delphi panel judgment process. A panel of 11 ophthalmologists was assembled. Ratings were analyzed regarding the level of agreement among panelists. We studied the effect of all variables on the final panel score using general linear and logistic regression models. Priority scoring systems were developed by means of optimal scaling and general linear models. The explicit criteria developed were summarized by means of regression tree analysis. Results Eight variables were considered to create the indications. Of the 310 indications that the panel evaluated, 22.6% were considered high priority, 52.3% intermediate priority, and 25.2% low priority. Agreement was reached for 31.9% of the indications and disagreement for 0.3%. Logistic regression and general linear models showed that the preoperative visual acuity of the cataractous eye, visual function, and anticipated visual acuity postoperatively were the most influential variables. Alternative and simple scoring systems were obtained by optimal scaling and general linear models where the previous variables were also the most important. The decision tree also shows the importance of the previous variables and the appropriateness of the intervention. Conclusion Our results showed acceptable validity as an evaluation and management tool for prioritizing cataract extraction. It also provides easy algorithms for use in clinical practice. PMID:16512893

Differentially private distributed logistic regression using private and public data

PubMed Central

2014-01-01

Background Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. Methodology In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. Experiments and results We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Conclusion Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee. PMID:25079786

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Performance and strategy comparisons of human listeners and logistic regression in discriminating underwater targets.

PubMed

Yang, Lixue; Chen, Kean

2015-11-01

To improve the design of underwater target recognition systems based on auditory perception, this study compared human listeners with automatic classifiers. Performances measures and strategies in three discrimination experiments, including discriminations between man-made and natural targets, between ships and submarines, and among three types of ships, were used. In the experiments, the subjects were asked to assign a score to each sound based on how confident they were about the category to which it belonged, and logistic regression, which represents linear discriminative models, also completed three similar tasks by utilizing many auditory features. The results indicated that the performances of logistic regression improved as the ratio between inter- and intra-class differences became larger, whereas the performances of the human subjects were limited by their unfamiliarity with the targets. Logistic regression performed better than the human subjects in all tasks but the discrimination between man-made and natural targets, and the strategies employed by excellent human subjects were similar to that of logistic regression. Logistic regression and several human subjects demonstrated similar performances when discriminating man-made and natural targets, but in this case, their strategies were not similar. An appropriate fusion of their strategies led to further improvement in recognition accuracy.

Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

NASA Astrophysics Data System (ADS)

Mei, Zhixiong; Wu, Hao; Li, Shiyun

2018-06-01

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

PubMed Central

Dipnall, Joanna F.

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009–2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. Results After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). Conclusion The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin. PMID:26848571

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

PubMed

Dipnall, Joanna F; Pasco, Julie A; Berk, Michael; Williams, Lana J; Dodd, Seetal; Jacka, Felice N; Meyer, Denny

2016-01-01

Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators. After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001). The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

A logistic regression equation for estimating the probability of a stream in Vermont having intermittent flow

USGS Publications Warehouse

Olson, Scott A.; Brouillette, Michael C.

2006-01-01

A logistic regression equation was developed for estimating the probability of a stream flowing intermittently at unregulated, rural stream sites in Vermont. These determinations can be used for a wide variety of regulatory and planning efforts at the Federal, State, regional, county and town levels, including such applications as assessing fish and wildlife habitats, wetlands classifications, recreational opportunities, water-supply potential, waste-assimilation capacities, and sediment transport. The equation will be used to create a derived product for the Vermont Hydrography Dataset having the streamflow characteristic of 'intermittent' or 'perennial.' The Vermont Hydrography Dataset is Vermont's implementation of the National Hydrography Dataset and was created at a scale of 1:5,000 based on statewide digital orthophotos. The equation was developed by relating field-verified perennial or intermittent status of a stream site during normal summer low-streamflow conditions in the summer of 2005 to selected basin characteristics of naturally flowing streams in Vermont. The database used to develop the equation included 682 stream sites with drainage areas ranging from 0.05 to 5.0 square miles. When the 682 sites were observed, 126 were intermittent (had no flow at the time of the observation) and 556 were perennial (had flowing water at the time of the observation). The results of the logistic regression analysis indicate that the probability of a stream having intermittent flow in Vermont is a function of drainage area, elevation of the site, the ratio of basin relief to basin perimeter, and the areal percentage of well- and moderately well-drained soils in the basin. Using a probability cutpoint (a lower probability indicates the site has perennial flow and a higher probability indicates the site has intermittent flow) of 0.5, the logistic regression equation correctly predicted the perennial or intermittent status of 116 test sites 85 percent of the time.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

Binary logistic regression-Instrument for assessing museum indoor air impact on exhibits.

PubMed

Bucur, Elena; Danet, Andrei Florin; Lehr, Carol Blaziu; Lehr, Elena; Nita-Lazar, Mihai

2017-04-01

This paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The prediction of the impact on the exhibits during certain pollution scenarios (environmental impact) was calculated by a mathematical model based on the binary logistic regression; it allows the identification of those environmental parameters from a multitude of possible parameters with a significant impact on exhibitions and ranks them according to their severity effect. Air quality (NO 2 , SO 2 , O 3 and PM 2.5 ) and microclimate parameters (temperature, humidity) monitoring data from a case study conducted within exhibition and storage spaces of the Romanian National Aviation Museum Bucharest have been used for developing and validating the binary logistic regression method and the mathematical model. The logistic regression analysis was used on 794 data combinations (715 to develop of the model and 79 to validate it) by a Statistical Package for Social Sciences (SPSS 20.0). The results from the binary logistic regression analysis demonstrated that from six parameters taken into consideration, four of them present a significant effect upon exhibits in the following order: O 3 >PM 2.5 >NO 2 >humidity followed at a significant distance by the effects of SO 2 and temperature. The mathematical model, developed in this study, correctly predicted 95.1 % of the cumulated effect of the environmental parameters upon the exhibits. Moreover, this model could also be used in the decisional process regarding the preventive preservation measures that should be implemented within the exhibition space. The paper presents a new way to assess the environmental impact on historical artifacts using binary logistic regression. The mathematical model developed on the environmental parameters analyzed by the binary logistic regression method could be useful in a decision-making process establishing the best measures for pollution reduction and preventive preservation of exhibits.

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

PubMed

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

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.

Self-reported work ability and work performance in workers with chronic nonspecific musculoskeletal pain.

PubMed

de Vries, Haitze J; Reneman, Michiel F; Groothoff, Johan W; Geertzen, Jan H B; Brouwer, Sandra

2013-03-01

To assess self-reported work ability and work performance of workers who stay at work despite chronic nonspecific musculoskeletal pain (CMP), and to explore which variables were associated with these outcomes. In a cross-sectional study we assessed work ability (Work Ability Index, single item scale 0-10) and work performance (Health and Work Performance Questionnaire, scale 0-10) among 119 workers who continued work while having CMP. Scores of work ability and work performance were categorized into excellent (10), good (9), moderate (8) and poor (0-7). Hierarchical multiple regression and logistic regression analysis was used to analyze the relation of socio-demographic, pain-related, personal- and work-related variables with work ability and work performance. Mean work ability and work performance were 7.1 and 7.7 (poor to moderate). Hierarchical multiple regression analysis revealed that higher work ability scores were associated with lower age, better general health perception, and higher pain self-efficacy beliefs (R(2) = 42 %). Higher work performance was associated with lower age, higher pain self-efficacy beliefs, lower physical work demand category and part-time work (R(2) = 37 %). Logistic regression analysis revealed that work ability ≥8 was significantly explained by age (OR = 0.90), general health perception (OR = 1.04) and pain self-efficacy (OR = 1.15). Work performance ≥8 was explained by pain self-efficacy (OR = 1.11). Many workers with CMP who stay at work report poor to moderate work ability and work performance. Our findings suggest that a subgroup of workers with CMP can stay at work with high work ability and performance, especially when they have high beliefs of pain self-efficacy. Our results further show that not the pain itself, but personal and work-related factors relate to work ability and work performance.

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

Statistical analysis of subjective preferences for video enhancement

NASA Astrophysics Data System (ADS)

Woods, Russell L.; Satgunam, PremNandhini; Bronstad, P. Matthew; Peli, Eli

2010-02-01

Measuring preferences for moving video quality is harder than for static images due to the fleeting and variable nature of moving video. Subjective preferences for image quality can be tested by observers indicating their preference for one image over another. Such pairwise comparisons can be analyzed using Thurstone scaling (Farrell, 1999). Thurstone (1927) scaling is widely used in applied psychology, marketing, food tasting and advertising research. Thurstone analysis constructs an arbitrary perceptual scale for the items that are compared (e.g. enhancement levels). However, Thurstone scaling does not determine the statistical significance of the differences between items on that perceptual scale. Recent papers have provided inferential statistical methods that produce an outcome similar to Thurstone scaling (Lipovetsky and Conklin, 2004). Here, we demonstrate that binary logistic regression can analyze preferences for enhanced video.

Mixed conditional logistic regression for habitat selection studies.

PubMed

Duchesne, Thierry; Fortin, Daniel; Courbin, Nicolas

2010-05-01

1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unconditional (standard) and conditional logistic regressions. While the advantage of mixed-effects models is recognized for standard logistic regression, mixed conditional logistic regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed conditional logistic regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional logistic regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects conditional logistic regression should become a valuable tool for ecological research.

An Exploratory Analysis of Work Engagement, Satisfaction, and Depression in Psychiatry Residents.

PubMed

Agarwal, Gaurava; Karpouzian, Tatiana

2016-02-01

This exploratory study aims to measure work engagement levels in psychiatry residents at three psychiatry residency programs using the Utrecht Work Engagement Scale (UWES). In addition, the study investigates the relationship between total engagement and its subscales, resident satisfaction, and a depression screen. Recruitment of 53/79 residents from three psychiatry residency programs in Illinois was completed. The residents were administered a questionnaire consisting of the UWES, the Primary Care Evaluation of Mental Disorders (Prime-MD) depression screen, and a residency satisfaction scale. Statistical analysis using independent samples t test and a one-way analysis of variance was used to assess differences on engagement total score and subscales and satisfaction scale. A logistic regression was used with the engagement subscales and the satisfaction scale as predictors of belonging to the depressed or non-depressed group. Psychiatry residents scored in the high range for total engagement and all its subscales except for vigor which was in the moderate range. Residents who screened positive for depression reported lower total engagement than those who were negative on the depression screen. Vigor was the only significant predictor (p = .004) of being in the depressed group after logistic regression. Total engagement and the subscale of dedication significantly predicted overall residency satisfaction (β = .473, p = .016). Higher total UWES-15 and its subscales of vigor and dedication are correlated with a lower rate of screening positive for depression and higher residency satisfaction. This exploratory study lends support for further study of this psychological construct in medical training programs, but replication is needed.

PubMed Central

GUZZO, A.S.; MEGGIOLARO, A.; MANNOCCI, A.; TECCA, M.; SALOMONE, I.

2015-01-01

Summary Introduction. "Umberto I" Teaching Hospital adopted 'Conley scale' as internal procedure for fall risk assessment, with the aim of strengthening surveillance and improving prevention and management of impatient falls. Materials and methods. Case-control study was performed. Fall events from 1st March 2012 to 30th September 2013 were considered. Cases have been matched for gender, department and period of hospitalization with two or three controls when it is possible. A table including intrinsic and extrinsic 'fall risk' factors, not foreseen by Conley Scale, and setted up after a literature overview was built. Univariate analysis and conditional logistic regression model have been performed. Results. 50 cases and 102 controls were included. Adverse event 'fall' were associated with filled Conley scale at the admission to care unit (OR = 4.92, 95%CI = 2.34-10.37). Univariate analysis identified intrinsic factors increasing risk of falls: dizziness (OR = 3.22; 95%CI = 1.34-7.75), psychomotor agitation (OR = 2.61; 95%CI = 1.06-6.43); and use of means of restraint (OR = 5.05 95%CI = 1.77-14.43). Conditional logistic regression model revealed a significant association with the following variables: use of instruments of restraint (HR = 5.54, 95%CI = 1.2- 23.80), dizziness (OR = 3.97, 95%CI = 1.22-12.89). Discussion. Conley Scale must be filled at the access of patient to care unit. There were no significant differences between cases and controls with regard to risk factors provided by Conley, except for the use of means of restraint. Empowerment strategies for Conley compilation are needed. PMID:26789993

Electrophysiology and optical coherence tomography to evaluate Parkinson disease severity.

PubMed

Garcia-Martin, Elena; Rodriguez-Mena, Diego; Satue, Maria; Almarcegui, Carmen; Dolz, Isabel; Alarcia, Raquel; Seral, Maria; Polo, Vicente; Larrosa, Jose M; Pablo, Luis E

2014-02-04

To evaluate correlations between visual evoked potentials (VEP), pattern electroretinogram (PERG), and macular and retinal nerve fiber layer (RNFL) thickness measured by optical coherence tomography (OCT) and the severity of Parkinson disease (PD). Forty-six PD patients and 33 age and sex-matched healthy controls were enrolled, and underwent VEP, PERG, and OCT measurements of macular and RNFL thicknesses, and evaluation of PD severity using the Hoehn and Yahr scale to measure PD symptom progression, the Schwab and England Activities of Daily Living Scale (SE-ADL) to evaluate patient quality of life (QOL), and disease duration. Logistical regression was performed to analyze which measures, if any, could predict PD symptom progression or effect on QOL. Visual functional parameters (best corrected visual acuity, mean deviation of visual field, PERG positive (P) component at 50 ms -P50- and negative (N) component at 95 ms -N95- component amplitude, and PERG P50 component latency) and structural parameters (OCT measurements of RNFL and retinal thickness) were decreased in PD patients compared with healthy controls. OCT measurements were significantly negatively correlated with the Hoehn and Yahr scale, and significantly positively correlated with the SE-ADL scale. Based on logistical regression analysis, fovea thickness provided by OCT equipment predicted PD severity, and QOL and amplitude of the PERG N95 component predicted a lower SE-ADL score. Patients with greater damage in the RNFL tend to have lower QOL and more severe PD symptoms. Foveal thicknesses and the PERG N95 component provide good biomarkers for predicting QOL and disease severity.

Cross-cultural Study of Understanding of Scale and Measurement: Does the everyday use of US customary units disadvantage US students?

NASA Astrophysics Data System (ADS)

Delgado, Cesar

2013-06-01

Following a sociocultural perspective, this study investigates how students who have grown up using the SI (Système International d'Unités) (metric) or US customary (USC) systems of units for everyday use differ in their knowledge of scale and measurement. Student groups were similar in terms of socioeconomic status, curriculum, native language transparency of number word structure, type of school, and makeup by gender and grade level, while varying by native system of measurement. Their performance on several tasks was compared using binary logistic regression, ordinal logistic regression, and analysis of variance, with gender and grade level as covariates. Participants included 17 USC-native and 89 SI-native students in a school in Mexico, and 31 USC-native students in a school in the Midwestern USA. SI-native students performed at a significantly higher level estimating the length of a metre and a conceptual task (coordinating relative size and absolute size). No statistically significant differences were found on tasks involving factual knowledge about objects or units, scale construction, or estimation of other units. USC-native students in the US school performed at a higher level on smallest known object. These findings suggest that the more transparent SI system better supports conceptual thinking about scale and measurement than the idiosyncratic USC system. Greater emphasis on the SI system and more complete adoption of the SI system for everyday life may improve understanding among US students. Advancing sociocultural theory, systems of units were found to mediate learner's understanding of scale and measurement, much as number words mediate counting and problem solving.

Advanced colorectal neoplasia risk stratification by penalized logistic regression.

PubMed

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

High depressive symptomatology among older community-dwelling Mexican Americans: the impact of immigration.

PubMed

Gerst, Kerstin; Al-Ghatrif, Majd; Beard, Holly A; Samper-Ternent, Rafael; Markides, Kyriakos S

2010-04-01

This analysis explores nativity differences in depressive symptoms among very old (75+) community-dwelling Mexican Americans. Cross-sectional analysis using the fifth wave (2004-2005) of the Hispanic Established Population for the Epidemiological Study of the Elderly (Hispanic EPESE). The sample consisted of 1699 non-institutionalized Mexican American men and women aged 75 years and above. Depressive symptoms were measured by the Center for Epidemiological Studies Depression Scale (CES-D). Logistic regression was used to predict high depressive symptoms (CES-D score 16 or higher) and multinomial logistic regression was used to predict sub-threshold, moderate, and high depressive symptoms. Results showed that elders born in Mexico had higher odds of more depressive symptoms compared to otherwise similar Mexican Americans born in the US. Age of arrival, gender, and other covariates did not modify that risk. The findings suggest that older Mexican American immigrants are at higher risk of depressive symptomatology compared to persons born in the US, which has significant implications for research, policy, and clinical practice.

Individual relocation decisions after tornadoes: a multi-level analysis.

PubMed

Cong, Zhen; Nejat, Ali; Liang, Daan; Pei, Yaolin; Javid, Roxana J

2018-04-01

This study examines how multi-level factors affected individuals' relocation decisions after EF4 and EF5 (Enhanced Fujita Tornado Intensity Scale) tornadoes struck the United States in 2013. A telephone survey was conducted with 536 respondents, including oversampled older adults, one year after these two disaster events. Respondents' addresses were used to associate individual information with block group-level variables recorded by the American Community Survey. Logistic regression revealed that residential damage and homeownership are important predictors of relocation. There was also significant interaction between these two variables, indicating less difference between homeowners and renters at higher damage levels. Homeownership diminished the likelihood of relocation among younger respondents. Random effects logistic regression found that the percentage of homeownership and of higher income households in the community buffered the effect of damage on relocation; the percentage of older adults reduced the likelihood of this group relocating. The findings are assessed from the standpoint of age difference, policy implications, and social capital and vulnerability. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Are math readiness and personality predictive of first-year retention in engineering?

PubMed

Moses, Laurie; Hall, Cathy; Wuensch, Karl; De Urquidi, Karen; Kauffmann, Paul; Swart, William; Duncan, Steve; Dixon, Gene

2011-01-01

On the basis of J. G. Borkowski, L. K. Chan, and N. Muthukrishna's model of academic success (2000), the present authors hypothesized that freshman retention in an engineering program would be related to not only basic aptitude but also affective factors. Participants were 129 college freshmen with engineering as their stated major. Aptitude was measured by SAT verbal and math scores, high school grade-point average (GPA), and an assessment of calculus readiness. Affective factors were assessed by the NEO-Five Factor Inventory (FFI; P. I. Costa & R. R. McCrae, 2007), and the Nowicki-Duke Locus of Control (LOC) scale (S. Nowicki & M. Duke, 1974). A binary logistic regression analysis found that calculus readiness and high school GPA were predictive of retention. Scores on the Neuroticism and Openness subscales from the NEO-FFI and LOC were correlated with retention status, but Openness was the only affective factor with a significant unique effect in the binary logistic regression. Results of the study lend modest support to Borkowski's model.

Comparison of two occurrence risk assessment methods for collapse gully erosion ——A case study in Guangdong province

NASA Astrophysics Data System (ADS)

Sun, K.; Cheng, D. B.; He, J. J.; Zhao, Y. L.

2018-02-01

Collapse gully erosion is a specific type of soil erosion in the red soil region of southern China, and early warning and prevention of the occurrence of collapse gully erosion is very important. Based on the idea of risk assessment, this research, taking Guangdong province as an example, adopt the information acquisition analysis and the logistic regression analysis, to discuss the feasibility for collapse gully erosion risk assessment in regional scale, and compare the applicability of the different risk assessment methods. The results show that in the Guangdong province, the risk degree of collapse gully erosion occurrence is high in northeastern and western area, and relatively low in southwestern and central part. The comparing analysis of the different risk assessment methods on collapse gully also indicated that the risk distribution patterns from the different methods were basically consistent. However, the accuracy of risk map from the information acquisition analysis method was slightly better than that from the logistic regression analysis method.

An Exploratory Factor Analysis of Coping Styles and Relationship to Depression Among a Sample of Homeless Youth.

PubMed

Brown, Samantha M; Begun, Stephanie; Bender, Kimberly; Ferguson, Kristin M; Thompson, Sanna J

2015-10-01

The extent to which measures of coping adequately capture the ways that homeless youth cope with challenges, and the influence these coping styles have on mental health outcomes, is largely absent from the literature. This study tests the factor structure of the Coping Scale using Exploratory Factor Analysis (EFA) and then investigates the relationship between coping styles and depression using hierarchical logistic regression with data from 201 homeless youth. Results of the EFA indicate a 3-factor structure of coping, which includes active, avoidant, and social coping styles. Results of the hierarchical logistic regression show that homeless youth who engage in greater avoidant coping are at increased risk of meeting criteria for major depressive disorder. Findings provide insight into the utility of a preliminary tool for assessing homeless youths' coping styles. Such assessment may identify malleable risk factors that could be addressed by service providers to help prevent mental health problems.

A comparison between Bayes discriminant analysis and logistic regression for prediction of debris flow in southwest Sichuan, China

NASA Astrophysics Data System (ADS)

Xu, Wenbo; Jing, Shaocai; Yu, Wenjuan; Wang, Zhaoxian; Zhang, Guoping; Huang, Jianxi

2013-11-01

In this study, the high risk areas of Sichuan Province with debris flow, Panzhihua and Liangshan Yi Autonomous Prefecture, were taken as the studied areas. By using rainfall and environmental factors as the predictors and based on the different prior probability combinations of debris flows, the prediction of debris flows was compared in the areas with statistical methods: logistic regression (LR) and Bayes discriminant analysis (BDA). The results through the comprehensive analysis show that (a) with the mid-range scale prior probability, the overall predicting accuracy of BDA is higher than those of LR; (b) with equal and extreme prior probabilities, the overall predicting accuracy of LR is higher than those of BDA; (c) the regional predicting models of debris flows with rainfall factors only have worse performance than those introduced environmental factors, and the predicting accuracies of occurrence and nonoccurrence of debris flows have been changed in the opposite direction as the supplemented information.

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

PubMed

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

Association of the FGA and SLC6A4 genes with autistic spectrum disorder in a Korean population.

PubMed

Ro, Myungja; Won, Seongsik; Kang, Hyunjun; Kim, Su-Yeon; Lee, Seung Ku; Nam, Min; Bang, Hee Jung; Yang, Jae Won; Choi, Kyung-Sik; Kim, Su Kang; Chung, Joo-Ho; Kwack, Kyubum

2013-01-01

Autism spectrum disorder (ASD) is a neurobiological disorder characterized by distinctive impairments in cognitive function, language, and behavior. Linkage and population studies suggest a genetic association between solute carrier family 6 member 4 (SLC6A4) variants and ASD. Logistic regression was used to identify associations between single-nucleotide polymorphisms (SNPs) and ASD with 3 alternative models (additive, dominant, and recessive). Linear regression analysis was performed to determine the influence of SNPs on Childhood Autism Rating Scale (CARS) scores as a quantitative phenotype. In the present study, we examined the associations of SNPs in the SLC6A4 gene and the fibrinogen alpha chain (FGA) gene. Logistic regression analysis showed a significant association between the risk of ASD and rs2070025 and rs2070011 in the FGA gene. The gene-gene interaction between SLC6A4 and FGA was not significantly associated with ASD susceptibility. However, polymorphisms in both SLC6A4 and the FGA gene significantly affected the symptoms of ASD. Our findings indicate that FGA and SLC6A4 gene interactions may contribute to the phenotypes of ASD rather than the incidence of ASD. © 2013 S. Karger AG, Basel.

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

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

How Is Health Related to Literacy, Numeracy, and Technological Problem-Solving Skills among U.S. Adults? Evidence from the Program for the International Assessment of Adult Competencies (PIAAC)

ERIC Educational Resources Information Center

Prins, Esther; Monnat, Shannon; Clymer, Carol; Toso, Blaire Wilson

2015-01-01

This paper uses data from the Program for the International Assessment of Adult Competencies (PIAAC) to analyze the relationship between U.S. adults' self-reported health and proficiencies in literacy, numeracy, and technological problem solving. Ordinal logistic regression analyses showed that scores on all three scales were positively and…

Lack of motivation for treatment associated with greater care needs and psychosocial problems.

PubMed

Stobbe, Jolanda; Wierdsma, Andre I; Kok, Rob M; Kroon, Hans; Depla, Marja; Roosenschoon, Bert-Jan; Mulder, Cornelis L

2013-01-01

To compare the care needs and severity of psychosocial problems in older patients with severe mental illness (SMI) between those who were and were not motivated for treatment. Cross-sectional study in which we enrolled 141 outpatients with SMI aged 55 and older. Needs were measured using the Camberwell Assessment of Needs for the Elderly, and psychosocial problems with the Health of the Nation Outcome Scale 65+. Motivation for treatment was assessed using a motivation-for-change scale. Parametric and non-parametric tests were used to analyze differences between motivated and non-motivated patients. Explorative logistic regression analyses were used to establish, which unmet needs were associated with motivation. Less-motivated patients had greater unmet care needs and more psychosocial problems than those who were motivated. Logistic regression analyses showed that lack of motivation was associated with greater unmet needs regarding daytime activities, psychotic symptoms, behavioral problems, and addiction problems. Lack of treatment motivation was associated with more unmet needs and more severe psychosocial problems. Further research will be needed to identify other factors associated with motivation in older people with SMI and to investigate whether this group of patient benefits from interventions such as assertive outreach, integrated care or treatment-adherence therapy.

Inverse associations between perceived racism and coronary artery calcification.

PubMed

Everage, Nicholas J; Gjelsvik, Annie; McGarvey, Stephen T; Linkletter, Crystal D; Loucks, Eric B

2012-03-01

To evaluate whether racial discrimination is associated with coronary artery calcification (CAC) in African-American participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study. The study included American Black men (n = 571) and women (n = 791) aged 33 to 45 years in the CARDIA study. Perceived racial discrimination was assessed based on the Experiences of Discrimination scale (range, 1-35). CAC was evaluated using computed tomography. Primary analyses assessed associations between perceived racial discrimination and presence of CAC using multivariable-adjusted logistic regression analysis, adjusted for age, gender, socioeconomic position (SEP), psychosocial variables, and coronary heart disease (CHD) risk factors. In age- and gender-adjusted logistic regression models, odds of CAC decreased as the perceived racial discrimination score increased (odds ratio [OR], 0.94; 95% confidence interval [CI], 0.90-0.98 per 1-unit increase in Experiences of Discrimination scale). The relationship did not markedly change after further adjustment for SEP, psychosocial variables, or CHD risk factors (OR, 0.93; 95% CI, 0.87-0.99). Perceived racial discrimination was negatively associated with CAC in this study. Estimation of more forms of racial discrimination as well as replication of analyses in other samples will help to confirm or refute these findings. Copyright © 2012 Elsevier Inc. All rights reserved.

Younger age, female sex, and high number of awakenings and arousals predict fatigue in patients with sleep disorders: a retrospective polysomnographic observational study

PubMed Central

Veauthier, Christian

2013-01-01

Background The Fatigue Severity Scale (FSS) is widely used to assess fatigue, not only in the context of multiple sclerosis-related fatigue, but also in many other medical conditions. Some polysomnographic studies have shown high FSS values in sleep-disordered patients without multiple sclerosis. The Modified Fatigue Impact Scale (MFIS) has increasingly been used in order to assess fatigue, but polysomnographic data investigating sleep-disordered patients are thus far unavailable. Moreover, the pathophysiological link between sleep architecture and fatigue measured with the MFIS and the FSS has not been previously investigated. Methods This was a retrospective observational study (n = 410) with subgroups classified according to sleep diagnosis. The statistical analysis included nonparametric correlation between questionnaire results and polysomnographic data, age and sex, and univariate and multiple logistic regression. Results The multiple logistic regression showed a significant relationship between FSS/MFIS values and younger age and female sex. Moreover, there was a significant relationship between FSS values and number of arousals and between MFIS values and number of awakenings. Conclusion Younger age, female sex, and high number of awakenings and arousals are predictive of fatigue in sleep-disordered patients. Further investigations are needed to find the pathophysiological explanation for these relationships. PMID:24109185

Constructive thinking, rational intelligence and irritable bowel syndrome.

PubMed

Rey, Enrique; Moreno Ortega, Marta; Garcia Alonso, Monica-Olga; Diaz-Rubio, Manuel

2009-07-07

To evaluate rational and experiential intelligence in irritable bowel syndrome (IBS) sufferers. We recruited 100 subjects with IBS as per Rome II criteria (50 consulters and 50 non-consulters) and 100 healthy controls, matched by age, sex and educational level. Cases and controls completed a clinical questionnaire (including symptom characteristics and medical consultation) and the following tests: rational-intelligence (Wechsler Adult Intelligence Scale, 3rd edition); experiential-intelligence (Constructive Thinking Inventory); personality (NEO personality inventory); psychopathology (MMPI-2), anxiety (state-trait anxiety inventory) and life events (social readjustment rating scale). Analysis of variance was used to compare the test results of IBS-sufferers and controls, and a logistic regression model was then constructed and adjusted for age, sex and educational level to evaluate any possible association with IBS. No differences were found between IBS cases and controls in terms of IQ (102.0 +/- 10.8 vs 102.8 +/- 12.6), but IBS sufferers scored significantly lower in global constructive thinking (43.7 +/- 9.4 vs 49.6 +/- 9.7). In the logistic regression model, global constructive thinking score was independently linked to suffering from IBS [OR 0.92 (0.87-0.97)], without significant OR for total IQ. IBS subjects do not show lower rational intelligence than controls, but lower experiential intelligence is nevertheless associated with IBS.

Female autonomy and reported abortion-seeking in Ghana, West Africa.

PubMed

Rominski, Sarah D; Gupta, Mira; Aborigo, Raymond; Adongo, Phillip; Engman, Cyril; Hodgson, Abraham; Moyer, Cheryl

2014-09-01

To investigate factors associated with self-reported pregnancy termination in Ghana and thereby appreciate the correlates of abortion-seeking in order to understand safe abortion care provision. In a retrospective study, data from the Ghana 2008 Demographic and Health Survey were used to investigate factors associated with self-reported pregnancy termination. Variables on an individual and household level were examined by both bivariate analyses and multivariate logistic regression. A five-point autonomy scale was created to explore the role of female autonomy in reported abortion-seeking behavior. Among 4916 women included in the survey, 791 (16.1%) reported having an abortion. Factors associated with abortion-seeking included being older, having attended school, and living in an urban versus a rural area. When entered into a logistic regression model with demographic control variables, every step up the autonomy scale (i.e. increasing autonomy) was associated with a 14.0% increased likelihood of reporting the termination of a pregnancy (P < 0.05). Although health system barriers might play a role in preventing women from seeking safe abortion services, autonomy on an individual level is also important and needs to be addressed if women are to be empowered to seek safe abortion services. Copyright © 2014 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Household Food Insecurity May Predict Underweightand Wasting among Children Aged 24-59 Months.

PubMed

Abdurahman, Ahmed A; Mirzaei, Khadijeh; Dorosty, Ahmed Reza; Rahimiforoushani, A; Kedir, Haji

2016-01-01

The aim of this study was to examine the association between household food insecurity and nutritional status among children aged 24-59 months in Haromaya District. Children (N = 453) aged 24-59 months were recruited in a community-based cross-sectional survey with a representative sample of households selected by a multistage sampling procedure in Haromaya District. Household Food Insecurity Access Scale and anthropometry were administered. Multinomial logistic regression models were applied to select variables that are candidate for multivariable model. The prevalences of stunting, underweight, and wasting among children aged 24-59 months were 61.1%, 28.1%, and 11.8%, respectively. The mean household food insecurity access scale score was 3.34, and 39.7% of households experienced some degree of food insecurity. By logistic regression analysis and after adjusting for the confounding factors, household food insecurity was significantly predictive of underweight (AOR = 2.48, CI = 1.17-5.24, p = .05) and chronic energy deficiency (AOR = 0.47, CI = 0.23-0.97, p = .04) and marginally significant for wasting (AOR = 0.53, CI = 0.27-1.03, p = .06). It is concluded that household food security improves child growth and nutritional status.

Logistic regression analysis to predict Medical Licensing Examination of Thailand (MLET) Step1 success or failure.

PubMed

Wanvarie, Samkaew; Sathapatayavongs, Boonmee

2007-09-01

The aim of this paper was to assess factors that predict students' performance in the Medical Licensing Examination of Thailand (MLET) Step1 examination. The hypothesis was that demographic factors and academic records would predict the students' performance in the Step1 Licensing Examination. A logistic regression analysis of demographic factors (age, sex and residence) and academic records [high school grade point average (GPA), National University Entrance Examination Score and GPAs of the pre-clinical years] with the MLET Step1 outcome was accomplished using the data of 117 third-year Ramathibodi medical students. Twenty-three (19.7%) students failed the MLET Step1 examination. Stepwise logistic regression analysis showed that the significant predictors of MLET Step1 success/failure were residence background and GPAs of the second and third preclinical years. For students whose sophomore and third-year GPAs increased by an average of 1 point, the odds of passing the MLET Step1 examination increased by a factor of 16.3 and 12.8 respectively. The minimum GPAs for students from urban and rural backgrounds to pass the examination were estimated from the equation (2.35 vs 2.65 from 4.00 scale). Students from rural backgrounds and/or low-grade point averages in their second and third preclinical years of medical school are at risk of failing the MLET Step1 examination. They should be given intensive tutorials during the second and third pre-clinical years.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models.

PubMed

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models' with and without novel biomarkers. Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham's "general CVD risk" algorithm. The command is addpred for logistic regression models. The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers.

Smoking media literacy in Vietnamese adolescents.

PubMed

Page, Randy M; Huong, Nguyen T; Chi, Hoang K; Tien, Truong Q

2011-01-01

Smoking media literacy (SML) has been found to be independently associated with reduced current smoking and reduced susceptibility to future smoking in a sample of American adolescents, but not in other populations of adolescents. Thus, the purpose of this study was to assess SML in Vietnamese adolescents and to determine the association with smoking behavior and susceptibility to future smoking. A cross-sectional survey of 2000 high school students completed the SML scale, which is based on an integrated theoretical framework of media literacy, and items assessing cigarette use. Ordinal logistic regression was used to determine the association of SML with smoking and susceptibility to future smoking. Ordinal logistic regression was also to determine whether smoking in the past 30 days was associated with the 8 domains/core concepts of media literacy which comprise the SML. Smoking media literacy was lower among the Vietnamese adolescents than what has been previously reported in American adolescents. Ordinal logistic regression analysis results showed that in the total sample SML was associated with reduced smoking, but there was no association with susceptibility to future smoking. Further analysis showed that results differed according to school and grade level. There did not appear to be association of smoking with the specific domains/concepts that comprise the SML. The association of SML with reduced smoking suggests the need for further research involving SML, including the testing of media literacy training interventions, in Vietnamese adolescents and also other populations of adolescents. © 2011, American School Health Association.

Comparison of naïve Bayes and logistic regression for computer-aided diagnosis of breast masses using ultrasound imaging

NASA Astrophysics Data System (ADS)

Cary, Theodore W.; Cwanger, Alyssa; Venkatesh, Santosh S.; Conant, Emily F.; Sehgal, Chandra M.

2012-03-01

This study compares the performance of two proven but very different machine learners, Naïve Bayes and logistic regression, for differentiating malignant and benign breast masses using ultrasound imaging. Ultrasound images of 266 masses were analyzed quantitatively for shape, echogenicity, margin characteristics, and texture features. These features along with patient age, race, and mammographic BI-RADS category were used to train Naïve Bayes and logistic regression classifiers to diagnose lesions as malignant or benign. ROC analysis was performed using all of the features and using only a subset that maximized information gain. Performance was determined by the area under the ROC curve, Az, obtained from leave-one-out cross validation. Naïve Bayes showed significant variation (Az 0.733 +/- 0.035 to 0.840 +/- 0.029, P < 0.002) with the choice of features, but the performance of logistic regression was relatively unchanged under feature selection (Az 0.839 +/- 0.029 to 0.859 +/- 0.028, P = 0.605). Out of 34 features, a subset of 6 gave the highest information gain: brightness difference, margin sharpness, depth-to-width, mammographic BI-RADs, age, and race. The probabilities of malignancy determined by Naïve Bayes and logistic regression after feature selection showed significant correlation (R2= 0.87, P < 0.0001). The diagnostic performance of Naïve Bayes and logistic regression can be comparable, but logistic regression is more robust. Since probability of malignancy cannot be measured directly, high correlation between the probabilities derived from two basic but dissimilar models increases confidence in the predictive power of machine learning models for characterizing solid breast masses on ultrasound.

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

PubMed

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

23 %. AUTIOR(.) S. CONTRACT OR GRANT NUMBE Rf.i %Z. D. Bai, P. R. Krishnaiah and . C. Zhao F49620-85- C-0008 " PERFORMING ORGANIZATION NAME AND AOORESS...d I7 IOK-TK- d 7 -I0 7’ VARIABLE SELECTION IN LOGISTIC REGRESSION Z. D. Bai, P. R. Krishnaiah and L. C. Zhao Center for Multivariate Analysis...University of Pittsburgh Center for Multivariate Analysis University of Pittsburgh Y !I VARIABLE SELECTION IN LOGISTIC REGRESSION Z- 0. Bai, P. R. Krishnaiah

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

PubMed Central

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Methods: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Conclusions: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant. PMID:23113198

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

PubMed

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

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.

ADCYAP1R1 and asthma in Puerto Rican children.

PubMed

Chen, Wei; Boutaoui, Nadia; Brehm, John M; Han, Yueh-Ying; Schmitz, Cassandra; Cressley, Alex; Acosta-Pérez, Edna; Alvarez, María; Colón-Semidey, Angel; Baccarelli, Andrea A; Weeks, Daniel E; Kolls, Jay K; Canino, Glorisa; Celedón, Juan C

2013-03-15

Epigenetic and/or genetic variation in the gene encoding the receptor for adenylate-cyclase activating polypeptide 1 (ADCYAP1R1) has been linked to post-traumatic stress disorder in adults and anxiety in children. Psychosocial stress has been linked to asthma morbidity in Puerto Rican children. To examine whether epigenetic or genetic variation in ADCYAP1R1 is associated with childhood asthma in Puerto Ricans. We conducted a case-control study of 516 children ages 6-14 years living in San Juan, Puerto Rico. We assessed methylation at a CpG site in the promoter of ADCYAP1R1 (cg11218385) using a pyrosequencing assay in DNA from white blood cells. We tested whether cg11218385 methylation (range, 0.4-6.1%) is associated with asthma using logistic regression. We also examined whether exposure to violence (assessed by the Exposure to Violence [ETV] Scale in children 9 yr and older) is associated with cg11218385 methylation (using linear regression) or asthma (using logistic regression). Logistic regression was used to test for association between a single nucleotide polymorphism in ADCYAP1R1 (rs2267735) and asthma under an additive model. All multivariate models were adjusted for age, sex, household income, and principal components. EACH 1% increment in cg11218385 methylation was associated with increased odds of asthma (adjusted odds ratio, 1.3; 95% confidence interval, 1.0-1.6; P = 0.03). Among children 9 years and older, exposure to violence was associated with cg11218385 methylation. The C allele of single nucleotide polymorphism rs2267735 was significantly associated with increased odds of asthma (adjusted odds ratio, 1.3; 95% confidence interval, 1.02-1.67; P = 0.03). Epigenetic and genetic variants in ADCYAP1R1 are associated with asthma in Puerto Rican children.

Hierarchical faunal filters: An approach to assessing effects of habitat and nonnative species on native fishes

USGS Publications Warehouse

Quist, M.C.; Rahel, F.J.; Hubert, W.A.

2005-01-01

Understanding factors related to the occurrence of species across multiple spatial and temporal scales is critical to the conservation and management of native fishes, especially for those species at the edge of their natural distribution. We used the concept of hierarchical faunal filters to provide a framework for investigating the influence of habitat characteristics and normative piscivores on the occurrence of 10 native fishes in streams of the North Platte River watershed in Wyoming. Three faunal filters were developed for each species: (i) large-scale biogeographic, (ii) local abiotic, and (iii) biotic. The large-scale biogeographic filter, composed of elevation and stream-size thresholds, was used to determine the boundaries within which each species might be expected to occur. Then, a local abiotic filter (i.e., habitat associations), developed using binary logistic-regression analysis, estimated the probability of occurrence of each species from features such as maximum depth, substrate composition, submergent aquatic vegetation, woody debris, and channel morphology (e.g., amount of pool habitat). Lastly, a biotic faunal filter was developed using binary logistic regression to estimate the probability of occurrence of each species relative to the abundance of nonnative piscivores in a reach. Conceptualising fish assemblages within a framework of hierarchical faunal filters is simple and logical, helps direct conservation and management activities, and provides important information on the ecology of fishes in the western Great Plains of North America. ?? Blackwell Munksgaard, 2004.

Factors Predicting Recovery of Oral Intake in Stroke Survivors with Dysphagia in a Convalescent Rehabilitation Ward.

PubMed

Ikenaga, Yasunori; Nakayama, Sayaka; Taniguchi, Hiroki; Ohori, Isao; Komatsu, Nahoko; Nishimura, Hitoshi; Katsuki, Yasuo

2017-05-01

Percutaneous endoscopic gastrostomy may be performed in dysphagic stroke patients. However, some patients regain complete oral intake without gastrostomy. This study aimed to investigate the predictive factors of intake, thereby determining gastrostomy indications. Stroke survivors admitted to our convalescent rehabilitation ward who underwent gastrostomy or nasogastric tube placement from 2009 to 2015 were divided into 2 groups based on intake status at discharge. Demographic data and Functional Independence Measure (FIM), Dysphagia Severity Scale (DSS), National Institutes of Health Stroke Scale, and Glasgow Coma Scale (GCS) scores on admission were compared between groups. We evaluated the factors predicting intake using a stepwise logistic regression analysis. Thirty-four patients recovered intake, whereas 38 achieved incomplete intake. Mean age was lower, mean body mass index (BMI) was higher, and mean time from stroke onset to admission was shorter in the complete intake group. The complete intake group had less impairment in terms of GCS, FIM, and DSS scores. In the stepwise logistic regression analysis, BMI, FIM-cognitive score, and DSS score were significant independent factors predicting intake. The formula of BMI × .26 + FIM cognitive score × .19 + DSS score × 1.60 predicted recovery of complete intake with a sensitivity of 88.2% and a specificity of 84.2%. Stroke survivors with dysphagia with a high BMI and FIM-cognitive and DSS scores tended to recover oral intake. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Indicators of suboptimal performance embedded in the Wechsler Memory Scale-Fourth Edition (WMS-IV).

PubMed

Bouman, Zita; Hendriks, Marc P H; Schmand, Ben A; Kessels, Roy P C; Aldenkamp, Albert P

2016-01-01

Recognition and visual working memory tasks from the Wechsler Memory Scale-Fourth Edition (WMS-IV) have previously been documented as useful indicators for suboptimal performance. The present study examined the clinical utility of the Dutch version of the WMS-IV (WMS-IV-NL) for the identification of suboptimal performance using an analogue study design. The patient group consisted of 59 mixed-etiology patients; the experimental malingerers were 50 healthy individuals who were asked to simulate cognitive impairment as a result of a traumatic brain injury; the last group consisted of 50 healthy controls who were instructed to put forth full effort. Experimental malingerers performed significantly lower on all WMS-IV-NL tasks than did the patients and healthy controls. A binary logistic regression analysis was performed on the experimental malingerers and the patients. The first model contained the visual working memory subtests (Spatial Addition and Symbol Span) and the recognition tasks of the following subtests: Logical Memory, Verbal Paired Associates, Designs, Visual Reproduction. The results showed an overall classification rate of 78.4%, and only Spatial Addition explained a significant amount of variation (p < .001). Subsequent logistic regression analysis and receiver operating characteristic (ROC) analysis supported the discriminatory power of the subtest Spatial Addition. A scaled score cutoff of <4 produced 93% specificity and 52% sensitivity for detection of suboptimal performance. The WMS-IV-NL Spatial Addition subtest may provide clinically useful information for the detection of suboptimal performance.

Relational coordination among home healthcare professions and goal attainment in nursing care.

PubMed

Sakai, Mahiro; Naruse, Takashi; Nagata, Satoko

2016-07-01

To examine whether interprofessional coordination is related to goal attainment in home visit nursing care. Self-administered questionnaire surveys were administered to home visit nursing agencies in Chiba Prefecture, Japan, from July to December 2014. Nurses evaluated their interprofessional coordination with professional groups (nursing colleague and managers, home doctors, care managers, home care workers, visiting therapists, day service and day care professionals, visiting bath professionals, and short stay professionals) using the Japanese version of the Relational Coordination Scale (RCS-J). Goal attainment across all clients during the most recent 3 months was measured with a rating scale ranging from incompletely attained (0) to completely attained (10). Data were analyzed with multivariate logistic regression analysis. A total of 83 nurses in 14 agencies responded, and data from 74 nurses were analyzed. The mean RCS-J and goal attainment scores were 3.59 (standard deviation = 0.47) and 6.51 (1.40), respectively. The RCS-J scores of the low and high goal attainment groups were 3.41 (0.46) and 3.73 (0.42), respectively. Multivariate logistic regression analysis revealed that RCS-J scores were positively associated with goal attainment (odds ratio, 5.71; 95% confidence interval, 1.65-19.79). The finding of this study suggest that well-coordinated professionals may fulfill client needs better than poorly coordinated professionals do. Future research is needed to determine whether similar results are obtained in individual clients using a well-validated goal attainment scale. © 2016 Japan Academy of Nursing Science.

Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

ERIC Educational Resources Information Center

Koon, Sharon; Petscher, Yaacov

2015-01-01

The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…

Desire thinking: A risk factor for binge eating?

PubMed

Spada, Marcantonio M; Caselli, Gabriele; Fernie, Bruce A; Manfredi, Chiara; Boccaletti, Fabio; Dallari, Giulia; Gandini, Federica; Pinna, Eleonora; Ruggiero, Giovanni M; Sassaroli, Sandra

2015-08-01

In the current study we explored the role of desire thinking in predicting binge eating independently of Body Mass Index, negative affect and irrational food beliefs. A sample of binge eaters (n=77) and a sample of non-binge eaters (n=185) completed the following self-report instruments: Hospital Anxiety and Depression Scale, Irrational Food Beliefs Scale, Desire Thinking Questionnaire, and Binge Eating Scale. Mann-Whitney U tests revealed that all variable scores were significantly higher for binge eaters than non-binge eaters. A logistic regression analysis indicated that verbal perseveration was a predictor of classification as a binge eater over and above Body Mass Index, negative affect and irrational food beliefs. A hierarchical regression analysis, on the combined sample, indicated that verbal perseveration predicted levels of binge eating independently of Body Mass Index, negative affect and irrational food beliefs. These results highlight the possible role of desire thinking as a risk factor for binge eating. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

Expression of Proteins Involved in Epithelial-Mesenchymal Transition as Predictors of Metastasis and Survival in Breast Cancer Patients

DTIC Science & Technology

2013-11-01

Ptrend 0.78 0.62 0.75 Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for risk of node...Ptrend 0.71 0.67 Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for risk of high-grade tumors... logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for the associations between each of the seven SNPs and

Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography.

PubMed

Kim, Sun Mi; Kim, Yongdai; Jeong, Kuhwan; Jeong, Heeyeong; Kim, Jiyoung

2018-01-01

The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests. Logistic LASSO regression was superior (P<0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD) and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD). However, it was inferior (P<0.05) to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD) and the AUC without CDD (0.785 vs. 0.844, P<0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141). Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

PubMed

Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

2017-12-28

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision. All adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.

Use and interpretation of logistic regression in habitat-selection studies

USGS Publications Warehouse

Keating, Kim A.; Cherry, Steve

2004-01-01

 Logistic regression is an important tool for wildlife habitat-selection studies, but the method frequently has been misapplied due to an inadequate understanding of the logistic model, its interpretation, and the influence of sampling design. To promote better use of this method, we review its application and interpretation under 3 sampling designs: random, case-control, and use-availability. Logistic regression is appropriate for habitat use-nonuse studies employing random sampling and can be used to directly model the conditional probability of use in such cases. Logistic regression also is appropriate for studies employing case-control sampling designs, but careful attention is required to interpret results correctly. Unless bias can be estimated or probability of use is small for all habitats, results of case-control studies should be interpreted as odds ratios, rather than probability of use or relative probability of use. When data are gathered under a use-availability design, logistic regression can be used to estimate approximate odds ratios if probability of use is small, at least on average. More generally, however, logistic regression is inappropriate for modeling habitat selection in use-availability studies. In particular, using logistic regression to fit the exponential model of Manly et al. (2002:100) does not guarantee maximum-likelihood estimates, valid probabilities, or valid likelihoods. We show that the resource selection function (RSF) commonly used for the exponential model is proportional to a logistic discriminant function. Thus, it may be used to rank habitats with respect to probability of use and to identify important habitat characteristics or their surrogates, but it is not guaranteed to be proportional to probability of use. Other problems associated with the exponential model also are discussed. We describe an alternative model based on Lancaster and Imbens (1996) that offers a method for estimating conditional probability of use in use-availability studies. Although promising, this model fails to converge to a unique solution in some important situations. Further work is needed to obtain a robust method that is broadly applicable to use-availability studies.

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

NASA Astrophysics Data System (ADS)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

Drought Patterns Forecasting using an Auto-Regressive Logistic Model

NASA Astrophysics Data System (ADS)

del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.

2014-12-01

Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.

Logistic regression models of factors influencing the location of bioenergy and biofuels plants

Treesearch

T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu

2011-01-01

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...

Discrete post-processing of total cloud cover ensemble forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

2017-04-01

This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

NASA Astrophysics Data System (ADS)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

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.

[Influences of environmental factors and interaction of several chemokines gene-environmental on systemic lupus erythematosus].

PubMed

Ye, Dong-qing; Hu, Yi-song; Li, Xiang-pei; Huang, Fen; Yang, Shi-gui; Hao, Jia-hu; Yin, Jing; Zhang, Guo-qing; Liu, Hui-hui

2004-11-01

To explore the impact of environmental factors, daily lifestyle, psycho-social factors and the interactions between environmental factors and chemokines genes on systemic lupus erythematosus (SLE). Case-control study was carried out and environmental factors for SLE were analyzed by univariate and multivariate unconditional logistic regression. Interactions between environmental factors and chemokines polymorphism contributing to systemic lupus erythematosus were also analyzed by logistic regression model. There were nineteen factors associated with SLE when univariate unconditional logistic regression was used. However, when multivariate unconditional logistic regression was used, only five factors showed having impacts on the disease, in which drinking well water (OR=0.099) was protective factor for SLE, and multiple drug allergy (OR=8.174), over-exposure to sunshine (OR=18.339), taking antibiotics (OR=9.630) and oral contraceptives were risk factors for SLE. When unconditional logistic regression model was used, results showed that there was interaction between eating irritable food and -2518MCP-1G/G genotype (OR=4.387). No interaction between environmental factors was found that contributing to SLE in this study. Many environmental factors were related to SLE, and there was an interaction between -2518MCP-1G/G genotype and eating irritable food.

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

PubMed

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

Controlling Type I Error Rates in Assessing DIF for Logistic Regression Method Combined with SIBTEST Regression Correction Procedure and DIF-Free-Then-DIF Strategy

ERIC Educational Resources Information Center

Shih, Ching-Lin; Liu, Tien-Hsiang; Wang, Wen-Chung

2014-01-01

The simultaneous item bias test (SIBTEST) method regression procedure and the differential item functioning (DIF)-free-then-DIF strategy are applied to the logistic regression (LR) method simultaneously in this study. These procedures are used to adjust the effects of matching true score on observed score and to better control the Type I error…

Clinical features of pure obsessive-compulsive disorder.

PubMed

Torres, Albina R; Shavitt, Roseli G; Torresan, Ricardo C; Ferrão, Ygor A; Miguel, Euripedes C; Fontenelle, Leonardo F

2013-10-01

Psychiatric comorbidity is the rule in obsessive-compulsive disorder (OCD); however, very few studies have evaluated the clinical characteristics of patients with no co-occurring disorders (non-comorbid or "pure" OCD). The aim of this study was to estimate the prevalence of pure cases in a large multicenter sample of OCD patients and compare the sociodemographic and clinical characteristics of individuals with and without any lifetime axis I comorbidity. A cross-sectional study with 955 adult patients of the Brazilian Research Consortium on Obsessive-Compulsive Spectrum Disorders (C-TOC). Assessment instruments included the Yale-Brown Obsessive-Compulsive Scale, the Dimensional Yale-Brown Obsessive-Compulsive Scale, The USP-Sensory Phenomena Scale and the Brown Assessment of Beliefs Scale. Comorbidities were evaluated using the Structured Clinical Interview for DSM-IV Axis I Disorders. Bivariate analyses were followed by logistic regression. Only 74 patients (7.7%) presented pure OCD. Compared with those presenting at least one lifetime comorbidity (881, 92.3%), non-comorbid patients were more likely to be female and to be working, reported less traumatic experiences and presented lower scores in the Y-BOCS obsession subscale and in total DY-BOCS scores. All symptom dimensions except contamination-cleaning and hoarding were less severe in non-comorbid patients. They also presented less severe depression and anxiety, lower suicidality and less previous treatments. In the logistic regression, the following variables predicted pure OCD: sex, severity of depressive and anxious symptoms, previous suicidal thoughts and psychotherapy. Pure OCD patients were the minority in this large sample and were characterized by female sex, less severe depressive and anxious symptoms, less suicidal thoughts and less use of psychotherapy as a treatment modality. The implications of these findings for clinical practice are discussed. Copyright © 2013 Elsevier Inc. All rights reserved.

Data-driven mapping of the potential mountain permafrost distribution.

PubMed

Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

2017-07-15

Existing mountain permafrost distribution models generally offer a good overview of the potential extent of this phenomenon at a regional scale. They are however not always able to reproduce the high spatial discontinuity of permafrost at the micro-scale (scale of a specific landform; ten to several hundreds of meters). To overcome this lack, we tested an alternative modelling approach using three classification algorithms belonging to statistics and machine learning: Logistic regression, Support Vector Machines and Random forests. These supervised learning techniques infer a classification function from labelled training data (pixels of permafrost absence and presence) with the aim of predicting the permafrost occurrence where it is unknown. The research was carried out in a 588km 2 area of the Western Swiss Alps. Permafrost evidences were mapped from ortho-image interpretation (rock glacier inventorying) and field data (mainly geoelectrical and thermal data). The relationship between selected permafrost evidences and permafrost controlling factors was computed with the mentioned techniques. Classification performances, assessed with AUROC, range between 0.81 for Logistic regression, 0.85 with Support Vector Machines and 0.88 with Random forests. The adopted machine learning algorithms have demonstrated to be efficient for permafrost distribution modelling thanks to consistent results compared to the field reality. The high resolution of the input dataset (10m) allows elaborating maps at the micro-scale with a modelled permafrost spatial distribution less optimistic than classic spatial models. Moreover, the probability output of adopted algorithms offers a more precise overview of the potential distribution of mountain permafrost than proposing simple indexes of the permafrost favorability. These encouraging results also open the way to new possibilities of permafrost data analysis and mapping. Copyright © 2017 Elsevier B.V. All rights reserved.

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

PubMed

Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

2016-01-01

Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46 450 cases and 42 461 controls from the breast cancer association consortium

PubMed Central

Milne, Roger L.; Herranz, Jesús; Michailidou, Kyriaki; Dennis, Joe; Tyrer, Jonathan P.; Zamora, M. Pilar; Arias-Perez, José Ignacio; González-Neira, Anna; Pita, Guillermo; Alonso, M. Rosario; Wang, Qin; Bolla, Manjeet K.; Czene, Kamila; Eriksson, Mikael; Humphreys, Keith; Darabi, Hatef; Li, Jingmei; Anton-Culver, Hoda; Neuhausen, Susan L.; Ziogas, Argyrios; Clarke, Christina A.; Hopper, John L.; Dite, Gillian S.; Apicella, Carmel; Southey, Melissa C.; Chenevix-Trench, Georgia; Swerdlow, Anthony; Ashworth, Alan; Orr, Nicholas; Schoemaker, Minouk; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Andrulis, Irene L.; Knight, Julia A.; Glendon, Gord; Mulligan, Anna Marie; Bojesen, Stig E.; Nordestgaard, Børge G.; Flyger, Henrik; Nevanlinna, Heli; Muranen, Taru A.; Aittomäki, Kristiina; Blomqvist, Carl; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Wang, Xianshu; Olson, Janet E.; Vachon, Celine; Purrington, Kristen; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Dunning, Alison M.; Shah, Mitul; Guénel, Pascal; Truong, Thérèse; Sanchez, Marie; Mulot, Claire; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Lindblom, Annika; Margolin, Sara; Hooning, Maartje J.; Hollestelle, Antoinette; Collée, J. Margriet; Jager, Agnes; Cox, Angela; Brock, Ian W.; Reed, Malcolm W.R.; Devilee, Peter; Tollenaar, Robert A.E.M.; Seynaeve, Caroline; Haiman, Christopher A.; Henderson, Brian E.; Schumacher, Fredrick; Le Marchand, Loic; Simard, Jacques; Dumont, Martine; Soucy, Penny; Dörk, Thilo; Bogdanova, Natalia V.; Hamann, Ute; Försti, Asta; Rüdiger, Thomas; Ulmer, Hans-Ulrich; Fasching, Peter A.; Häberle, Lothar; Ekici, Arif B.; Beckmann, Matthias W.; Fletcher, Olivia; Johnson, Nichola; dos Santos Silva, Isabel; Peto, Julian; Radice, Paolo; Peterlongo, Paolo; Peissel, Bernard; Mariani, Paolo; Giles, Graham G.; Severi, Gianluca; Baglietto, Laura; Sawyer, Elinor; Tomlinson, Ian; Kerin, Michael; Miller, Nicola; Marme, Federik; Burwinkel, Barbara; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M.; Lambrechts, Diether; Yesilyurt, Betul T.; Floris, Giuseppe; Leunen, Karin; Alnæs, Grethe Grenaker; Kristensen, Vessela; Børresen-Dale, Anne-Lise; García-Closas, Montserrat; Chanock, Stephen J.; Lissowska, Jolanta; Figueroa, Jonine D.; Schmidt, Marjanka K.; Broeks, Annegien; Verhoef, Senno; Rutgers, Emiel J.; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Couch, Fergus J.; Toland, Amanda E.; Yannoukakos, Drakoulis; Pharoah, Paul D.P.; Hall, Per; Benítez, Javier; Malats, Núria; Easton, Douglas F.

2014-01-01

Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70 917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46 450 breast cancer cases and 42 461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P < 0.01) of a per-allele main effect, and all two-way combinations of those were evaluated by a per-allele (1 d.f.) test for interaction using logistic regression. Second, all 2.5 billion possible two-SNP combinations were evaluated using Boolean operation-based screening and testing, and SNP pairs with the strongest evidence of interaction (P < 10−4) were selected for more careful assessment by logistic regression. Under the first approach, 3277 SNPs were preselected, but an evaluation of all possible two-SNP combinations (1 d.f.) identified no interactions at P < 10−8. Results from the second analytic approach were consistent with those from the first (P > 10−10). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome. PMID:24242184

A large-scale assessment of two-way SNP interactions in breast cancer susceptibility using 46,450 cases and 42,461 controls from the breast cancer association consortium.

PubMed

Milne, Roger L; Herranz, Jesús; Michailidou, Kyriaki; Dennis, Joe; Tyrer, Jonathan P; Zamora, M Pilar; Arias-Perez, José Ignacio; González-Neira, Anna; Pita, Guillermo; Alonso, M Rosario; Wang, Qin; Bolla, Manjeet K; Czene, Kamila; Eriksson, Mikael; Humphreys, Keith; Darabi, Hatef; Li, Jingmei; Anton-Culver, Hoda; Neuhausen, Susan L; Ziogas, Argyrios; Clarke, Christina A; Hopper, John L; Dite, Gillian S; Apicella, Carmel; Southey, Melissa C; Chenevix-Trench, Georgia; Swerdlow, Anthony; Ashworth, Alan; Orr, Nicholas; Schoemaker, Minouk; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Andrulis, Irene L; Knight, Julia A; Glendon, Gord; Mulligan, Anna Marie; Bojesen, Stig E; Nordestgaard, Børge G; Flyger, Henrik; Nevanlinna, Heli; Muranen, Taru A; Aittomäki, Kristiina; Blomqvist, Carl; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Wang, Xianshu; Olson, Janet E; Vachon, Celine; Purrington, Kristen; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Dunning, Alison M; Shah, Mitul; Guénel, Pascal; Truong, Thérèse; Sanchez, Marie; Mulot, Claire; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Lindblom, Annika; Margolin, Sara; Hooning, Maartje J; Hollestelle, Antoinette; Collée, J Margriet; Jager, Agnes; Cox, Angela; Brock, Ian W; Reed, Malcolm W R; Devilee, Peter; Tollenaar, Robert A E M; Seynaeve, Caroline; Haiman, Christopher A; Henderson, Brian E; Schumacher, Fredrick; Le Marchand, Loic; Simard, Jacques; Dumont, Martine; Soucy, Penny; Dörk, Thilo; Bogdanova, Natalia V; Hamann, Ute; Försti, Asta; Rüdiger, Thomas; Ulmer, Hans-Ulrich; Fasching, Peter A; Häberle, Lothar; Ekici, Arif B; Beckmann, Matthias W; Fletcher, Olivia; Johnson, Nichola; dos Santos Silva, Isabel; Peto, Julian; Radice, Paolo; Peterlongo, Paolo; Peissel, Bernard; Mariani, Paolo; Giles, Graham G; Severi, Gianluca; Baglietto, Laura; Sawyer, Elinor; Tomlinson, Ian; Kerin, Michael; Miller, Nicola; Marme, Federik; Burwinkel, Barbara; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Lambrechts, Diether; Yesilyurt, Betul T; Floris, Giuseppe; Leunen, Karin; Alnæs, Grethe Grenaker; Kristensen, Vessela; Børresen-Dale, Anne-Lise; García-Closas, Montserrat; Chanock, Stephen J; Lissowska, Jolanta; Figueroa, Jonine D; Schmidt, Marjanka K; Broeks, Annegien; Verhoef, Senno; Rutgers, Emiel J; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Couch, Fergus J; Toland, Amanda E; Yannoukakos, Drakoulis; Pharoah, Paul D P; Hall, Per; Benítez, Javier; Malats, Núria; Easton, Douglas F

2014-04-01

Part of the substantial unexplained familial aggregation of breast cancer may be due to interactions between common variants, but few studies have had adequate statistical power to detect interactions of realistic magnitude. We aimed to assess all two-way interactions in breast cancer susceptibility between 70,917 single nucleotide polymorphisms (SNPs) selected primarily based on prior evidence of a marginal effect. Thirty-eight international studies contributed data for 46,450 breast cancer cases and 42,461 controls of European origin as part of a multi-consortium project (COGS). First, SNPs were preselected based on evidence (P < 0.01) of a per-allele main effect, and all two-way combinations of those were evaluated by a per-allele (1 d.f.) test for interaction using logistic regression. Second, all 2.5 billion possible two-SNP combinations were evaluated using Boolean operation-based screening and testing, and SNP pairs with the strongest evidence of interaction (P < 10(-4)) were selected for more careful assessment by logistic regression. Under the first approach, 3277 SNPs were preselected, but an evaluation of all possible two-SNP combinations (1 d.f.) identified no interactions at P < 10(-8). Results from the second analytic approach were consistent with those from the first (P > 10(-10)). In summary, we observed little evidence of two-way SNP interactions in breast cancer susceptibility, despite the large number of SNPs with potential marginal effects considered and the very large sample size. This finding may have important implications for risk prediction, simplifying the modelling required. Further comprehensive, large-scale genome-wide interaction studies may identify novel interacting loci if the inherent logistic and computational challenges can be overcome.

On the use and misuse of scalar scores of confounders in design and analysis of observational studies.

PubMed

Pfeiffer, R M; Riedl, R

2015-08-15

We assess the asymptotic bias of estimates of exposure effects conditional on covariates when summary scores of confounders, instead of the confounders themselves, are used to analyze observational data. First, we study regression models for cohort data that are adjusted for summary scores. Second, we derive the asymptotic bias for case-control studies when cases and controls are matched on a summary score, and then analyzed either using conditional logistic regression or by unconditional logistic regression adjusted for the summary score. Two scores, the propensity score (PS) and the disease risk score (DRS) are studied in detail. For cohort analysis, when regression models are adjusted for the PS, the estimated conditional treatment effect is unbiased only for linear models, or at the null for non-linear models. Adjustment of cohort data for DRS yields unbiased estimates only for linear regression; all other estimates of exposure effects are biased. Matching cases and controls on DRS and analyzing them using conditional logistic regression yields unbiased estimates of exposure effect, whereas adjusting for the DRS in unconditional logistic regression yields biased estimates, even under the null hypothesis of no association. Matching cases and controls on the PS yield unbiased estimates only under the null for both conditional and unconditional logistic regression, adjusted for the PS. We study the bias for various confounding scenarios and compare our asymptotic results with those from simulations with limited sample sizes. To create realistic correlations among multiple confounders, we also based simulations on a real dataset. Copyright © 2015 John Wiley & Sons, Ltd.

[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.

Mortality-Associated Characteristics of Patients with Traumatic Brain Injury at the University Teaching Hospital of Kigali, Rwanda.

PubMed

Krebs, Elizabeth; Gerardo, Charles J; Park, Lawrence P; Nickenig Vissoci, Joao Ricardo; Byiringiro, Jean Claude; Byiringiro, Fidele; Rulisa, Stephen; Thielman, Nathan M; Staton, Catherine A

2017-06-01

Traumatic brain injury (TBI) is a leading cause of death and disability. Patients with TBI in low and middle-income countries have worse outcomes than patients in high-income countries. We evaluated important clinical indicators associated with mortality for patients with TBI at University Teaching Hospital of Kigali, Kigali, Rwanda. A prospective consecutive sampling of patients with TBI presenting to University Teaching Hospital of Kigali Accident and Emergency Department was screened for inclusion criteria: reported head trauma, alteration in consciousness, headache, and visible head trauma. Exclusion criteria were age <10 years, >48 hours after injury, and repeat visit. Data were assessed for association with death using logistic regression. Significant variables were included in a multivariate logistic regression model and refined via backward elimination. Between October 7, 2013, and April 6, 2014, 684 patients were enrolled; 14 (2%) were excluded because of incomplete data. Of patients, 81% were male with mean age of 31 years (range, 10-89 years; SD 11.8). Most patients (80%) had mild TBI (Glasgow Coma Scale [GCS] score 13-15); 10% had moderate (GCS score 9-12) and 10% had severe (GCS score 3-8) TBI. Multivariate logistic regression determined that GCS score <13, hypoxia, bradycardia, tachycardia, and age >50 years were significantly associated with death. GCS score <13, hypoxia, bradycardia, tachycardia, and age >50 years were associated with mortality. These findings inform future research that may guide clinicians in prioritizing care for patients at highest risk of mortality. Copyright © 2017 Elsevier Inc. All rights reserved.

Finding the Perfect Match: Factors That Influence Family Medicine Residency Selection.

PubMed

Wright, Katherine M; Ryan, Elizabeth R; Gatta, John L; Anderson, Lauren; Clements, Deborah S

2016-04-01

Residency program selection is a significant experience for emerging physicians, yet there is limited information about how applicants narrow their list of potential programs. This study examines factors that influence residency program selection among medical students interested in family medicine at the time of application. Medical students with an expressed interest in family medicine were invited to participate in a 37-item, online survey. Students were asked to rate factors that may impact residency selection on a 6-point Likert scale in addition to three open-ended qualitative questions. Mean values were calculated for each survey item and were used to determine a rank order for selection criteria. Logistic regression analysis was performed to identify factors that predict a strong interest in urban, suburban, and rural residency programs. Logistic regression was also used to identify factors that predict a strong interest in academic health center-based residencies, community-based residencies, and community-based residencies with an academic affiliation. A total of 705 medical students from 32 states across the country completed the survey. Location, work/life balance, and program structure (curriculum, schedule) were rated the most important factors for residency selection. Logistic regression analysis was used to refine our understanding of how each factor relates to specific types of residencies. These findings have implications for how to best advise students in selecting a residency, as well as marketing residencies to the right candidates. Refining the recruitment process will ensure a better fit between applicants and potential programs. Limited recruitment resources may be better utilized by focusing on targeted dissemination strategies.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

PubMed Central

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

NASA Astrophysics Data System (ADS)

Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

2017-12-01

Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three thresholds. The probability of a well with iron content higher than 5mg/L to contain greater than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be more than 91%, 85% and 51%, respectively, while the probability of a well from depth more than 160m to contain more than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be less than 38%, 25% and 14%, respectively.

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

PubMed

van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

2016-11-24

Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

PubMed Central

Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

2008-01-01

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

Internalized stigma among psychiatric outpatients: Associations with quality of life, functioning, hope and self-esteem.

PubMed

Picco, Louisa; Pang, Shirlene; Lau, Ying Wen; Jeyagurunathan, Anitha; Satghare, Pratika; Abdin, Edimansyah; Vaingankar, Janhavi Ajit; Lim, Susan; Poh, Chee Lien; Chong, Siow Ann; Subramaniam, Mythily

2016-12-30

This study aimed to: (i) determine the prevalence, socio-demographic and clinical correlates of internalized stigma and (ii) explore the association between internalized stigma and quality of life, general functioning, hope and self-esteem, among a multi-ethnic Asian population of patients with mental disorders. This cross-sectional, survey recruited adult patients (n=280) who were seeking treatment at outpatient and affiliated clinics of the only tertiary psychiatric hospital in Singapore. Internalized stigma was measured using the Internalized Stigma of Mental Illness scale. 43.6% experienced moderate to high internalized stigma. After making adjustments in multiple logistic regression analysis, results revealed there were no significant socio-demographic or clinical correlates relating to internalized stigma. Individual logistic regression models found a negative relationship between quality of life, self-esteem, general functioning and internalized stigma whereby lower scores were associated with higher internalized stigma. In the final regression model, which included all psychosocial variables together, self-esteem was the only variable significantly and negatively associated with internalized stigma. The results of this study contribute to our understanding of the role internalized stigma plays in patients with mental illness, and the impact it can have on psychosocial aspects of their lives. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Prevalence and risk factors associated with tardive dyskinesia among Indian patients with schizophrenia.

PubMed

Achalia, Rashmin M; Chaturvedi, Santosh K; Desai, Geetha; Rao, Girish N; Prakash, Om

2014-06-01

Tardive dyskinesia (TD) is one of the most distressing side effects of antipsychotic treatment. As prevalence studies of TD in Asian population are scarce, a cross-sectional study was performed to assess the frequency of TD in Indian patients with schizophrenia and risk factors of TD. Cross-sectional study of 160 Indian patients fulfilling the DSM-IV TR criteria for schizophrenia and who received antipsychotics for at least one year, were examined with two validated scales for TD. Logistic regression analyses were used to examine the relationship between TD and clinical risk factors. The frequency of probable TD in the total sample was 26.4%. The logistic regression yielded significant odds ratios between TD and age, intermittent treatment, and total cumulative antipsychotic dose. The difference of TD between SGA and FGA disappeared after adjusting for important co-variables in regression analysis. Indian patients with schizophrenia and long-term antipsychotic treatment have a high risk of TD, and TD is associated with older age, intermittent antipsychotic treatment, and a high total cumulative antipsychotic dose. Our study findings suggest that there is no significant difference between SGAs with regards to the risk of causing TD as compared to FGAs. Copyright © 2014 Elsevier B.V. All rights reserved.

MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION

EPA Science Inventory

Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...

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.

Screening for postpartum depression using Kurdish version of Edinburgh postnatal depression scale.

PubMed

Ahmed, Hamdia Mirkhan; Alalaf, Shahla Kareem; Al-Tawil, Namir Ghanim

2012-05-01

One of the important public health problems affecting maternal and child health is postpartum depression (PPD). It generally occurs within 6-8 weeks after childbirth. To determine the prevalence of postpartum depression (PPD) using a Kurdish version of Edinburgh postpartum depression scale (EPDS) and to analyze the risk factors for postpartum depression in a population of puerperal Kurdish women in Erbil city. A cross-sectional study was conducted between 20th of June and 30th of November 2010, in 14 antenatal care units of primary health centers, in Erbil city, Kurdistan region, Iraq. The sample of the study included 1,000 puerperal women (6-8 weeks postpartum), ranging in age from 14 to 48 years. Data were collected after interviewing the women using a questionnaire designed by the researchers, and the Kurdish version of the EPDS. Chi square test of association and the logistic regression tests were used in the analysis. The prevalence of postpartum depression was 28.4%. Logistic regression analysis showed that the factors found to be associated with PPD were: physical or sexual abuse, delivery by cesarean section, history of past psychiatric illness, and family history of past psychiatric illness; while marriage with no previous agreement, and high socio-economic level were associated with lower levels of PPD. The Kurdish version of the EPDS can be successfully used to screen depression in a Kurdish population of puerperal women.

Constructive thinking, rational intelligence and irritable bowel syndrome

PubMed Central

Rey, Enrique; Ortega, Marta Moreno; Alonso, Monica Olga Garcia; Diaz-Rubio, Manuel

2009-01-01

AIM: To evaluate rational and experiential intelligence in irritable bowel syndrome (IBS) sufferers. METHODS: We recruited 100 subjects with IBS as per Rome II criteria (50 consulters and 50 non-consulters) and 100 healthy controls, matched by age, sex and educational level. Cases and controls completed a clinical questionnaire (including symptom characteristics and medical consultation) and the following tests: rational-intelligence (Wechsler Adult Intelligence Scale, 3rd edition); experiential-intelligence (Constructive Thinking Inventory); personality (NEO personality inventory); psychopathology (MMPI-2), anxiety (state-trait anxiety inventory) and life events (social readjustment rating scale). Analysis of variance was used to compare the test results of IBS-sufferers and controls, and a logistic regression model was then constructed and adjusted for age, sex and educational level to evaluate any possible association with IBS. RESULTS: No differences were found between IBS cases and controls in terms of IQ (102.0 ± 10.8 vs 102.8 ± 12.6), but IBS sufferers scored significantly lower in global constructive thinking (43.7 ± 9.4 vs 49.6 ± 9.7). In the logistic regression model, global constructive thinking score was independently linked to suffering from IBS [OR 0.92 (0.87-0.97)], without significant OR for total IQ. CONCLUSION: IBS subjects do not show lower rational intelligence than controls, but lower experiential intelligence is nevertheless associated with IBS. PMID:19575489

Associations of health behaviors on depressive symptoms among employed men in Japan.

PubMed

Wada, Koji; Satoh, Toshihiko; Tsunoda, Masashi; Aizawa, Yoshiharu

2006-07-01

The associations between health behaviors and depressive symptoms have been demonstrated in many studies. However, job strain has also been associated with health behaviors. The aim of this study was to analyze whether health behaviors such as physical activity, sleeping, smoking and alcohol intake are associated with depressive symptoms after adjusting for job strain. Workers were recruited from nine companies and factories located in east and central areas of Japan. The Center for Epidemiologic Studies Depression (CES-D) Scale was used to assess depressive symptoms. Psychological demand and control (decision-latitude) at work were measured with the Job Content Questionnaire. Multiple logistic regression analysis was used to determine the independent contribution of each health behavior to depressive symptoms. Among the total participants, 3,748 (22.7%) had depressive symptoms, which was defined as scoring 16 or higher on the CES-D scale. Using the multiple logistic regression analysis, depressive symptoms were significantly associated with physical activity less than once a week (adjusted relative risk [ARR] = 1.18, 95% confidence interval [CI], 1.14 to 1.25) and daily hours of sleep of 6 h or less (ARR, 1.25; 95% CI, 1.14 to 1.35). Smoking and frequency of alcohol intake were not significantly associated with depressive symptoms. This study suggests some health behaviors such as physical activity or daily hours of sleep are associated with depressive symptoms after adjusting for job strain.

Early Predictors of Fever in Patients with Aneurysmal Subarachnoid Hemorrhage.

PubMed

Rocha Ferreira da Silva, Ivan; Rodriguez de Freitas, Gabriel

2016-12-01

Fever is commonly observed in patients who have had aneurysmal subarachnoid hemorrhage (SAH), and it has been associated with the occurrence of delayed cerebral ischemia and worse outcomes in previous studies. Frequently, fever is not the result of bacterial infections, and distinction between infection-related fever and fever secondary to brain injury (also referred as central fever) can be challenging. The current study aimed to identify risk factors on admission for the development of central fever in patients with SAH. Databank analysis was performed using information from demographic data (age, gender), imaging (transcranial Doppler ultrasound, computed tomography, and cerebral angiogram), laboratory (white blood cell count, hemoglobin, renal function, and electrolytes), and clinical assessment (Hunt-Hess and modified Fisher scales on admission, occurrence of fever). A multivariate logistic regression model was created. Of 55 patients, 32 developed fever during the first 7 days of hospital stay (58%). None of the patients had identifiable bacterial infections during their first week in the neurocritical care unit. Hunt-Hess scale >2 and leukocytosis on admission were associated to the development of central fever, even after correction in a logistic regression model. Leukocytosis and a poor neurologic examination on admission might help predict which subset of patients with SAH are at higher risk of developing central fever early in their hospital stay. Copyright © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Performance of the score systems Acute Physiology and Chronic Health Evaluation II and III at an interdisciplinary intensive care unit, after customization

PubMed Central

Markgraf, Rainer; Deutschinoff, Gerd; Pientka, Ludger; Scholten, Theo; Lorenz, Cristoph

2001-01-01

Background: Mortality predictions calculated using scoring scales are often not accurate in populations other than those in which the scales were developed because of differences in case-mix. The present study investigates the effect of first-level customization, using a logistic regression technique, on discrimination and calibration of the Acute Physiology and Chronic Health Evaluation (APACHE) II and III scales. Method: Probabilities of hospital death for patients were estimated by applying APACHE II and III and comparing these with observed outcomes. Using the split sample technique, a customized model to predict outcome was developed by logistic regression. The overall goodness-of-fit of the original and the customized models was assessed. Results: Of 3383 consecutive intensive care unit (ICU) admissions over 3 years, 2795 patients could be analyzed, and were split randomly into development and validation samples. The discriminative powers of APACHE II and III were unchanged by customization (areas under the receiver operating characteristic [ROC] curve 0.82 and 0.85, respectively). Hosmer-Lemeshow goodness-of-fit tests showed good calibration for APACHE II, but insufficient calibration for APACHE III. Customization improved calibration for both models, with a good fit for APACHE III as well. However, fit was different for various subgroups. Conclusions: The overall goodness-of-fit of APACHE III mortality prediction was improved significantly by customization, but uniformity of fit in different subgroups was not achieved. Therefore, application of the customized model provides no advantage, because differences in case-mix still limit comparisons of quality of care. PMID:11178223

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Modification of the Mantel-Haenszel and Logistic Regression DIF Procedures to Incorporate the SIBTEST Regression Correction

ERIC Educational Resources Information Center

DeMars, Christine E.

2009-01-01

The Mantel-Haenszel (MH) and logistic regression (LR) differential item functioning (DIF) procedures have inflated Type I error rates when there are large mean group differences, short tests, and large sample sizes.When there are large group differences in mean score, groups matched on the observed number-correct score differ on true score,…

Predictive factors of alcohol and tobacco use in adolescents.

PubMed

Alvarez-Aguirre, Alicia; Alonso-Castillo, María Magdalena; Zanetti, Ana Carolina Guidorizzi

2014-01-01

to analyze the effect of self-esteem, assertiveness, self-efficacy and resiliency on alcohol and tobacco consumption in adolescents. a descriptive and correlational study was undertaken with 575 adolescents in 2010. The Self-Esteem Scale, the Situational Confidence Scale, the Assertiveness Questionnaire and the Resiliency Scale were used. the adjustment of the logistic regression model, considering age, sex, self-esteem, assertiveness, self-efficacy and resiliency, demonstrates significance in the consumption of alcohol and tobacco. Age, resiliency and assertiveness predict alcohol consumption in the lifetime and assertiveness predicts alcohol consumption in the last year. Similarly, age and sex predict tobacco consumption in the lifetime and age in the last year. this study can offer important information to plan nursing interventions involving adolescent alcohol and tobacco users.

Satellite rainfall retrieval by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.

1986-01-01

The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.

Bias in discriminating very mild dementia for older adults with different levels of education in Hong Kong.

PubMed

Chang, Jianfang; Tse, Chi-Shing; Leung, Grace Tak Yu; Fung, Ada Wai Tung; Hau, Kit-Tai; Chiu, Helen Fung Kum; Lam, Linda Chiu Wa

2014-06-01

Education has a profound effect on older adults' cognitive performance. In Hong Kong, some dementia screening tasks were originally designed for developed population with, on average, higher education. We compared the screening power of these tasks for Chinese older adults with different levels of education. Community-dwelling older adults who were healthy (N = 383) and with very mild dementia (N = 405) performed the following tasks: Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-Cognitive subscales, Verbal Fluency, Abstract Thinking, and Visual/Digit Span. Logistic regression was used to examine the power of these tasks to predict Clinical Dementia Rating (CDR 0.5 vs. 0). Logistic regression analysis showed that while the screening power of the total scores in all tasks was similar for high and low education groups, there were education biases in some items of these tasks. The differential screening power in high and low education groups was not identical across items in some tasks. Thus, in cognitive assessments, we should exercise great caution when using these potentially biased items for older adults with limited education.

Alcohol and tobacco use and cognitive-motivational variables in school settings: effects on academic performance in Spanish adolescents.

PubMed

Inglés, Cándido J; Torregrosa, María S; Rodríguez-Marín, Jesús; García del Castillo, José A; Gázquez, José J; García-Fernández, José M; Delgado, Beatriz

2013-01-01

The aim of the present study was to analyze: (a) the relationship between alcohol and tobacco use and academic performance, and (b) the predictive role of psycho-educational factors and alcohol and tobacco abuse on academic performance in a sample of 352 Spanish adolescents from grades 8 to 10 of Compulsory Secondary Education. The Self-Description Questionnaire-II, the Sydney Attribution Scale, and the Achievement Goal Tendencies Questionnaire were administered in order to analyze cognitive-motivational variables. Alcohol and tobacco abuse, sex, and grade retention were also measured using self-reported questions. Academic performance was measured by school records. Frequency analyses and logistic regression analyses were used. Frequency analyses revealed that students who abuse of tobacco and alcohol show a higher rate of poor academic performance. Logistic regression analyses showed that health behaviours, and educational and cognitive-motivational variables exert a different effect on academic performance depending on the academic area analyzed. These results point out that not only academic, but also health variables should be address to improve academic performance in adolescence.

Gender differences in social support and leisure-time physical activity.

PubMed

Oliveira, Aldair J; Lopes, Claudia S; Rostila, Mikael; Werneck, Guilherme Loureiro; Griep, Rosane Härter; Leon, Antônio Carlos Monteiro Ponce de; Faerstein, Eduardo

2014-08-01

To identify gender differences in social support dimensions' effect on adults' leisure-time physical activity maintenance, type, and time. Longitudinal study of 1,278 non-faculty public employees at a university in Rio de Janeiro, RJ, Southeastern Brazil. Physical activity was evaluated using a dichotomous question with a two-week reference period, and further questions concerning leisure-time physical activity type (individual or group) and time spent on the activity. Social support was measured with the Medical Outcomes Study Social Support Scale. For the analysis, logistic regression models were adjusted separately by gender. A multinomial logistic regression showed an association between material support and individual activities among women (OR = 2.76; 95%CI 1.2;6.5). Affective support was associated with time spent on leisure-time physical activity only among men (OR = 1.80; 95%CI 1.1;3.2). All dimensions of social support that were examined influenced either the type of, or the time spent on, leisure-time physical activity. In some social support dimensions, the associations detected varied by gender. Future studies should attempt to elucidate the mechanisms involved in these gender differences.

Practical Session: Logistic Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

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

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test ofmore » the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.« less

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

NASA Astrophysics Data System (ADS)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

2015-10-01

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

PubMed

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

Frontal lobe function and behavioral changes in amyotrophic lateral sclerosis: a study from Southwest China.

PubMed

Wei, QianQian; Chen, XuePing; Zheng, ZhenZhen; Huang, Rui; Guo, XiaoYan; Cao, Bei; Zhao, Bi; Shang, Hui-Fang

2014-12-01

Despite growing interest, the frequency and characteristics of frontal lobe functional and behavioral deficits in Chinese people with amyotrophic lateral sclerosis (ALS), as well as their impact on the survival of ALS patients, remain unknown. The Chinese version of the frontal assessment battery (FAB) and frontal behavioral inventory (FBI) were used to evaluate 126 sporadic ALS patients and 50 healthy controls. The prevalence of frontal lobe dysfunction was 32.5%. The most notable impairment domain of the FAB was lexical fluency (30.7%). The binary logistic regression model revealed that an onset age older than 45 years (OR 5.976, P = 0.002) and a lower educational level (OR 0.858, P = 0.002) were potential determinants of an abnormal FAB. Based on the FBI score, 46.0% of patients showed varied degrees of frontal behavioral changes. The most common impaired neurobehavioral domains were irritability (25.4%), logopenia (20.6%) and apathy (19.0%). The binary logistic regression model revealed that the ALS Functional Rating Scale-Revised scale score (OR 0.127, P = 0.001) was a potential determinant of an abnormal FBI. Frontal functional impairment and the severity of frontal behavioral changes were not associated with the survival status or the progression of ALS by the cox proportional hazard model and multivariate regression analyses, respectively. Frontal lobe dysfunction and frontal behavioral changes are common in Chinese ALS patients. Frontal lobe dysfunction may be related to the onset age and educational level. The severity of frontal behavioral changes may be associated with the ALSFRS-R. However, the frontal functional impairment and the frontal behavioral changes do not worsen the progression or survival of ALS.

Health and Nutrition Literacy and Adherence to Treatment in Children, Adolescents, and Young Adults With Chronic Kidney Disease and Hypertension, North Carolina, 2015

PubMed Central

Ferris, Maria; Rak, Eniko

2016-01-01

Introduction Adherence to treatment and dietary restrictions is important for health outcomes of patients with chronic/end-stage kidney disease and hypertension. The relationship of adherence with nutritional and health literacy in children, adolescents, and young adults is not well understood. The current study examined the relationship of health literacy, nutrition knowledge, nutrition knowledge–behavior concordance, and medication adherence in a sample of children and young people with chronic/end-stage kidney disease and hypertension. Methods We enrolled 74 patients (aged 7–29 y) with a diagnosis of chronic/end-stage kidney disease and hypertension from the University of North Carolina Kidney Center. Participants completed instruments of nutrition literacy (Disease-Specific Nutrition Knowledge Test), health literacy (Newest Vital Sign), nutrition behavior (Nutrition Knowledge–Behavior Concordance Scale), and medication adherence (Morisky Medication Adherence Scale). Linear and binary logistic regressions were used to test the associations. Results In univariate comparisons, nutrition knowledge was significantly higher in people with adequate health literacy. Medication adherence was related to nutrition knowledge and nutrition knowledge–behavior concordance. Multivariate regression models demonstrated that knowledge of disease-specific nutrition restrictions did not significantly predict nutrition knowledge–behavior concordance scores. In logistic regression, knowledge of nutrition restrictions did not significantly predict medication adherence. Lastly, health literacy and nutrition knowledge–behavior concordance were significant predictors of medication adherence. Conclusion Nutrition knowledge and health literacy skills are positively associated. Nutrition knowledge, health literacy, and nutrition knowledge–behavior concordance are positively related to medication adherence. Future research should focus on additional factors that may predict disease-specific nutrition behavior (adherence to dietary restrictions) in children and young people with chronic conditions. PMID:27490366

Short-term quality of life after subthalamic stimulation depends on non-motor symptoms in Parkinson's disease.

PubMed

Dafsari, Haidar Salimi; Weiß, Luisa; Silverdale, Monty; Rizos, Alexandra; Reddy, Prashanth; Ashkan, Keyoumars; Evans, Julian; Reker, Paul; Petry-Schmelzer, Jan Niklas; Samuel, Michael; Visser-Vandewalle, Veerle; Antonini, Angelo; Martinez-Martin, Pablo; Ray-Chaudhuri, K; Timmermann, Lars

2018-02-24

Subthalamic nucleus (STN) deep brain stimulation (DBS) improves quality of life (QoL), motor, and non-motor symptoms (NMS) in advanced Parkinson's disease (PD). However, considerable inter-individual variability has been observed for QoL outcome. We hypothesized that demographic and preoperative NMS characteristics can predict postoperative QoL outcome. In this ongoing, prospective, multicenter study (Cologne, Manchester, London) including 88 patients, we collected the following scales preoperatively and on follow-up 6 months postoperatively: PDQuestionnaire-8 (PDQ-8), NMSScale (NMSS), NMSQuestionnaire (NMSQ), Scales for Outcomes in PD (SCOPA)-motor examination, -complications, and -activities of daily living, levodopa equivalent daily dose. We dichotomized patients into "QoL responders"/"non-responders" and screened for factors associated with QoL improvement with (1) Spearman-correlations between baseline test scores and QoL improvement, (2) step-wise linear regressions with baseline test scores as independent and QoL improvement as dependent variables, (3) logistic regressions using aforementioned "responders/non-responders" as dependent variable. All outcomes improved significantly on follow-up. However, approximately 44% of patients were categorized as "QoL non-responders". Spearman-correlations, linear and logistic regression analyses were significant for NMSS and NMSQ but not for SCOPA-motor examination. Post-hoc, we identified specific NMS (flat moods, difficulties experiencing pleasure, pain, bladder voiding) as significant contributors to QoL outcome. Our results provide evidence that QoL improvement after STN-DBS depends on preoperative NMS characteristics. These findings are important in the advising and selection of individuals for DBS therapy. Future studies investigating motor and non-motor PD clusters may enable stratifying QoL outcomes and help predict patients' individual prospects of benefiting from DBS. Copyright © 2018. Published by Elsevier Inc.

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

PubMed

Vaeth, Michael; Skovlund, Eva

2004-06-15

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

Insight and suicidality in psychosis: A cross-sectional study.

PubMed

Massons, Carmen; Lopez-Morinigo, Javier-David; Pousa, Esther; Ruiz, Ada; Ochoa, Susana; Usall, Judith; Nieto, Lourdes; Cobo, Jesus; David, Anthony S; Dutta, Rina

2017-06-01

We aimed to test whether specific insight dimensions are associated with suicidality in patients with psychotic disorders. 143 patients with schizophrenia spectrum disorders were recruited. Suicidality was assessed by item 8 of the Calgary Depression Scale for Schizophrenia (CDSS). Insight was measured by the Scale of Unawareness of Mental Disorder (SUMD) and the Markova and Berrios Insight Scale. Bivariate analyses and multivariable logistic regression models were conducted. Those subjects aware of having a mental illness and its social consequences had higher scores on suicidality than those with poor insight. Awareness of the need for treatment was not linked with suicidality. The Markova and Berrios Insight scale total score and two specific domains (awareness of "disturbed thinking and loss of control over the situation" and "having a vague feeling that something is wrong") were related to suicidality. However, no insight dimensions survived the multivariable regression model, which found depression and previous suicidal behaviour to predict suicidality. Suicidality in psychosis was linked with some insight dimensions: awareness of mental illness and awareness of social consequences, but not compliance. Depression and previous suicidal behaviour mediated the associations with insight; thus, predicting suicidality. Copyright © 2017. Published by Elsevier B.V.

Does clinical pretest probability influence image quality and diagnostic accuracy in dual-source coronary CT angiography?

PubMed

Thomas, Christoph; Brodoefel, Harald; Tsiflikas, Ilias; Bruckner, Friederike; Reimann, Anja; Ketelsen, Dominik; Drosch, Tanja; Claussen, Claus D; Kopp, Andreas; Heuschmid, Martin; Burgstahler, Christof

2010-02-01

To prospectively evaluate the influence of the clinical pretest probability assessed by the Morise score onto image quality and diagnostic accuracy in coronary dual-source computed tomography angiography (DSCTA). In 61 patients, DSCTA and invasive coronary angiography were performed. Subjective image quality and accuracy for stenosis detection (>50%) of DSCTA with invasive coronary angiography as gold standard were evaluated. The influence of pretest probability onto image quality and accuracy was assessed by logistic regression and chi-square testing. Correlations of image quality and accuracy with the Morise score were determined using linear regression. Thirty-eight patients were categorized into the high, 21 into the intermediate, and 2 into the low probability group. Accuracies for the detection of significant stenoses were 0.94, 0.97, and 1.00, respectively. Logistic regressions and chi-square tests showed statistically significant correlations between Morise score and image quality (P < .0001 and P < .001) and accuracy (P = .0049 and P = .027). Linear regression revealed a cutoff Morise score for a good image quality of 16 and a cutoff for a barely diagnostic image quality beyond the upper Morise scale. Pretest probability is a weak predictor of image quality and diagnostic accuracy in coronary DSCTA. A sufficient image quality for diagnostic images can be reached with all pretest probabilities. Therefore, coronary DSCTA might be suitable also for patients with a high pretest probability. Copyright 2010 AUR. Published by Elsevier Inc. All rights reserved.

Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants.

PubMed

Kesselmeier, Miriam; Lorenzo Bermejo, Justo

2017-11-01

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package 'robustbase' with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Prediction of Large Vessel Occlusions in Acute Stroke: National Institute of Health Stroke Scale Is Hard to Beat.

PubMed

Vanacker, Peter; Heldner, Mirjam R; Amiguet, Michael; Faouzi, Mohamed; Cras, Patrick; Ntaios, George; Arnold, Marcel; Mattle, Heinrich P; Gralla, Jan; Fischer, Urs; Michel, Patrik

2016-06-01

Endovascular treatment for acute ischemic stroke with a large vessel occlusion was recently shown to be effective. We aimed to develop a score capable of predicting large vessel occlusion eligible for endovascular treatment in the early hospital management. Retrospective, cohort study. Two tertiary, Swiss stroke centers. Consecutive acute ischemic stroke patients (1,645 patients; Acute STroke Registry and Analysis of Lausanne registry), who had CT angiography within 6 and 12 hours of symptom onset, were categorized according to the occlusion site. Demographic and clinical information was used in logistic regression analysis to derive predictors of large vessel occlusion (defined as intracranial carotid, basilar, and M1 segment of middle cerebral artery occlusions). Based on logistic regression coefficients, an integer score was created and validated internally and externally (848 patients; Bernese Stroke Registry). None. Large vessel occlusions were present in 316 patients (21%) in the derivation and 566 (28%) in the external validation cohort. Five predictors added significantly to the score: National Institute of Health Stroke Scale at admission, hemineglect, female sex, atrial fibrillation, and no history of stroke and prestroke handicap (modified Rankin Scale score, < 2). Diagnostic accuracy in internal and external validation cohorts was excellent (area under the receiver operating characteristic curve, 0.84 both). The score performed slightly better than National Institute of Health Stroke Scale alone regarding prediction error (Wilcoxon signed rank test, p < 0.001) and regarding discriminatory power in derivation and pooled cohorts (area under the receiver operating characteristic curve, 0.81 vs 0.80; DeLong test, p = 0.02). Our score accurately predicts the presence of emergent large vessel occlusions, which are eligible for endovascular treatment. However, incorporation of additional demographic and historical information available on hospital arrival provides minimal incremental predictive value compared with the National Institute of Health Stroke Scale alone.

Endovascular Therapy Is Effective and Safe for Patients With Severe Ischemic Stroke: Pooled Analysis of Interventional Management of Stroke III and Multicenter Randomized Clinical Trial of Endovascular Therapy for Acute Ischemic Stroke in the Netherlands Data.

PubMed

Broderick, Joseph P; Berkhemer, Olvert A; Palesch, Yuko Y; Dippel, Diederik W J; Foster, Lydia D; Roos, Yvo B W E M; van der Lugt, Aad; Tomsick, Thomas A; Majoie, Charles B L M; van Zwam, Wim H; Demchuk, Andrew M; van Oostenbrugge, Robert J; Khatri, Pooja; Lingsma, Hester F; Hill, Michael D; Roozenbeek, Bob; Jauch, Edward C; Jovin, Tudor G; Yan, Bernard; von Kummer, Rüdiger; Molina, Carlos A; Goyal, Mayank; Schonewille, Wouter J; Mazighi, Mikael; Engelter, Stefan T; Anderson, Craig S; Spilker, Judith; Carrozzella, Janice; Ryckborst, Karla J; Janis, L Scott; Simpson, Kit N

2015-12-01

We assessed the effect of endovascular treatment in acute ischemic stroke patients with severe neurological deficit (National Institutes of Health Stroke Scale score, ≥20) after a prespecified analysis plan. The pooled analysis of the Interventional Management of Stroke III (IMS III) and Multicenter Randomized Clinical Trial of Endovascular Therapy for Acute Ischemic Stroke in the Netherlands (MR CLEAN) trials included participants with an National Institutes of Health Stroke Scale score of ≥20 before intravenous tissue-type plasminogen activator (tPA) treatment (IMS III) or randomization (MR CLEAN) who were treated with intravenous tPA ≤3 hours of stroke onset. Our hypothesis was that participants with severe stroke randomized to endovascular therapy after intravenous tPA would have improved 90-day outcome (distribution of modified Rankin Scale scores), when compared with those who received intravenous tPA alone. Among 342 participants in the pooled analysis (194 from IMS III and 148 from MR CLEAN), an ordinal logistic regression model showed that the endovascular group had superior 90-day outcome compared with the intravenous tPA group (adjusted odds ratio, 1.78; 95% confidence interval, 1.20-2.66). In the logistic regression model of the dichotomous outcome (modified Rankin Scale score, 0-2, or functional independence), the endovascular group had superior outcomes (adjusted odds ratio, 1.97; 95% confidence interval, 1.09-3.56). Functional independence (modified Rankin Scale score, ≤2) at 90 days was 25% in the endovascular group when compared with 14% in the intravenous tPA group. Endovascular therapy after intravenous tPA within 3 hours of symptom onset improves functional outcome at 90 days after severe ischemic stroke. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00359424 (IMS III) and ISRCTN10888758 (MR CLEAN). © 2015 American Heart Association, Inc.

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction.

PubMed

Che Azemin, M Z; Kumar, Dinesh K; Wong, T Y; Wang, J J; Kawasaki, R; Mitchell, P; Arjunan, Sridhar P

2010-01-01

In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation, Receivers Operating Characteristics (ROC) analysis and medical prediction statistics, odds ratio. Stroke prediction model was developed using the proposed system.

Parenting styles and alcohol consumption among Brazilian adolescents.

PubMed

Paiva, Fernando Santana; Bastos, Ronaldo Rocha; Ronzani, Telmo Mota

2012-10-01

This study evaluates the correlation between alcohol consumption in adolescence and parenting styles of socialization among Brazilian adolescents. The sample was composed of 273 adolescents, 58% whom were males. Instruments were: 1) Sociodemographic Questionnaire; 2) Demand and Responsiveness Scales; 3) Drug Use Screening Inventory (DUSI). Study analyses employed multiple correspondence analysis and logistic regression. Maternal, but not paternal, authoritative and authoritarian parenting styles were directly related to adolescent alcohol intake. The style that mothers use to interact with their children may influence uptake of high-risk behaviors.

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

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.

Comparison of cranial sex determination by discriminant analysis and logistic regression.

PubMed

Amores-Ampuero, Anabel; Alemán, Inmaculada

2016-04-05

Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

PubMed

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

Preisser, John S.; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.

2013-01-01

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts. PMID:22777960

Biomass Stoves and Lens Opacity and Cataract in Nepalese Women

PubMed Central

Pokhrel, Amod K.; Bates, Michael N.; Shrestha, Sachet P.; Bailey, Ian L.; DiMartino, Robert B.; Smith, Kirk R.; Joshi, N. D.

2014-01-01

Purpose Cataract is the most prevalent cause of blindness in Nepal. Several epidemiologic studies have associated cataracts with use of biomass cookstoves. These studies, however, have had limitations, including potential control selection bias and limited adjustment for possible confounding. This study, in Pokhara city, in an area of Nepal where biomass cookstoves are widely used without direct venting of the smoke to the outdoors, focuses on pre-clinical measures of opacity, while avoiding selection bias and taking into account comprehensive data on potential confounding factors Methods Using a cross-sectional study design, severity of lenticular damage, judged on the LOCS III scales, was investigated in females (n=143), aged 20-65 years, without previously diagnosed cataract. Linear and logistic regression analyses were used to examine the relationships with stove type and length of use. Clinically significant cataract, used in the logistic regression models, was defined as a LOCS III score > 2. Results Using gas cookstoves as the reference group, logistic regression analysis for nuclear cataract showed the evidence of relationships with stove type: for biomass stoves, the odds ratio (OR) was 2.58 (95% confidence interval [CI]: 1.22-5.46) and, for kerosene stoves, the OR was 5.18 (95% CI: 0.88-30.38). Similar results were found for nuclear color (LOCS III score > 2), but no association was found with cortical cataracts. Supporting a relationship between biomass stoves and nuclear cataract was a trend with years of exposure to biomass cookstoves (p=0.01). Linear regression analyses did not show clear evidence of an association between lenticular damage and stove types. Biomass fuel used for heating was not associated with any form of opacity. Conclusions This study provides support for associations of biomass and kerosene cookstoves with nuclear opacity and change in nuclear color. The novel associations with kerosene cookstove use deserve further investigation. PMID:23400024

An examination of the MASC Social Anxiety Scale in a non-referred sample of adolescents.

PubMed

Anderson, Emily R; Jordan, Judith A; Smith, Ashley J; Inderbitzen-Nolan, Heidi M

2009-12-01

Social phobia is prevalent during adolescence and is associated with negative outcomes. Two self-report instruments are empirically validated to specifically assess social phobia symptomatology in youth: the Social Phobia and Anxiety Inventory for Children and the Social Anxiety Scale for Adolescents. The Multidimensional Anxiety Scale for Children is a broad-band measure of anxiety containing a scale assessing the social phobia construct. The present study investigated the MASC Social Anxiety Scale in relation to other well-established measures of social phobia and depression in a non-referred sample of adolescents. Results support the convergent validity of the MASC Social Anxiety Scale and provide some support for its discriminant validity, suggesting its utility in the initial assessment of social phobia. Receiver Operating Characteristics (ROCs) calculated the sensitivity and specificity of the MASC Social Anxiety Scale. Binary logistic regression analyses determined the predictive utility of the MASC Social Anxiety Scale. Implications for assessment are discussed.

Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results

ERIC Educational Resources Information Center

Osborne, Jason W.

2012-01-01

Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These…

An 8-year study of people with multiple sclerosis in Isfahan, Iran: Association between environmental air pollutants and severity of disease.

PubMed

Ashtari, Fereshte; Esmaeil, Nafiseh; Mansourian, Marjan; Poursafa, Parinaz; Mirmosayyeb, Omid; Barzegar, Mahdi; Pourgheisari, Hajar

2018-06-15

The evidence for an impact of ambient air pollution on the incidence and severity of multiple sclerosis (MS) is still limited. In the present study, we assessed the association between daily air pollution levels and MS prevalence and severity in Isfahan city, Iran. Data related to MS patients has been collected from 2008 to 2016 in a referral university clinic. The air quality index (AQI) data, were collected from 6 monitoring stations of Isfahan department of environment. The distribution map presenting the sites of air pollution monitoring stations as well as the residential address of MS patients was plotted on geographical information system (GIS). An increase in AQI level in four areas of the city (north, west, east and south) was associated with higher expanded disability status scale (EDSS) of MS patients[logistic regression odds ratio = 1.01 (95% CI = 1.008,1.012)]. Moreover, significant inverse association between the complete remission after the first attack with AQI level in total areas [logistic regression odds ratio = 0.987 (95% CI = 0.977, 0.997)] was found in crude model. However, after adjustment for confounding variables through multivariate logistic regression, AQI level was associated with degree of complete remission after first attack 1.005 (95% CI = 1.004, 1.006). The results of our study suggest that air pollution could play a role in the severity and remission of MS disease. However, more detailed studies with considering the complex involvement of different environmental factors including sunlight exposure, diet, depression and vitamin D are needed to determine the outcome of MS. Copyright © 2018 Elsevier B.V. All rights reserved.

Nailfold capillaroscopy for prediction of novel future severe organ involvement in systemic sclerosis.

PubMed

Smith, Vanessa; Riccieri, Valeria; Pizzorni, Carmen; Decuman, Saskia; Deschepper, Ellen; Bonroy, Carolien; Sulli, Alberto; Piette, Yves; De Keyser, Filip; Cutolo, Maurizio

2013-12-01

Assessment of associations of nailfold videocapillaroscopy (NVC) scleroderma (systemic sclerosis; SSc) ("early," "active," and "late") with novel future severe clinical involvement in 2 independent cohorts. Sixty-six consecutive Belgian and 82 Italian patients with SSc underwent NVC at baseline. Images were blindly assessed and classified into normal, early, active, or late NVC pattern. Clinical evaluation was performed for 9 organ systems (general, peripheral vascular, skin, joint, muscle, gastrointestinal tract, lung, heart, and kidney) according to the Medsger disease severity scale (DSS) at baseline and in the future (18-24 months of followup). Severe clinical involvement was defined as category 2 to 4 per organ of the DSS. Logistic regression analysis (continuous NVC predictor variable) was performed. The OR to develop novel future severe organ involvement was stronger according to more severe NVC patterns and similar in both cohorts. In simple logistic regression analysis the OR in the Belgian/Italian cohort was 2.16 (95% CI 1.19-4.47, p = 0.010)/2.33 (95% CI 1.36-4.22, p = 0.002) for the early NVC SSc pattern, 4.68/5.42 for the active pattern, and 10.14/12.63 for the late pattern versus the normal pattern. In multiple logistic regression analysis, adjusting for disease duration, subset, and vasoactive medication, the OR was 2.99 (95% CI 1.31-8.82, p = 0.007)/1.88 (95% CI 1.00-3.71, p = 0.050) for the early NVC SSc pattern, 8.93/3.54 for the active pattern, and 26.69/6.66 for the late pattern versus the normal pattern. Capillaroscopy may be predictive of novel future severe organ involvement in SSc, as attested by 2 independent cohorts.

Intermediate and advanced topics in multilevel logistic regression analysis

PubMed Central

Merlo, Juan

2017-01-01

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher‐level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within‐cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population‐average effect of covariates measured at the subject and cluster level, in contrast to the within‐cluster or cluster‐specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster‐level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28543517

Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

Austin, Peter C; Merlo, Juan

2017-09-10

Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R 2 measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Predicting Social Trust with Binary Logistic Regression

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph; Hufstedler, Shirley

2015-01-01

This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…

Effect of folic acid on appetite in children: ordinal logistic and fuzzy logistic regressions.

PubMed

Namdari, Mahshid; Abadi, Alireza; Taheri, S Mahmoud; Rezaei, Mansour; Kalantari, Naser; Omidvar, Nasrin

2014-03-01

Reduced appetite and low food intake are often a concern in preschool children, since it can lead to malnutrition, a leading cause of impaired growth and mortality in childhood. It is occasionally considered that folic acid has a positive effect on appetite enhancement and consequently growth in children. The aim of this study was to assess the effect of folic acid on the appetite of preschool children 3 to 6 y old. The study sample included 127 children ages 3 to 6 who were randomly selected from 20 preschools in the city of Tehran in 2011. Since appetite was measured by linguistic terms, a fuzzy logistic regression was applied for modeling. The obtained results were compared with a statistical ordinal logistic model. After controlling for the potential confounders, in a statistical ordinal logistic model, serum folate showed a significantly positive effect on appetite. A small but positive effect of folate was detected by fuzzy logistic regression. Based on fuzzy regression, the risk for poor appetite in preschool children was related to the employment status of their mothers. In this study, a positive association was detected between the levels of serum folate and improved appetite. For further investigation, a randomized controlled, double-blind clinical trial could be helpful to address causality. Copyright © 2014 Elsevier Inc. All rights reserved.

A new predictive indicator for development of pressure ulcers in bedridden patients based on common laboratory tests results.

PubMed

Hatanaka, N; Yamamoto, Y; Ichihara, K; Mastuo, S; Nakamura, Y; Watanabe, M; Iwatani, Y

2008-04-01

Various scales have been devised to predict development of pressure ulcers on the basis of clinical and laboratory data, such as the Braden Scale (Braden score), which is used to monitor activity and skin conditions of bedridden patients. However, none of these scales facilitates clinically reliable prediction. To develop a clinical laboratory data-based predictive equation for the development of pressure ulcers. Subjects were 149 hospitalised patients with respiratory disorders who were monitored for the development of pressure ulcers over a 3-month period. The proportional hazards model (Cox regression) was used to analyse the results of 12 basic laboratory tests on the day of hospitalisation in comparison with Braden score. Pressure ulcers developed in 38 patients within the study period. A Cox regression model consisting solely of Braden scale items showed that none of these items contributed to significantly predicting pressure ulcers. Rather, a combination of haemoglobin (Hb), C-reactive protein (CRP), albumin (Alb), age, and gender produced the best model for prediction. Using the set of explanatory variables, we created a new indicator based on a multiple logistic regression equation. The new indicator showed high sensitivity (0.73) and specificity (0.70), and its diagnostic power was higher than that of Alb, Hb, CRP, or the Braden score alone. The new indicator may become a more useful clinical tool for predicting presser ulcers than Braden score. The new indicator warrants verification studies to facilitate its clinical implementation in the future.

Predictive factors of alcohol and tobacco use in adolescents

PubMed Central

Alvarez-Aguirre, Alicia; Alonso-Castillo, María Magdalena; Zanetti, Ana Carolina Guidorizzi

2014-01-01

OBJECTIVES: to analyze the effect of self-esteem, assertiveness, self-efficacy and resiliency on alcohol and tobacco consumption in adolescents. METHOD: a descriptive and correlational study was undertaken with 575 adolescents in 2010. The Self-Esteem Scale, the Situational Confidence Scale, the Assertiveness Questionnaire and the Resiliency Scale were used. RESULTS: the adjustment of the logistic regression model, considering age, sex, self-esteem, assertiveness, self-efficacy and resiliency, demonstrates significance in the consumption of alcohol and tobacco. Age, resiliency and assertiveness predict alcohol consumption in the lifetime and assertiveness predicts alcohol consumption in the last year. Similarly, age and sex predict tobacco consumption in the lifetime and age in the last year. CONCLUSION: this study can offer important information to plan nursing interventions involving adolescent alcohol and tobacco users. PMID:25591103

Psychological distress among low-income U.S.- and foreign-born women of Mexican descent: impact of acculturation.

PubMed

Bekteshi, Venera; Xu, Qingwen; Van Tran, Thanh

2015-01-01

After testing the capacity of Kessler's psychological distress (K6) scale to measure equally across low-income Mexican-born women (n=881) and U.S.-born women of Mexican descent (n=317), this study assesses the impact of acculturation on this group's psychological distress. We employ descriptive and confirmatory factor analyses to test the cross-cultural equivalence of K6. Multivariate and logistic regression is used to test the association between acculturation and psychological distress among low-income, Mexican-American women. The cross-cultural equivalence analysis shows that some of the scale's items have the capacity to measure psychological distress equally among participants. Regression results indicate that the more acculturated these women become, the greater their psychological distress is. The study recommends that researchers emphasize the cross-cultural equivalence of their measures and suggests a heightened awareness among practitioners of the multidimensional impact of acculturation on clients of Mexican descent. Copyright © 2015 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

[Contraceptive self efficacy in male and female adolescents: validation of the French version of the Levinson scale].

PubMed

Bilodeau, A; Forget, G; Tétreault, J

1994-01-01

The social learning theory of Bandura leads us to believe that contraceptive self-efficacy supports the adoption and the maintenance of effective contraceptive behaviours during the teenage years. Levinson has developed a validated measure of this concept which consists of an 18-item scale for sexually active girls. However there are no such scales in French or for sexually active boys. The health promotion program, entitled SEXPRIMER, which aims at reducing teenage pregnancy, has incorporated the French version of the Levinson scale, the adapted boy's version and the validity studies. A 15-item scale for girls and a 14-item scale for boys with respective reliability coefficients of .78 and .71 resulted from this program. A logistic regression analysis shows the predictive value of the measures in regard to contraceptive behaviours. According to Levinson's more recent studies, results indicate that new research on the factor matrix of the scale are relevant.

Subjective quality of life and suicidal behavior among Taiwanese schizophrenia patients.

PubMed

Kao, Yu-Chen; Liu, Yia-Ping; Cheng, Tsung-Hsing; Chou, Ming-Kuen

2012-04-01

Research of suicidal behavior in individuals with schizophrenia has often suggested that clinical characteristics and symptoms likely influence a patient's suicidal risk. However, there is a lack of research describing the link between patients' subjective quality of life (SQOL) and suicidal behavior in non-Western countries. Therefore, the current study attempts to explore how schizophrenia patients' SQOL and their suicidal behavior are related in a Taiwanese sample. In this study, 102 schizophrenia outpatients were investigated using the Taiwanese World Health Organization Quality of Life Schedule-Brief Version (WHO-QOL-BREF-TW), several Beck-Related symptom rating scales, and the Positive and Negative Syndrome Scale (PANSS) for psychopathology. These patients were also evaluated for suicidal risk using the critical items of the Scale for Suicide Ideation (SSI) and lifetime suicide attempts. Statistical analyses, including independent sample t tests, analysis of covariance (ANCOVA) and logistic stepwise regression models were completed. Compared with the non-suicidal group, suicidal patients had significantly lower scores in SQOL domains. The differences in social domain remained significant after adjusting for depressive symptoms. In multiple logistic regression analyses, level of depressive and psychotic symptoms increased and poor social and psychological SQOL were significant contributors to suicidal behavior. Having removed depressive symptoms from the model, only dissatisfaction with social SQOL was associated with heightened suicidal risk. Schizophrenia is associated with a high suicidal risk, of which depressive and psychotic symptoms are the major correlates. Again, the present study confirms and extends previous research showing that dissatisfied SQOL, particularly dissatisfaction with social relationships, should be considered in the assessment of suicidal risk in outpatients with schizophrenia, even when accounting other possible confounding factor such as depression.

Gauging climate change effects at local scales: weather-based indices to monitor insect harassment in caribou.

PubMed

Witter, Leslie A; Johnson, Chris J; Croft, Bruno; Gunn, Anne; Poirier, Lisa M

2012-09-01

Climate change is occurring at an accelerated rate in the Arctic. Insect harassment may be an important link between increased summer temperature and reduced body condition in caribou and reindeer (both Rangifer tarandus). To examine the effects of climate change at a scale relevant to Rangifer herds, we developed monitoring indices using weather to predict activity of parasitic insects across the central Arctic. During 2007-2009, we recorded weather conditions and used carbon dioxide baited traps to monitor activity of mosquitoes (Culicidae), black flies (Simuliidae), and oestrid flies (Oestridae) on the post-calving and summer range of the Bathurst barren-ground caribou (Rangifer tarandus groenlandicus) herd in Northwest Territories and Nunavut, Canada. We developed statistical models representing hypotheses about effects of weather, habitat, location, and temporal variables on insect activity. We used multinomial logistic regression to model mosquito and black fly activity, and logistic regression to model oestrid fly presence. We used information theory to select models to predict activity levels of insects. Using historical weather data, we used hindcasting to develop a chronology of insect activity on the Bathurst range from 1957 to 2008. Oestrid presence and mosquito and black fly activity levels were explained by temperature. Wind speed, light intensity, barometric pressure, relative humidity, vegetation, topography, location, time of day, and growing degree-days also affected mosquito and black fly levels. High predictive ability of all models justified the use of weather to index insect activity. Retrospective analyses indicated conditions favoring mosquito activity declined since the late 1950s, while predicted black fly and oestrid activity increased. Our indices can be used as monitoring tools to gauge potential changes in insect harassment due to climate change at scales relevant to caribou herds.

INTENTION TO EXPERIMENT WITH E-CIGARETTES IN A CROSS-SECTIONAL SURVEY OF UNDERGRADUATE UNIVERSITY STUDENTS IN HUNGARY

PubMed Central

Pénzes, Melinda; Foley, Kristie L.; Balázs, Péter; Urbán, Róbert

2016-01-01

Background Electronic cigarettes are often used to promote cessation. Only a few studies have explored the motivations for e-cigarette experimentation among young adults. Objectives The goals of this study were to assess the intention to try e-cigarettes among Hungarian university students and to develop a motivational scale to measure vulnerability to e-cigarette experimentation. Methods 826 Hungarian university students completed an internet-based survey in 2013 to measure motives for trying e-cigarettes. We conducted exploratory factor analyses and identified factors that promote and deter experimentation. Logistic regression analysis was performed to test the concurrent predictive validity of the identified motivational factors and we used these factors to predict e-cigarette experimentation, controlling for other known correlates of e-cigarette use. Results 24.9% of the participants have ever tried an e-cigarette and 17.2% of current nonsmokers experimented with the product. Almost 11% of respondents intended to try an e-cigarette in the future, yet only 0.6% were current e-cigarette users. Six factors were identified in the motivational scale for experimentation, four that promote usage (health benefits/smoking cessation; curiosity/taste variety; perceived social norms; convenience when smoking is prohibited) and two that deter usage (chemical hazard; danger of dependence). In a logistic regression analysis, the curiosity/taste factor was the only motivational factor significantly associated with the intention to try e-cigarettes in the future. Conclusions This is the first study to test a motivational scale about what motivates e-cigarettes usage among university students. Additional research is needed to better understand these factors and their influence on e-cigarette uptake. PMID:27159776

Depression among family caregivers of community-dwelling older people who used services under the Long Term Care Insurance program: a large-scale population-based study in Japan.

PubMed

Arai, Yumiko; Kumamoto, Keigo; Mizuno, Yoko; Washio, Masakazu

2014-01-01

To identify predictors for depression among family caregivers of community-dwelling older people under the Long Term Care Insurance (LTCI) program in Japan through a large-scale population-based survey. All 5938 older people with disabilities, using domiciliary services under the LTCI in the city of Toyama, and their family caregivers participated in this study. Caregiver depression was defined as scores of ≥16 on the Center for Epidemiological Studies Depression Scale (CES-D). Other caregiver measures included age, sex, hours spent caregiving, relationship to the care recipient, income adequacy, living arrangement, self-rated health, and work status. Care recipient measures included age, sex, level of functional disability, and severity of dementia. The data from 4128 pairs of the care recipients and their family caregivers were eligible for further analyses. A multiple logistic regression analysis was used to examine the predictors associated with being at risk of clinical depression (CES-D of ≥16). Overall, 34.2% of caregivers scored ≥16 on the CES-D. The independent predictors for depression by logistic regression analysis were six caregiver characteristics (female, income inadequacy, longer hours spent caregiving, worse subjective health, and co-residence with the care recipient) and one care-recipient characteristic (moderate dementia). This is one of the first population-based examinations of caregivers of older people who are enrolled in a national service system that provides affordable access to services. The results highlighted the importance of monitoring caregivers who manifest the identified predictors to attenuate caregiver depression at the population level under the LTCI.

Depression and its association with self-esteem, family, peer and school factors in a population of 9586 adolescents in southern Taiwan.

PubMed

Lin, Huang-Chi; Tang, Tze-Chun; Yen, Ju-Yu; Ko, Chin-Hung; Huang, Chi-Fen; Liu, Shu-Chun; Yen, Cheng-Fang

2008-08-01

The aim of the present study was to gain insight into the prevalence of depression and its association with self-esteem, family, peer and school factors in a large-scale representative Taiwanese adolescent population. A total of 12,210 adolescent students were recruited into the present study. Subjects with a score >28 on the Center for Epidemiological Studies' Depression Scale were defined as having significant depression; the Rosenberg Self-Esteem Scale, Adolescent Family and Social Life Questionnaire and Family C-APGAR Index were applied to assess subjects' self-esteem, family, peer and school factors. The association between depression and correlates were examined on t-test and chi(2) test. The significant factors were further included in logistic regression analysis. Among 9586 participants (response rate: 86.3%), the prevalence of depression was 12.3%. The risk factors associated with depression in univariate analysis included female gender, older age, residency in urban areas, lower self-esteem, disruptive parental marriage, low family income, family conflict, poorer family function, less satisfaction with peer relationships, less connectedness to school, and poor academic performance. After adjusting the effects of sex, age and location, only subjects with lower self-esteem, higher family conflict, poorer family function, lower rank and decreased satisfaction in their peer group, and less connectedness to school were prone to depression on logistic regression. The prevalence of depression is high in Taiwanese adolescents, and the multiple factors of family, peer, school and individuals are associated with adolescent depression. The factors identified in the present study may be helpful when designing and implementing preventive intervention programs.

Evaluating the effect of a third-party implementation of resolution recovery on the quality of SPECT bone scan imaging using visual grading regression.

PubMed

Hay, Peter D; Smith, Julie; O'Connor, Richard A

2016-02-01

The aim of this study was to evaluate the benefits to SPECT bone scan image quality when applying resolution recovery (RR) during image reconstruction using software provided by a third-party supplier. Bone SPECT data from 90 clinical studies were reconstructed retrospectively using software supplied independent of the gamma camera manufacturer. The current clinical datasets contain 120×10 s projections and are reconstructed using an iterative method with a Butterworth postfilter. Five further reconstructions were created with the following characteristics: 10 s projections with a Butterworth postfilter (to assess intraobserver variation); 10 s projections with a Gaussian postfilter with and without RR; and 5 s projections with a Gaussian postfilter with and without RR. Two expert observers were asked to rate image quality on a five-point scale relative to our current clinical reconstruction. Datasets were anonymized and presented in random order. The benefits of RR on image scores were evaluated using ordinal logistic regression (visual grading regression). The application of RR during reconstruction increased the probability of both observers of scoring image quality as better than the current clinical reconstruction even where the dataset contained half the normal counts. Type of reconstruction and observer were both statistically significant variables in the ordinal logistic regression model. Visual grading regression was found to be a useful method for validating the local introduction of technological developments in nuclear medicine imaging. RR, as implemented by the independent software supplier, improved bone SPECT image quality when applied during image reconstruction. In the majority of clinical cases, acquisition times for bone SPECT intended for the purposes of localization can safely be halved (from 10 s projections to 5 s) when RR is applied.

Disorganized Symptoms Predicted Worse Functioning Outcome in Schizophrenia Patients with Established Illness.

PubMed

Ortiz, Bruno Bertolucci; Gadelha, Ary; Higuchi, Cinthia Hiroko; Noto, Cristiano; Medeiros, Daiane; Pitta, José Cássio do Nascimento; de Araújo Filho, Gerardo Maria; Hallak, Jaime Eduardo Cecílio; Bressan, Rodrigo Affonseca

Most patients with schizophrenia will have subsequent relapses of the disorder, with continuous impairments in functioning. However, evidence is lacking on how symptoms influence functioning at different phases of the disease. This study aims to investigate the relationship between symptom dimensions and functioning at different phases: acute exacerbation, nonremission and remission. Patients with schizophrenia were grouped into acutely ill (n=89), not remitted (n=89), and remitted (n=69). Three exploratory stepwise linear regression analyses were performed for each phase of schizophrenia, in which the five PANSS factors and demographic variables were entered as the independent variables and the total Global Assessment of Functioning Scale (GAF) score was entered as the dependent variable. An additional exploratory stepwise logistic regression analysis was performed to predict subsequent remission at discharge in the inpatient population. The Disorganized factor was the most significant predictor for acutely ill patients (p<0.001), while the Hostility factor was the most significant for not-remitted patients and the Negative factor was the most significant for remitted patients (p=0.001 and p<0.001, respectively). In the logistic regression, the Disorganized factor score presented a significant negative association with remission (p=0.007). Higher disorganization symptoms showed the greatest impact in functioning at acute phase, and prevented patients from achieving remission, suggesting it may be a marker of symptom severity and worse outcome in schizophrenia.

A simple measure of cognitive reserve is relevant for cognitive performance in MS patients.

PubMed

Della Corte, Marida; Santangelo, Gabriella; Bisecco, Alvino; Sacco, Rosaria; Siciliano, Mattia; d'Ambrosio, Alessandro; Docimo, Renato; Cuomo, Teresa; Lavorgna, Luigi; Bonavita, Simona; Tedeschi, Gioacchino; Gallo, Antonio

2018-05-04

Cognitive reserve (CR) contributes to preserve cognition despite brain damage. This theory has been applied to multiple sclerosis (MS) to explain the partial relationship between cognition and MRI markers of brain pathology. Our aim was to determine the relationship between two measures of CR and cognition in MS. One hundred and forty-seven MS patients were enrolled. Cognition was assessed using the Rao's Brief Repeatable Battery and the Stroop Test. CR was measured as the vocabulary subtest of the WAIS-R score (VOC) and the number of years of formal education (EDU). Regression analysis included raw score data on each neuropsychological (NP) test as dependent variables and demographic/clinical parameters, VOC, and EDU as independent predictors. A binary logistic regression analysis including clinical/CR parameters as covariates and absence/presence of cognitive deficits as dependent variables was performed too. VOC, but not EDU, was strongly correlated with performances at all ten NP tests. EDU was correlated with executive performances. The binary logistic regression showed that only the Expanded Disability Status Scale (EDSS) and VOC were independently correlated with the presence/absence of CD. The lower the VOC and/or the higher the EDSS, the higher the frequency of CD. In conclusion, our study supports the relevance of CR in subtending cognitive performances and the presence of CD in MS patients.

Correlation analysis between work-related musculoskeletal disorders and the nursing practice environment, quality of life, and social support in the nursing professionals

PubMed Central

Yan, Ping; Yang, Yi; Zhang, Li; Li, Fuye; Huang, Amei; Wang, Yanan; Dai, Yali; Yao, Hua

2018-01-01

Abstract We aim to analyze the correlated influential factors between work-related musculoskeletal disorders (WMSDs) and nursing practice environment and quality of life and social support. From January 2015 to October 2015, cluster sampling was performed on the nurses from 12 hospitals in the 6 areas in Xinjiang. The questionnaires including the modified Nordic Musculoskeletal Questionnaire, Practice Environment Scale (PES), the Mos 36-item Short Form Health Survey, and Social Support Rating Scale were used to investigate. Multivariate logistic regression analysis was used to explore the influential factors of WMSDs. The total prevalence of WMSDs was 79.52% in the nurses ever since the working occupation, which was mainly involved waist (64.83%), neck (61.83%), and shoulder (52.36%). Multivariate logistic regression analysis indicated age (≥26 years), working in the Department of Surgery, Department of Critical Care, Outpatient Department, and Department of Anesthesia, working duration of >40 hours per week were the risk factors of WMSDs in the nurses. The physiological function (PF), body pain, total healthy condition, adequate working force and financial support, and social support were the protective factors of WMSDs. The prevalence of WMSDs in the nurses in Xinjiang Autonomous Region was high. PF, bodily pain, total healthy condition, having adequate staff and support resources to provide quality patient care, and social support were the protective factors of WMSDs in the nurses. PMID:29489648

Relationship between late-life depression and life stressors: large-scale cross-sectional study of a representative sample of the Japanese general population.

PubMed

Kaji, Tatsuhiko; Mishima, Kazuo; Kitamura, Shingo; Enomoto, Minori; Nagase, Yukihiro; Li, Lan; Kaneita, Yoshitaka; Ohida, Takashi; Nishikawa, Toru; Uchiyama, Makoto

2010-08-01

The purpose of the present study was to clarify the relationship between late-life depression and daily life stress in a representative sample of 10 969 Japanese subjects. Data on 10 969 adults aged > or =50 who participated in the Active Survey of Health and Welfare in 2000, were analyzed. The self-administered questionnaire included items on 21 reasons for life stressors and the magnitude of stress, as well as the Japanese version of the Center for Epidemiologic Studies Depression Scale (CES-D). The relationship between the incidence of life stressors and mild-moderate (D(16)) and severe (D(26)) depressive symptoms was examined using logistic regression analysis. A total of 21.9% of subjects had D(16) symptoms, and 9.3% had D(26) symptoms. Further, increased age and being female were associated with more severe depressive state. Logistic regression analysis indicated that the strongest relationship between both the incidence of D(16) and D(26) symptoms and life stressors stemmed from 'having no one to talk to' (odds ratio = 3.3 and 5.0, respectively). Late-life depression was also associated with 'loss of purpose in life', 'separation/divorce', 'having nothing to do', 'health/illness/care of self', and 'debt'. There is a relationship between late-life depression and diminished social relationships, experiences involving loss of purpose in life or human relationships, and health problems in the Japanese general population.

Sleep Bruxism and Anxiety Impacts in Quality of Life Related to Oral Health of Brazilian Children and their Families.

PubMed

de Alencar, Nashalie Andrade; Leão, Cecília Sued; Leão, Anna Thereza Thomé; Luiz, Ronir Raggio; Fonseca-Gonçalves, Andréa; Maia, Lucianne Cople

This study aimed to assess the impact of parent reported sleep bruxism, trait anxiety and sociodemographic/socioeconomic features on quality of life related to oral health (OHRQoL) of children and their families. Healthy children aged 3-7 years, with (n=34) and without (n=32) bruxism were select for this study. Data was collected by applying the following instruments: The Early Childhood Oral Health Scale (B-ECOHIS) and Trait-anxiety Scale (TAS). The sociodemographic/socioeconomic characteristics were obtained by interviews with parents. Multiple logistic regression tests were performed to observe the influence of sociodemographic/socioeconomic characteristics, bruxism and trait-anxiety on the children's OHRQoL. No association between sleep bruxism and all evaluated sociodemographic/socioeconomic conditions, with exception of being the only child (p=0.029), were observed. Mean B-ECOHIS and TAS scores were different (p<0.05) between children with (3.41 ± 4.87; 45.09 ± 15.46, respectively) and without (0.63 ± 1.28; 29.53 ± 11.82, respectively) bruxism. Although an association between bruxism and OHRQoL (p=0.015) was observed, it was dropped (p=0.336; OR=1.77) in the logistic regression model. Trait anxiety was the variable responsible for the impact on the OHRQoL of children (p=0.012; OR=1.05). Our results indicated anxiety as the main factor that interfered in the OHRQoL of children with sleep bruxism.

Correlation analysis between work-related musculoskeletal disorders and the nursing practice environment, quality of life, and social support in the nursing professionals.

PubMed

Yan, Ping; Yang, Yi; Zhang, Li; Li, Fuye; Huang, Amei; Wang, Yanan; Dai, Yali; Yao, Hua

2018-03-01

We aim to analyze the correlated influential factors between work-related musculoskeletal disorders (WMSDs) and nursing practice environment and quality of life and social support.From January 2015 to October 2015, cluster sampling was performed on the nurses from 12 hospitals in the 6 areas in Xinjiang. The questionnaires including the modified Nordic Musculoskeletal Questionnaire, Practice Environment Scale (PES), the Mos 36-item Short Form Health Survey, and Social Support Rating Scale were used to investigate. Multivariate logistic regression analysis was used to explore the influential factors of WMSDs.The total prevalence of WMSDs was 79.52% in the nurses ever since the working occupation, which was mainly involved waist (64.83%), neck (61.83%), and shoulder (52.36%). Multivariate logistic regression analysis indicated age (≥26 years), working in the Department of Surgery, Department of Critical Care, Outpatient Department, and Department of Anesthesia, working duration of >40 hours per week were the risk factors of WMSDs in the nurses. The physiological function (PF), body pain, total healthy condition, adequate working force and financial support, and social support were the protective factors of WMSDs.The prevalence of WMSDs in the nurses in Xinjiang Autonomous Region was high. PF, bodily pain, total healthy condition, having adequate staff and support resources to provide quality patient care, and social support were the protective factors of WMSDs in the nurses.

Clinical decision making for a tooth with apical periodontitis: the patients' preferred level of participation.

PubMed

Azarpazhooh, Amir; Dao, Thuan; Ungar, Wendy J; Chaudry, Faiza; Figueiredo, Rafael; Krahn, Murray; Friedman, Shimon

2014-06-01

To effectively engage patients in clinical decisions regarding the management of teeth with apical periodontitis (AP), there is a need to explore patients' perspectives on the decision-making process. This study surveyed patients for their preferred level of participation in making treatment decisions for a tooth with AP. Data were collected through a mail-out survey of 800 University of Toronto Faculty of Dentistry patients, complemented by a convenience sample of 200 patients from 10 community practices. The Control Preferences Scale was used to evaluate the patients' preferences for active, collaborative, or passive participation in treatment decisions for a tooth with AP. Using bivariate and logistic regression analyses, the Gelberg-Andersen Behavioral Model for Vulnerable Populations was applied to the Control Preferences Scale questions to understand the influential factors (P ≤ .05). Among 434 of 1,000 respondents, 44%, 40%, and 16% preferred an active, collaborative, and passive participation, respectively. Logistic regression showed a significant association (P ≤ .025) between participants' higher education and preference for active participation compared with a collaborative role. Also, immigrant status was significantly associated with preference for passive participation (P = .025). The majority of patients valued an active or collaborative participation in deciding treatment for a tooth with AP. This pattern implied a preference for a patient-centered practice mode that emphasizes patient autonomy in decision making. Copyright © 2014 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Racial residential segregation and preterm birth: built environment as a mediator.

PubMed

Anthopolos, Rebecca; Kaufman, Jay S; Messer, Lynne C; Miranda, Marie Lynn

2014-05-01

Racial residential segregation has been associated with preterm birth. Few studies have examined mediating pathways, in part because, with binary outcomes, indirect effects estimated from multiplicative models generally lack causal interpretation. We develop a method to estimate additive-scale natural direct and indirect effects from logistic regression. We then evaluate whether segregation operates through poor-quality built environment to affect preterm birth. To estimate natural direct and indirect effects, we derive risk differences from logistic regression coefficients. Birth records (2000-2008) for Durham, North Carolina, were linked to neighborhood-level measures of racial isolation and a composite construct of poor-quality built environment. We decomposed the total effect of racial isolation on preterm birth into direct and indirect effects. The adjusted total effect of an interquartile increase in racial isolation on preterm birth was an extra 27 preterm events per 1000 births (risk difference = 0.027 [95% confidence interval = 0.007 to 0.047]). With poor-quality built environment held at the level it would take under isolation at the 25th percentile, the direct effect of an interquartile increase in isolation was 0.022 (-0.001 to 0.042). Poor-quality built environment accounted for 35% (11% to 65%) of the total effect. Our methodology facilitates the estimation of additive-scale natural effects with binary outcomes. In this study, the total effect of racial segregation on preterm birth was partially mediated by poor-quality built environment.

Resilience and risk for alcohol use disorders: A Swedish twin study

PubMed Central

Long, E.C.; Lönn, S.L.; Ji, J.; Lichtenstein, P.; Sundquist, J.; Sundquist, K.; Kendler, K.S.

2016-01-01

Background Resilience has been shown to be protective against alcohol use disorders (AUD), but the magnitude and nature of the relationship between these two phenotypes is not clear. The aim of this study is to examine the strength of this relationship and the degree to which it results from common genetic or common environmental influences. Methods Resilience was assessed on a nine-point scale during a personal interview in 1,653,721 Swedish men aged 17–25 years. AUD was identified based on Swedish medical, legal, and pharmacy registries. The magnitude of the relationship between resilience and AUD was examined using logistic regression. The extent to which the relationship arises from common genetic or common environmental factors was examined using a bivariate Cholesky decomposition model. Results The five single items that comprised the resilience assessment (social maturity, interest, psychological energy, home environment, and emotional control) all reduced risk for subsequent AUD, with social maturity showing the strongest effect. The linear effect by logistic regression showed that a one-point increase on the resilience scale was associated with a 29% decrease in odds of AUD. The Cholesky decomposition model demonstrated that the resilience-AUD relationship was largely attributable to overlapping genetic and shared environmental factors (57% and 36%, respectively). Conclusion Resilience is strongly associated with a reduction in risk for AUD. This relationship appears to be the result of overlapping genetic and shared environmental influences that impact resilience and risk of AUD, rather than a directly causal relationship. PMID:27918840

Mortality prediction of head Abbreviated Injury Score and Glasgow Coma Scale: analysis of 7,764 head injuries.

PubMed

Demetriades, Demetrios; Kuncir, Eric; Murray, James; Velmahos, George C; Rhee, Peter; Chan, Linda

2004-08-01

We assessed the prognostic value and limitations of Glasgow Coma Scale (GCS) and head Abbreviated Injury Score (AIS) and correlated head AIS with GCS. We studied 7,764 patients with head injuries. Bivariate analysis was performed to examine the relationship of GCS, head AIS, age, gender, and mechanism of injury with mortality. Stepwise logistic regression analysis was used to identify the independent risk factors associated with mortality. The overall mortality in the group of head injury patients with no other major extracranial injuries and no hypotension on admission was 9.3%. Logistic regression analysis identified head AIS, GCS, age, and mechanism of injury as significant independent risk factors of death. The prognostic value of GCS and head AIS was significantly affected by the mechanism of injury and the age of the patient. Patients with similar GCS or head AIS but different mechanisms of injury or ages had significantly different outcomes. The adjusted odds ratio of death in penetrating trauma was 5.2 (3.9, 7.0), p < 0.0001, and in the age group > or = 55 years the adjusted odds ratio was 3.4 (2.6, 4.6), p < 0.0001. There was no correlation between head AIS and GCS (correlation coefficient -0.31). Mechanism of injury and age have a major effect in the predictive value of GCS and head AIS. There is no good correlation between GCS and head AIS.

Association of sarcopenia with functional decline in community-dwelling elderly subjects in Japan.

PubMed

Tanimoto, Yoshimi; Watanabe, Misuzu; Sun, Wei; Tanimoto, Keiji; Shishikura, Kanako; Sugiura, Yumiko; Kusabiraki, Toshiyuki; Kono, Koichi

2013-10-01

The present study aimed to determine the association of sarcopenia, defined by muscle mass, muscle strength and physical performance, with functional disability from a 2-year cohort study of community-dwelling elderly Japanese people. Participants were 743 community-dwelling elderly Japanese people aged 65 years or older. We used bioelectrical impedance analysis (BIA) to measure muscle mass, grip strength to measure muscle strength, and usual walking speed to measure physical performance in a baseline study. Functional disability was defined using an activities of daily living (ADL) scale and instrumental activities of daily living (IADL) scale at baseline and during follow-up examinations 2 years later. Logistic regression analysis, adjusted for age and body mass index, was used to examine the association between sarcopenia and the occurrence of functional disability. In the present study, 7.8% of men and 10.2% of women were classified as having sarcopenia. Among sarcopenia patients in the baseline study, 36.8% of men and 18.8% of women became dependent in ADL at 2-year follow up. From the logistic regression analysis adjusted by age and body mass index, sarcopenia was significantly associated with the occurrences of physical disability compared with normal subjects in both men and women. Sarcopenia, defined by muscle mass, muscle strength and physical performance, was associated with functional decline over a 2-year period in elderly Japanese. Interventions to prevent sarcopenia are very important to prevent functional decline among elderly individuals. © 2013 Japan Geriatrics Society.

Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Factors Associated with Metabolic Syndrome and Related Medical Costs by the Scale of Enterprise in Korea

PubMed Central

2013-01-01

Objectives The purpose of this study was to identify the risk factors of metabolic syndrome (MS) and to analyze the relationship between the risk factors of MS and medical cost of major diseases related to MS in Korean workers, according to the scale of the enterprise. Methods Data was obtained from annual physical examinations, health insurance qualification and premiums, and health insurance benefits of 4,094,217 male and female workers who underwent medical examinations provided by the National Health Insurance Corporation in 2009. Logistic regression analyses were used to the identify risk factors of MS and multiple regression was used to find factors associated with medical expenditures due to major diseases related to MS. Result The study found that low-income workers were more likely to work in small-scale enterprises. The prevalence rate of MS in males and females, respectively, was 17.2% and 9.4% in small-scale enterprises, 15.9% and 8.9% in medium-scale enterprises, and 15.9% and 5.5% in large-scale enterprises. The risks of MS increased with age, lower income status, and smoking in small-scale enterprise workers. The medical costs increased in workers with old age and past smoking history. There was also a gender difference in the pattern of medical expenditures related to MS. Conclusions Health promotion programs to manage metabolic syndrome should be developed to focus on workers who smoke, drink, and do little exercise in small scale enterprises. PMID:24472134

Factors associated with metabolic syndrome and related medical costs by the scale of enterprise in Korea.

PubMed

Kong, Hyung-Sik; Lee, Kang-Sook; Yim, Eun-Shil; Lee, Seon-Young; Cho, Hyun-Young; Lee, Bin Na; Park, Jee Young

2013-10-21

The purpose of this study was to identify the risk factors of metabolic syndrome (MS) and to analyze the relationship between the risk factors of MS and medical cost of major diseases related to MS in Korean workers, according to the scale of the enterprise. Data was obtained from annual physical examinations, health insurance qualification and premiums, and health insurance benefits of 4,094,217 male and female workers who underwent medical examinations provided by the National Health Insurance Corporation in 2009. Logistic regression analyses were used to the identify risk factors of MS and multiple regression was used to find factors associated with medical expenditures due to major diseases related to MS. The study found that low-income workers were more likely to work in small-scale enterprises. The prevalence rate of MS in males and females, respectively, was 17.2% and 9.4% in small-scale enterprises, 15.9% and 8.9% in medium-scale enterprises, and 15.9% and 5.5% in large-scale enterprises. The risks of MS increased with age, lower income status, and smoking in small-scale enterprise workers. The medical costs increased in workers with old age and past smoking history. There was also a gender difference in the pattern of medical expenditures related to MS. Health promotion programs to manage metabolic syndrome should be developed to focus on workers who smoke, drink, and do little exercise in small scale enterprises.

Racial/ethnic and educational differences in the estimated odds of recent nitrite use among adult household residents in the United States: an illustration of matching and conditional logistic regression.

PubMed

Delva, J; Spencer, M S; Lin, J K

2000-01-01

This article compares estimates of the relative odds of nitrite use obtained from weighted unconditional logistic regression with estimates obtained from conditional logistic regression after post-stratification and matching of cases with controls by neighborhood of residence. We illustrate these methods by comparing the odds associated with nitrite use among adults of four racial/ethnic groups, with and without a high school education. We used aggregated data from the 1994-B through 1996 National Household Survey on Drug Abuse (NHSDA). Difference between the methods and implications for analysis and inference are discussed.

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

PubMed Central

Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

2012-01-01

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999

Strategies for Testing Statistical and Practical Significance in Detecting DIF with Logistic Regression Models

ERIC Educational Resources Information Center

Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza

2014-01-01

This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…

Iterative Purification and Effect Size Use with Logistic Regression for Differential Item Functioning Detection

ERIC Educational Resources Information Center

French, Brian F.; Maller, Susan J.

2007-01-01

Two unresolved implementation issues with logistic regression (LR) for differential item functioning (DIF) detection include ability purification and effect size use. Purification is suggested to control inaccuracies in DIF detection as a result of DIF items in the ability estimate. Additionally, effect size use may be beneficial in controlling…

A Note on Three Statistical Tests in the Logistic Regression DIF Procedure

ERIC Educational Resources Information Center

Paek, Insu

2012-01-01

Although logistic regression became one of the well-known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying…

"Let Me Count the Ways:" Fostering Reasons for Living among Low-Income, Suicidal, African American Women

ERIC Educational Resources Information Center

West, Lindsey M.; Davis, Telsie A.; Thompson, Martie P.; Kaslow, Nadine J.

2011-01-01

Protective factors for fostering reasons for living were examined among low-income, suicidal, African American women. Bivariate logistic regressions revealed that higher levels of optimism, spiritual well-being, and family social support predicted reasons for living. Multivariate logistic regressions indicated that spiritual well-being showed…

Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression

ERIC Educational Resources Information Center

Peng, Chao-Ying Joanne; Zhu, Jin

2008-01-01

For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks to fill the void by comparing two approaches for handling missing data in categorical covariates in logistic regression: the…

Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

ERIC Educational Resources Information Center

Atar, Burcu; Kamata, Akihito

2011-01-01

The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…

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…

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…

Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression

ERIC Educational Resources Information Center

Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M.

2013-01-01

Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…

Two-factor logistic regression in pediatric liver transplantation

NASA Astrophysics Data System (ADS)

Uzunova, Yordanka; Prodanova, Krasimira; Spasov, Lyubomir

2017-12-01

Using a two-factor logistic regression analysis an estimate is derived for the probability of absence of infections in the early postoperative period after pediatric liver transplantation. The influence of both the bilirubin level and the international normalized ratio of prothrombin time of blood coagulation at the 5th postoperative day is studied.

Predictors of Placement Stability at the State Level: The Use of Logistic Regression to Inform Practice

ERIC Educational Resources Information Center

Courtney, Jon R.; Prophet, Retta

2011-01-01

Placement instability is often associated with a number of negative outcomes for children. To gain state level contextual knowledge of factors associated with placement stability/instability, logistic regression was applied to selected variables from the New Mexico Adoption and Foster Care Administrative Reporting System dataset. Predictors…

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

Length bias correction in gene ontology enrichment analysis using logistic regression.

PubMed

Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H

2012-01-01

When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.

Matched samples logistic regression in case-control studies with missing values: when to break the matches.

PubMed

Hansson, Lisbeth; Khamis, Harry J

2008-12-01

Simulated data sets are used to evaluate conditional and unconditional maximum likelihood estimation in an individual case-control design with continuous covariates when there are different rates of excluded cases and different levels of other design parameters. The effectiveness of the estimation procedures is measured by method bias, variance of the estimators, root mean square error (RMSE) for logistic regression and the percentage of explained variation. Conditional estimation leads to higher RMSE than unconditional estimation in the presence of missing observations, especially for 1:1 matching. The RMSE is higher for the smaller stratum size, especially for the 1:1 matching. The percentage of explained variation appears to be insensitive to missing data, but is generally higher for the conditional estimation than for the unconditional estimation. It is particularly good for the 1:2 matching design. For minimizing RMSE, a high matching ratio is recommended; in this case, conditional and unconditional logistic regression models yield comparable levels of effectiveness. For maximizing the percentage of explained variation, the 1:2 matching design with the conditional logistic regression model is recommended.

Label-noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study.

PubMed

Lee, Seokho; Shin, Hyejin; Lee, Sang Han

2016-12-01

Alzheimer's disease (AD) is usually diagnosed by clinicians through cognitive and functional performance test with a potential risk of misdiagnosis. Since the progression of AD is known to cause structural changes in the corpus callosum (CC), the CC thickness can be used as a functional covariate in AD classification problem for a diagnosis. However, misclassified class labels negatively impact the classification performance. Motivated by AD-CC association studies, we propose a logistic regression for functional data classification that is robust to misdiagnosis or label noise. Specifically, our logistic regression model is constructed by adopting individual intercepts to functional logistic regression model. This approach enables to indicate which observations are possibly mislabeled and also lead to a robust and efficient classifier. An effective algorithm using MM algorithm provides simple closed-form update formulas. We test our method using synthetic datasets to demonstrate its superiority over an existing method, and apply it to differentiating patients with AD from healthy normals based on CC from MRI. © 2016, The International Biometric Society.

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

Logistic regression for circular data

NASA Astrophysics Data System (ADS)

Al-Daffaie, Kadhem; Khan, Shahjahan

2017-05-01

This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.

Naval Research Logistics Quarterly. Volume 28. Number 3,

DTIC Science & Technology

1981-09-01

denotes component-wise maximum. f has antone (isotone) differences on C x D if for cl < c2 and d, < d2, NAVAL RESEARCH LOGISTICS QUARTERLY VOL. 28...or negative correlations and linear or nonlinear regressions. Given are the mo- ments to order two and, for special cases, (he regression function and...data sets. We designate this bnb distribution as G - B - N(a, 0, v). The distribution admits only of positive correlation and linear regressions

Regression approaches in the test-negative study design for assessment of influenza vaccine effectiveness.

PubMed

Bond, H S; Sullivan, S G; Cowling, B J

2016-06-01

Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network

PubMed Central

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins. PMID:27418910

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

PubMed

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

PubMed

Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.

PubMed

Armstrong, Ben G; Gasparrini, Antonio; Tobias, Aurelio

2014-11-24

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

Use of generalized ordered logistic regression for the analysis of multidrug resistance data.

PubMed

Agga, Getahun E; Scott, H Morgan

2015-10-01

Statistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR]=1.03; 95% CI=0.73-1.47) and CTC (Prop OR=1.1; 95% CI=0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated. Published by Elsevier B.V.

Core OCD Symptoms: Exploration of Specificity and Relations with Psychopathology

PubMed Central

Stasik, Sara M.; Naragon-Gainey, Kristin; Chmielewski, Michael; Watson, David

2012-01-01

Obsessive-compulsive disorder (OCD) is a heterogeneous condition, comprised of multiple symptom domains. This study used aggregate composite scales representing three core OCD dimensions (Checking, Cleaning, Rituals), as well as Hoarding, to examine the discriminant validity, diagnostic specificity, and predictive ability of OCD symptom scales. The core OCD scales demonstrated strong patterns of convergent and discriminant validity – suggesting that these dimensions are distinct from other self-reported symptoms – whereas hoarding symptoms correlated just as strongly with OCD and non-OCD symptoms in most analyses. Across analyses, our results indicated that Checking is a particularly strong, specific marker of OCD diagnosis, whereas the specificity of Cleaning and Hoarding to OCD was less strong. Finally, the OCD Checking scale was the only significant predictor of OCD diagnosis in logistic regression analyses. Results are discussed with regard to the importance of assessing OCD symptom dimensions separately and implications for classification. PMID:23026094

Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

Elevated Fasting Blood Glucose Is Predictive of Poor Outcome in Non-Diabetic Stroke Patients: A Sub-Group Analysis of SMART.

PubMed

Yao, Ming; Ni, Jun; Zhou, Lixin; Peng, Bin; Zhu, Yicheng; Cui, Liying

2016-01-01

Although increasing evidence suggests that hyperglycemia following acute stroke adversely affects clinical outcome, whether the association between glycaemia and functional outcome varies between stroke patients with\\without pre-diagnosed diabetes remains controversial. We aimed to investigate the relationship between the fasting blood glucose (FBG) and the 6-month functional outcome in a subgroup of SMART cohort and further to assess whether this association varied based on the status of pre-diagnosed diabetes. Data of 2862 patients with acute ischemic stroke (629 with pre-diagnosed diabetics) enrolled from SMART cohort were analyzed. Functional outcome at 6-month post-stroke was measured by modified Rankin Scale (mRS) and categorized as favorable (mRS:0-2) or poor (mRS:3-5). Binary logistic regression model, adjusting for age, gender, educational level, history of hypertension and stroke, baseline NIHSS and treatment group, was used in the whole cohort to evaluate the association between admission FBG and functional outcome. Stratified logistic regression analyses were further performed based on the presence/absence of pre-diabetes history. In the whole cohort, multivariable logistical regression showed that poor functional outcome was associated with elevated FBG (OR1.21 (95%CI 1.07-1.37), p = 0.002), older age (OR1.64 (95% CI1.38-1.94), p<0.001), higher NIHSS (OR2.90 (95%CI 2.52-3.33), p<0.001) and hypertension (OR1.42 (95%CI 1.13-1.98), p = 0.04). Stratified logistical regression analysis showed that the association between FBG and functional outcome remained significant only in patients without pre-diagnosed diabetes (OR1.26 (95%CI 1.03-1.55), p = 0.023), but not in those with premorbid diagnosis of diabetes (p = 0.885). The present results demonstrate a significant association between elevated FBG after stroke and poor functional outcome in patients without pre-diagnosed diabetes, but not in diabetics. This finding confirms the importance of glycemic control during acute phase of ischemic stroke especially in patients without pre-diagnosed diabetes. Further investigation for developing optimal strategies to control blood glucose level in hyperglycemic setting is therefore of great importance. ClinicalTrials.gov NCT00664846.

The impact of young drivers' lifestyle on their road traffic accident risk in greater Athens area.

PubMed

Chliaoutakis, J E; Darviri, C; Demakakos, P T

1999-11-01

Young drivers (18-24) both in Greece and elsewhere appear to have high rates of road traffic accidents. Many factors contribute to the creation of these high road traffic accidents rates. It has been suggested that lifestyle is an important one. The main objective of this study is to find out and clarify the (potential) relationship between young drivers' lifestyle and the road traffic accident risk they face. Moreover, to examine if all the youngsters have the same elevated risk on the road or not. The sample consisted of 241 young Greek drivers of both sexes. The statistical analysis included factor analysis and logistic regression analysis. Through the principal component analysis a ten factor scale was created which included the basic lifestyle traits of young Greek drivers. The logistic regression analysis showed that the young drivers whose dominant lifestyle trait is alcohol consumption or drive without destination have high accident risk, while these whose dominant lifestyle trait is culture, face low accident risk. Furthermore, young drivers who are religious in one way or another seem to have low accident risk. Finally, some preliminary observations on how health promotion should be put into practice are discussed.

Can we "predict" long-term outcome for ambulatory transcutaneous electrical nerve stimulation in patients with chronic pain?

PubMed

Köke, Albère J; Smeets, Rob J E M; Perez, Roberto S; Kessels, Alphons; Winkens, Bjorn; van Kleef, Maarten; Patijn, Jacob

2015-03-01

Evidence for effectiveness of transcutaneous electrical nerve stimulation (TENS) is still inconclusive. As heterogeneity of chronic pain patients might be an important factor for this lack of efficacy, identifying factors for a successful long-term outcome is of great importance. A prospective study was performed to identify variables with potential predictive value for 2 outcome measures on long term (6 months); (1) continuation of TENS, and (2) a minimally clinical important pain reduction of ≥ 33%. At baseline, a set of risk factors including pain-related variables, psychological factors, and disability was measured. In a multiple logistic regression analysis, higher patient's expectations, neuropathic pain, no severe pain (< 80 mm visual analogue scale [VAS]) were independently related to long-term continuation of TENS. For the outcome "minimally clinical important pain reduction," the multiple logistic regression analysis indicated that no multisited pain (> 2 pain locations) and intermittent pain were positively and independently associated with a minimally clinical important pain reduction of ≥ 33%. The results showed that factors associated with a successful outcome in the long term are dependent on definition of successful outcome. © 2014 World Institute of Pain.

Majorization Minimization by Coordinate Descent for Concave Penalized Generalized Linear Models

PubMed Central

Jiang, Dingfeng; Huang, Jian

2013-01-01

Recent studies have demonstrated theoretical attractiveness of a class of concave penalties in variable selection, including the smoothly clipped absolute deviation and minimax concave penalties. The computation of the concave penalized solutions in high-dimensional models, however, is a difficult task. We propose a majorization minimization by coordinate descent (MMCD) algorithm for computing the concave penalized solutions in generalized linear models. In contrast to the existing algorithms that use local quadratic or local linear approximation to the penalty function, the MMCD seeks to majorize the negative log-likelihood by a quadratic loss, but does not use any approximation to the penalty. This strategy makes it possible to avoid the computation of a scaling factor in each update of the solutions, which improves the efficiency of coordinate descent. Under certain regularity conditions, we establish theoretical convergence property of the MMCD. We implement this algorithm for a penalized logistic regression model using the SCAD and MCP penalties. Simulation studies and a data example demonstrate that the MMCD works sufficiently fast for the penalized logistic regression in high-dimensional settings where the number of covariates is much larger than the sample size. PMID:25309048

Depressive vulnerabilities predict depression status and trajectories of depression over 1 year in persons with acute coronary syndrome.

PubMed

Doyle, Frank; McGee, Hannah; Delaney, Mary; Motterlini, Nicola; Conroy, Ronán

2011-01-01

Depression is prevalent in patients hospitalized with acute coronary syndrome (ACS). We determined whether theoretical vulnerabilities for depression (interpersonal life events, reinforcing events, cognitive distortions, Type D personality) predicted depression, or depression trajectories, post-hospitalization. We followed 375 ACS patients who completed depression scales during hospital admission and at least once during three follow-up intervals over 1 year (949 observations). Questionnaires assessing vulnerabilities were completed at baseline. Logistic regression for panel/longitudinal data predicted depression status during follow-up. Latent class analysis determined depression trajectories. Multinomial logistic regression modeled the relationship between vulnerabilities and trajectories. Vulnerabilities predicted depression status over time in univariate and multivariate analysis, even when controlling for baseline depression. Proportions in each depression trajectory category were as follows: persistent (15%), subthreshold (37%), never depressed (48%). Vulnerabilities independently predicted each of these trajectories, with effect sizes significantly highest for the persistent depression group. Self-reported vulnerabilities - stressful life events, reduced reinforcing events, cognitive distortions, personality - measured during hospitalization can identify those at risk for depression post-ACS and especially those with persistent depressive episodes. Interventions should focus on these vulnerabilities. Copyright © 2011 Elsevier Inc. All rights reserved.

Factors associated with local public health agency participation in obesity prevention in southern States.

PubMed

Hatala, Jeffrey J; Fields, Tina T

2015-05-01

Obesity rates in the southern US states are higher than in other states. Historically, large-scale community-based interventions in the United States have not proven successful. With local public health agencies (LPHAs) tasked with prevention, their role in obesity prevention is important, yet little research exists regarding what predicts the participation of LPHAs. Cross-sectional data from the 2008 National Association of City and County Health Officials profile study and two public health conceptual frameworks were used to assess structural and environmental predictors of LPHA participation in obesity prevention. The predictors were compared between southern and nonsouthern states. Univariate and weighted logistic regressions were performed. Analysis revealed that more LPHAs in southern states were engaged in nearly all of the 10 essential public health functions related to obesity prevention compared with nonsouthern states. Presence of community-based organizations and staffing levels were the only significant variables in two of the six logistic regression models. This study provides insights into the success rates of the obesity prevention efforts of LPHAs in southern and nonsouthern states. Future research is needed to understand why and how certain structural elements and any additional factors influence LPHA participation in obesity prevention.

Gender differences in social support and leisure-time physical activity

PubMed Central

Oliveira, Aldair J; Lopes, Claudia S; Rostila, Mikael; Werneck, Guilherme Loureiro; Griep, Rosane Härter; de Leon, Antônio Carlos Monteiro Ponce; Faerstein, Eduardo

2014-01-01

OBJECTIVE To identify gender differences in social support dimensions’ effect on adults’ leisure-time physical activity maintenance, type, and time. METHODS Longitudinal study of 1,278 non-faculty public employees at a university in Rio de Janeiro, RJ, Southeastern Brazil. Physical activity was evaluated using a dichotomous question with a two-week reference period, and further questions concerning leisure-time physical activity type (individual or group) and time spent on the activity. Social support was measured with the Medical Outcomes Study Social Support Scale. For the analysis, logistic regression models were adjusted separately by gender. RESULTS A multinomial logistic regression showed an association between material support and individual activities among women (OR = 2.76; 95%CI 1.2;6.5). Affective support was associated with time spent on leisure-time physical activity only among men (OR = 1.80; 95%CI 1.1;3.2). CONCLUSIONS All dimensions of social support that were examined influenced either the type of, or the time spent on, leisure-time physical activity. In some social support dimensions, the associations detected varied by gender. Future studies should attempt to elucidate the mechanisms involved in these gender differences. PMID:25210819

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

PubMed

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

Detecting DIF in Polytomous Items Using MACS, IRT and Ordinal Logistic Regression

ERIC Educational Resources Information Center

Elosua, Paula; Wells, Craig

2013-01-01

The purpose of the present study was to compare the Type I error rate and power of two model-based procedures, the mean and covariance structure model (MACS) and the item response theory (IRT), and an observed-score based procedure, ordinal logistic regression, for detecting differential item functioning (DIF) in polytomous items. A simulation…

Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests

ERIC Educational Resources Information Center

Rudner, Lawrence

2016-01-01

In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows…

Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

ERIC Educational Resources Information Center

Fan, Xitao; Wang, Lin

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

School Exits in the Milwaukee Parental Choice Program: Evidence of a Marketplace?

ERIC Educational Resources Information Center

Ford, Michael

2011-01-01

This article examines whether the large number of school exits from the Milwaukee school voucher program is evidence of a marketplace. Two logistic regression and multinomial logistic regression models tested the relation between the inability to draw large numbers of voucher students and the ability for a private school to remain viable. Data on…

Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients.

PubMed

Dagliati, Arianna; Malovini, Alberto; Decata, Pasquale; Cogni, Giulia; Teliti, Marsida; Sacchi, Lucia; Cerra, Carlo; Chiovato, Luca; Bellazzi, Riccardo

2016-01-01

In this work we present our efforts in building a model able to forecast patients' changes in clinical conditions when repeated measurements are available. In this case the available risk calculators are typically not applicable. We propose a Hierarchical Bayesian Logistic Regression model, which allows taking into account individual and population variability in model parameters estimate. The model is used to predict metabolic control and its variation in type 2 diabetes mellitus. In particular we have analyzed a population of more than 1000 Italian type 2 diabetic patients, collected within the European project Mosaic. The results obtained in terms of Matthews Correlation Coefficient are significantly better than the ones gathered with standard logistic regression model, based on data pooling.

An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models

USGS Publications Warehouse

Li, Ji; Gray, B.R.; Bates, D.M.

2008-01-01

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.

Model building strategy for logistic regression: purposeful selection.

PubMed

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

Assessing landslide susceptibility by statistical data analysis and GIS: the case of Daunia (Apulian Apennines, Italy)

NASA Astrophysics Data System (ADS)

Ceppi, C.; Mancini, F.; Ritrovato, G.

2009-04-01

This study aim at the landslide susceptibility mapping within an area of the Daunia (Apulian Apennines, Italy) by a multivariate statistical method and data manipulation in a Geographical Information System (GIS) environment. Among the variety of existing statistical data analysis techniques, the logistic regression was chosen to produce a susceptibility map all over an area where small settlements are historically threatened by landslide phenomena. By logistic regression a best fitting between the presence or absence of landslide (dependent variable) and the set of independent variables is performed on the basis of a maximum likelihood criterion, bringing to the estimation of regression coefficients. The reliability of such analysis is therefore due to the ability to quantify the proneness to landslide occurrences by the probability level produced by the analysis. The inventory of dependent and independent variables were managed in a GIS, where geometric properties and attributes have been translated into raster cells in order to proceed with the logistic regression by means of SPSS (Statistical Package for the Social Sciences) package. A landslide inventory was used to produce the bivariate dependent variable whereas the independent set of variable concerned with slope, aspect, elevation, curvature, drained area, lithology and land use after their reductions to dummy variables. The effect of independent parameters on landslide occurrence was assessed by the corresponding coefficient in the logistic regression function, highlighting a major role played by the land use variable in determining occurrence and distribution of phenomena. Once the outcomes of the logistic regression are determined, data are re-introduced in the GIS to produce a map reporting the proneness to landslide as predicted level of probability. As validation of results and regression model a cell-by-cell comparison between the susceptibility map and the initial inventory of landslide events was performed and an agreement at 75% level achieved.

Hypnotizability, posttraumatic stress, and depressive symptoms in metastatic breast cancer.

PubMed

Keuroghlian, Alex S; Butler, Lisa D; Neri, Eric; Spiegel, David

2010-01-01

This study assessed whether high hypnotizability is associated with posttraumatic stress and depressive symptoms in a sample of 124 metastatic breast cancer patients. Hypnotic Induction Profile Scores were dichotomized into low and high categories; posttraumatic intrusion and avoidance symptoms were measured with the Impact of Events Scale (IES); hyperarousal symptoms with items from the Profile of Mood States; and depressive symptoms with the Center for Epidemiologic Studies-Depression Scale. High hypnotizability was significantly related to greater IES total, IES intrusion symptoms, and depressive symptoms. A logistic regression model showed that IES total predicts high hypnotizability after adjusting for depressive symptoms and hyperarousal. The authors relate these results to findings in other clinical populations and discuss implications for the psychosocial treatment of metastatic breast cancer.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

NASA Astrophysics Data System (ADS)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

A New Lebanese Medication Adherence Scale: Validation in Lebanese Hypertensive Adults.

PubMed

Bou Serhal, R; Salameh, P; Wakim, N; Issa, C; Kassem, B; Abou Jaoude, L; Saleh, N

2018-01-01

A new Lebanese scale measuring medication adherence considered socioeconomic and cultural factors not taken into account by the eight-item Morisky Medication Adherence Scale (MMAS-8). Objectives were to validate the new adherence scale and its prediction of hypertension control, compared to MMAS-8, and to assess adherence rates and factors. A cross-sectional study, including 405 patients, was performed in outpatient cardiology clinics of three hospitals in Beirut. Blood pressure was measured, a questionnaire filled, and sodium intake estimated by a urine test. Logistic regression defined predictors of hypertension control and adherence. 54.9% had controlled hypertension. 82.4% were adherent by the new scale, which showed good internal consistency, adequate questions (KMO coefficient = 0.743), and four factors. It predicted hypertension control (OR = 1.217; p value = 0.003), unlike MMAS-8, but the scores were correlated (ICC average measure = 0.651; p value < 0.001). Stress and smoking predicted nonadherence. This study elaborated a validated, practical, and useful tool measuring adherence to medications in Lebanese hypertensive patients.

A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

ERIC Educational Resources Information Center

Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard

2010-01-01

The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…

Odds Ratio, Delta, ETS Classification, and Standardization Measures of DIF Magnitude for Binary Logistic Regression

ERIC Educational Resources Information Center

Monahan, Patrick O.; McHorney, Colleen A.; Stump, Timothy E.; Perkins, Anthony J.

2007-01-01

Previous methodological and applied studies that used binary logistic regression (LR) for detection of differential item functioning (DIF) in dichotomously scored items either did not report an effect size or did not employ several useful measures of DIF magnitude derived from the LR model. Equations are provided for these effect size indices.…

A Generalized Logistic Regression Procedure to Detect Differential Item Functioning among Multiple Groups

ERIC Educational Resources Information Center

Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul

2011-01-01

We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…

Risk Factors of Falls in Community-Dwelling Older Adults: Logistic Regression Tree Analysis

ERIC Educational Resources Information Center

Yamashita, Takashi; Noe, Douglas A.; Bailer, A. John

2012-01-01

Purpose of the Study: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. Design and Methods: A nationally representative sample of American older adults aged 65 years and older (N = 9,592) in the Health and Retirement Study 2004 and 2006 modules was used.…

Estimation of Logistic Regression Models in Small Samples. A Simulation Study Using a Weakly Informative Default Prior Distribution

ERIC Educational Resources Information Center

Gordovil-Merino, Amalia; Guardia-Olmos, Joan; Pero-Cebollero, Maribel

2012-01-01

In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable…

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

USGS Publications Warehouse

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

2003-01-01

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

A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

NASA Technical Reports Server (NTRS)

Messer, Bradley

2007-01-01

Propulsion ground test facilities face the daily challenge of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Over the last decade NASA s propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and exceeded the capabilities of numerous test facility and test article components. A logistic regression mathematical modeling technique has been developed to predict the probability of successfully completing a rocket propulsion test. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),.., X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure of accomplishing a full duration test. The use of logistic regression modeling is not new; however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from this type of model provide project managers with insight and confidence into the effectiveness of rocket propulsion ground testing.

Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

PubMed

Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

2017-06-01

To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (P<0.05). In addition, a comparison of the area under receiver operating characteristic curves of the two models showed a statistically significant difference (P<0.05). The RBF ANNs model is more likely to predict the occurrence of PVT induced by AP than logistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.

An allometric scaling relation based on logistic growth of cities

NASA Astrophysics Data System (ADS)

Chen, Yanguang

2014-08-01

The relationships between urban area and population size have been empirically demonstrated to follow the scaling law of allometric growth. This allometric scaling is based on exponential growth of city size and can be termed "exponential allometry", which is associated with the concepts of fractals. However, both city population and urban area comply with the course of logistic growth rather than exponential growth. In this paper, I will present a new allometric scaling based on logistic growth to solve the abovementioned problem. The logistic growth is a process of replacement dynamics. Defining a pair of replacement quotients as new measurements, which are functions of urban area and population, we can derive an allometric scaling relation from the logistic processes of urban growth, which can be termed "logistic allometry". The exponential allometric relation between urban area and population is the approximate expression of the logistic allometric equation when the city size is not large enough. The proper range of the allometric scaling exponent value is reconsidered through the logistic process. Then, a medium-sized city of Henan Province, China, is employed as an example to validate the new allometric relation. The logistic allometry is helpful for further understanding the fractal property and self-organized process of urban evolution in the right perspective.

EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed Privacy-Preserving Online Model Learning

PubMed Central

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-01-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection etc.) as the traditional frequentist Logistic Regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. PMID:23562651

Hopelessness and Suicidal Ideation among Patients with Depression and Neurotic Disorders Attending a Tertiary Care Centre at Eastern Nepal.

PubMed

Pokharel, R; Lama, S; Adhikari, B R

2016-09-01

Hopelessness is thought to result from a negative appraisal system and interacts with, and worsens, appraisals of defeat and trap which in turn interact with suicide schema and lead to suicidal behaviour. This study was intended to assess hopelessness and suicidal ideation among patients with depression and neurotic disorders at tertiary care centre of eastern Nepal. A cross sectional design included 70 respondents by purposive sampling technique. Beck Hopelessness Scale and Scale of Suicidal Ideation were used to measure hopelessness and suicidal ideation, respectively. Data were analyzed using SPSS statistical software. Pearson chi-square, binary logistic regression and Spearmans' rho, test were applied at 95% confidence interval. Mean ± SD age was 32.8 ± 13.5 years. Most (62.8%) of the patients were female and with the diagnosis of depression. Majority (66%) of the patients had hopelessness. There was no significant difference in hopelessness among patients with depression and neurotic disorders. About 17% respondents had suicidal ideation, among them 82.4% were female. There was no significant difference of suicidal ideation among patients with depression and neurotic disorders (p=0.013). Significant positive correlation between hopelessness and suicidal ideation was found (p=0.001). Binary logistic regression revealed hopelessness was independently related to income and family history of mental illness. Similarly, suicidal ideation was independently related to depression and family history of mental illness. Female respondents, people living under poverty and positive family history of mental illness had more hopelessness and suicidal ideation.

Life stressors, coping strategies, and social supports in patients with irritable bowel syndrome

PubMed Central

Roohafza, Hamidreza; Keshteli, Ammar Hassanzadeh; Daghaghzadeh, Hamed; Afshar, Hamid; Erfani, Zahra; Adibi, Peyman

2016-01-01

Background: The frequency and the perceived intensity of life stressors, coping strategies, and social supports are very important in everybody's well-being. This study intended to estimate the relation of irritable bowel syndrome (IBS) and these factors. Materials and Methods: This was a cross-sectional study carried out in Isfahan on 2013. Data were extracted from the framework of the study on the epidemiology of psychological, alimentary health, and nutrition. Symptoms of IBS were evaluated by Talley bowel disease questionnaire. Stressful life event, modified COPE scale, and Multidimensional Scale of Perceived Social Support were also used. About 4763 subjects were completed questionnaires. Analyzing data were done by t-test and multivariate logistic regression. Results: Of all returned questionnaire, 1024 (21.5%) were diagnosed with IBS. IBS and clinically-significant IBS (IBS-S) groups have significantly experienced a higher level of perceived intensity of stressors and had a higher frequency of stressors. The mean score of social supports and the mean scores of three coping strategies (problem engagement, support seeking, and positive reinterpretation and growth) were significantly lower in subjects with either IBS-S or IBS than in those with no IBS. Multivariate logistic regression revealed a significant association between frequency of stressors and perceived intensity of stressors with IBS (odds ratio [OR] =1.09 and OR = 1.02, respectively) or IBS-S (OR = 1.09 and OR = 1.03, respectively). Conclusions: People with IBS had higher numbers of stressors, higher perception of the intensity of stressors, less adaptive coping strategies, and less social supports which should be focused in psychosocial interventions. PMID:27761433

Association of Perceived Stress with Stressful Life Events, Lifestyle and Sociodemographic Factors: A Large-Scale Community-Based Study Using Logistic Quantile Regression

PubMed Central

Feizi, Awat; Aliyari, Roqayeh; Roohafza, Hamidreza

2012-01-01

Objective. The present paper aimed at investigating the association between perceived stress and major life events stressors in Iranian general population. Methods. In a cross-sectional large-scale community-based study, 4583 people aged 19 and older, living in Isfahan, Iran, were investigated. Logistic quantile regression was used for modeling perceived stress, measured by GHQ questionnaire, as the bounded outcome (dependent), variable, and as a function of most important stressful life events, as the predictor variables, controlling for major lifestyle and sociodemographic factors. This model provides empirical evidence of the predictors' effects heterogeneity depending on individual location on the distribution of perceived stress. Results. The results showed that among four stressful life events, family conflicts and social problems were more correlated with level of perceived stress. Higher levels of education were negatively associated with perceived stress and its coefficients monotonically decrease beyond the 30th percentile. Also, higher levels of physical activity were associated with perception of low levels of stress. The pattern of gender's coefficient over the majority of quantiles implied that females are more affected by stressors. Also high perceived stress was associated with low or middle levels of income. Conclusions. The results of current research suggested that in a developing society with high prevalence of stress, interventions targeted toward promoting financial and social equalities, social skills training, and healthy lifestyle may have the potential benefits for large parts of the population, most notably female and lower educated people. PMID:23091560

The influence of family adaptability and cohesion on anxiety and depression of terminally ill cancer patients.

PubMed

Park, Young-Yoon; Jeong, Young-Jin; Lee, Junyong; Moon, Nayun; Bang, Inho; Kim, Hyunju; Yun, Kyung-Sook; Kim, Yong-I; Jeon, Tae-Hee

2018-01-01

This study investigated the effect of family members on terminally ill cancer patients by measuring the relationship of the presence of the family caregivers, visiting time by family and friends, and family adaptability and cohesion with patient's anxiety and depression. From June, 2016 to March, 2017, 100 terminally ill cancer patients who were admitted to a palliative care unit in Seoul, South Korea, were surveyed, and their medical records were reviewed. The Korean version of the Family Adaptability and Cohesion Evaluation Scales III and Hospital Anxiety-Depression Scale was used. Chi-square and multiple logistic regression analyses were used. The results of the chi-square analysis showed that the presence of family caregivers and family visit times did not have statistically significant effects on anxiety and depression in terminally ill cancer patients. In multiple logistic regression, when adjusted for age, sex, ECOG PS, and the monthly average income, the odds ratios (ORs) of the low family adaptability to anxiety and depression were 2.4 (1.03-5.83) and 5.4 (1.10-26.87), respectively. The OR of low family cohesion for depression was 5.4 (1.10-27.20) when adjusted for age, sex, ECOG PS, and monthly average household income. A higher family adaptability resulted in a lower degree of anxiety and depression in terminally ill cancer patients. The higher the family cohesion, the lower the degree of depression in the patient. The presence of the family caregiver and the visiting time by family and friends did not affect the patient's anxiety and depression.

Life stressors, coping strategies, and social supports in patients with irritable bowel syndrome.

PubMed

Roohafza, Hamidreza; Keshteli, Ammar Hassanzadeh; Daghaghzadeh, Hamed; Afshar, Hamid; Erfani, Zahra; Adibi, Peyman

2016-01-01

The frequency and the perceived intensity of life stressors, coping strategies, and social supports are very important in everybody's well-being. This study intended to estimate the relation of irritable bowel syndrome (IBS) and these factors. This was a cross-sectional study carried out in Isfahan on 2013. Data were extracted from the framework of the study on the epidemiology of psychological, alimentary health, and nutrition. Symptoms of IBS were evaluated by Talley bowel disease questionnaire. Stressful life event, modified COPE scale, and Multidimensional Scale of Perceived Social Support were also used. About 4763 subjects were completed questionnaires. Analyzing data were done by t -test and multivariate logistic regression. Of all returned questionnaire, 1024 (21.5%) were diagnosed with IBS. IBS and clinically-significant IBS (IBS-S) groups have significantly experienced a higher level of perceived intensity of stressors and had a higher frequency of stressors. The mean score of social supports and the mean scores of three coping strategies (problem engagement, support seeking, and positive reinterpretation and growth) were significantly lower in subjects with either IBS-S or IBS than in those with no IBS. Multivariate logistic regression revealed a significant association between frequency of stressors and perceived intensity of stressors with IBS (odds ratio [OR] =1.09 and OR = 1.02, respectively) or IBS-S (OR = 1.09 and OR = 1.03, respectively). People with IBS had higher numbers of stressors, higher perception of the intensity of stressors, less adaptive coping strategies, and less social supports which should be focused in psychosocial interventions.

[Relationship between perceptions of safety climate at workplace and depressive disorders in manufacturing workers].

PubMed

Liu, Xu-hua; Xiao, Ya-ni; Huang, Zhi-xiong; Huang, Shao-bin; Cao, Xiao-ou; Guan, Dong-bo; Chen, Wei-qing

2013-04-01

To investigate the risk factors for depressive disorders in manufacturing workers and to provide a basis for developing health promotion measures at workplace. A questionnaire survey was performed in 8085 front-line production workers from 33 manufacturing enterprises in Nanhai District of Foshan, Guangdong Province, China. The questionnaire contained a survey of demographic characteristics, the Safety Climate Scale, the Center for Epidemiological Studies Depression Scale, etc. The multilevel logistic regression analysis was applied to investigate the risk factors for depressive disorders in workers. A total of 6260 workers completed the survey; their mean age was 31.1 ± 8.6 years, and 53.2% of them were males. The multilevel logistic regression analysis showed that after adjustment for sociodemographic factors such as age, sex, and martial status, more depressive disorders were reported in the enterprises with higher score of "production safety training" than in those with lower score (OR = 1.46, 95%CI = 1.07 ∼ 1.97); fewer depressive disorders were reported in the enterprises with higher score of "colleagues concerned about production safety" than in those with lower score (OR = 0.08, 95%CI = 0.03 ∼ 0.26); the relationships of "safety warnings and precautions" and "managers concerned about production safety" with workers' depressive disorders were not statistically significant (OR = 0.78, 95%CI = 0.48 ∼ 1.28; OR = 1.08, 95%CI = 0.68 ∼ 1.72). Depressive disorders in manufacturing workers are related to the safety climate at workplace, which indicates that a good safety climate at workplace should be created to prevent and control depressive disorders in workers.

Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): Geomorphological features, conditioning factors and methodological implications in susceptibility assessment

NASA Astrophysics Data System (ADS)

Domínguez-Cuesta, María José; Jiménez-Sánchez, Montserrat; Berrezueta, Edgar

2007-09-01

A geomorphological study focussing on slope instability and landslide susceptibility modelling was performed on a 278 km 2 area in the Nalón River Basin (Central Coalfield, NW Spain). The methodology of the study includes: 1) geomorphological mapping at both 1:5000 and 1:25,000 scales based on air-photo interpretation and field work; 2) Digital Terrain Model (DTM) creation and overlay of geomorphological and DTM layers in a Geographical Information System (GIS); and 3) statistical treatment of variables using SPSS and development of a logistic regression model. A total of 603 mass movements including earth flow and debris flow were inventoried and were classified into two groups according to their size. This study focuses on the first group with small mass movements (10 0 to 10 1 m in size), which often cause damage to infrastructures and even victims. The detected conditioning factors of these landslides are lithology (soils and colluviums), vegetation (pasture) and topography. DTM analyses show that high instabilities are linked to slopes with NE and SW orientations, curvature values between - 6 and - 0.7, and slope values from 16° to 30°. Bedrock lithology (Carboniferous sandstone and siltstone), presence of Quaternary soils and sediments, vegetation, and the topographical factors were used to develop a landslide susceptibility model using the logistic regression method. Application of "zoom method" allows us to accurately detect small mass movements using a 5-m grid cell data even if geomorphological mapping is done at a 1:25,000 scale.

Risk factors for amendment in type, duration and setting of prescribed outpatient parenteral antimicrobial therapy (OPAT) for adult patients with cellulitis: a retrospective cohort study and CART analysis.

PubMed

Quirke, Michael; Curran, Emma May; O'Kelly, Patrick; Moran, Ruth; Daly, Eimear; Aylward, Seamus; McElvaney, Gerry; Wakai, Abel

2018-01-01

To measure the percentage rate and risk factors for amendment in the type, duration and setting of outpatient parenteral antimicrobial therapy ( OPAT) for the treatment of cellulitis. A retrospective cohort study of adult patients receiving OPAT for cellulitis was performed. Treatment amendment (TA) was defined as hospital admission or change in antibiotic therapy in order to achieve clinical response. Multivariable logistic regression (MVLR) and classification and regression tree (CART) analysis were performed. There were 307 patients enrolled. TA occurred in 36 patients (11.7%). Significant risk factors for TA on MVLR were increased age, increased Numerical Pain Scale Score (NPSS) and immunocompromise. The median OPAT duration was 7 days. Increased age, heart rate and C reactive protein were associated with treatment prolongation. CART analysis selected age <64.5 years, female gender and NPSS <2.5 in the final model, generating a low-sensitivity (27.8%), high-specificity (97.1%) decision tree. Increased age, NPSS and immunocompromise were associated with OPAT amendment. These identified risk factors can be used to support an evidence-based approach to patient selection for OPAT in cellulitis. The CART algorithm has good specificity but lacks sensitivity and is shown to be inferior in this study to logistic regression modelling. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Dietary consumption patterns and laryngeal cancer risk.

PubMed

Vlastarakos, Petros V; Vassileiou, Andrianna; Delicha, Evie; Kikidis, Dimitrios; Protopapas, Dimosthenis; Nikolopoulos, Thomas P

2016-06-01

We conducted a case-control study to investigate the effect of diet on laryngeal carcinogenesis. Our study population was made up of 140 participants-70 patients with laryngeal cancer (LC) and 70 controls with a non-neoplastic condition that was unrelated to diet, smoking, or alcohol. A food-frequency questionnaire determined the mean consumption of 113 different items during the 3 years prior to symptom onset. Total energy intake and cooking mode were also noted. The relative risk, odds ratio (OR), and 95% confidence interval (CI) were estimated by multiple logistic regression analysis. We found that the total energy intake was significantly higher in the LC group (p < 0.001), and that the difference remained statistically significant after logistic regression analysis (p < 0.001; OR: 118.70). Notably, meat consumption was higher in the LC group (p < 0.001), and the difference remained significant after logistic regression analysis (p = 0.029; OR: 1.16). LC patients also consumed significantly more fried food (p = 0.036); this difference also remained significant in the logistic regression model (p = 0.026; OR: 5.45). The LC group also consumed significantly more seafood (p = 0.012); the difference persisted after logistic regression analysis (p = 0.009; OR: 2.48), with the consumption of shrimp proving detrimental (p = 0.049; OR: 2.18). Finally, the intake of zinc was significantly higher in the LC group before and after logistic regression analysis (p = 0.034 and p = 0.011; OR: 30.15, respectively). Cereal consumption (including pastas) was also higher among the LC patients (p = 0.043), with logistic regression analysis showing that their negative effect was possibly associated with the sauces and dressings that traditionally accompany pasta dishes (p = 0.006; OR: 4.78). Conversely, a higher consumption of dairy products was found in controls (p < 0.05); logistic regression analysis showed that calcium appeared to be protective at the micronutrient level (p < 0.001; OR: 0.27). We found no difference in the overall consumption of fruits and vegetables between the LC patients and controls; however, the LC patients did have a greater consumption of cooked tomatoes and cooked root vegetables (p = 0.039 for both), and the controls had more consumption of leeks (p = 0.042) and, among controls younger than 65 years, cooked beans (p = 0.037). Lemon (p = 0.037), squeezed fruit juice (p = 0.032), and watermelon (p = 0.018) were also more frequently consumed by the controls. Other differences at the micronutrient level included greater consumption by the LC patients of retinol (p = 0.044), polyunsaturated fats (p = 0.041), and linoleic acid (p = 0.008); LC patients younger than 65 years also had greater intake of riboflavin (p = 0.045). We conclude that the differences in dietary consumption patterns between LC patients and controls indicate a possible role for lifestyle modifications involving nutritional factors as a means of decreasing the risk of laryngeal cancer.

A Comparison of the Logistic Regression and Contingency Table Methods for Simultaneous Detection of Uniform and Nonuniform DIF

ERIC Educational Resources Information Center

Guler, Nese; Penfield, Randall D.

2009-01-01

In this study, we investigate the logistic regression (LR), Mantel-Haenszel (MH), and Breslow-Day (BD) procedures for the simultaneous detection of both uniform and nonuniform differential item functioning (DIF). A simulation study was used to assess and compare the Type I error rate and power of a combined decision rule (CDR), which assesses DIF…

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…

Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis

ERIC Educational Resources Information Center

Johnson, William L.; Johnson, Annabel M.; Johnson, Jared

2012-01-01

Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…

Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

Treesearch

Susan L. King

2003-01-01

The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557

Coping Styles in Heart Failure Patients with Depressive Symptoms

PubMed Central

Trivedi, Ranak B.; Blumenthal, James A.; O'Connor, Christopher; Adams, Kirkwood; Hinderliter, Alan; Sueta-Dupree, Carla; Johnson, Kristy; Sherwood, Andrew

2009-01-01

Objective Elevated depressive symptoms have been linked to poorer prognosis in heart failure (HF) patients. Our objective was to identify coping styles associated with depressive symptoms in HF patients. Methods 222 stable HF patients (32.75% female, 45.4% non-Hispanic Black) completed multiple questionnaires. Beck Depression Inventory (BDI) assessed depressive symptoms, Life Orientation Test (LOT-R) assessed optimism, ENRICHD Social Support Inventory (ESSI) and Perceived Social Support Scale (PSSS) assessed social support, and COPE assessed coping styles. Linear regression analyses were employed to assess the association of coping styles with continuous BDI scores. Logistic regression analyses were performed using BDI scores dichotomized into BDI<10 versus BDI≥10, to identify coping styles accompanying clinically significant depressive symptoms. Results In linear regression models, higher BDI scores were associated with lower scores on the acceptance (β=-.14), humor (β=-.15), planning (β=-.15), and emotional support (β=-.14) subscales of the COPE, and higher scores on the behavioral disengagement (β=.41), denial (β=.33), venting (β=.25), and mental disengagement (β=.22) subscales. Higher PSSS and ESSI scores were associated with lower BDI scores (β=-.32 and -.25, respectively). Higher LOT-R scores were associated with higher BDI scores (β=.39, p<.001). In logistical regression models, BDI≥10 was associated with greater likelihood of behavioral disengagement (OR=1.3), denial (OR=1.2), mental disengagement (OR=1.3), venting (OR=1.2), and pessimism (OR=1.2), and lower perceived social support measured by PSSS (OR=.92) and ESSI (OR=.92). Conclusion Depressive symptoms in HF patients are associated with avoidant coping, lower perceived social support, and pessimism. Results raise the possibility that interventions designed to improve coping may reduce depressive symptoms. PMID:19773027

Coping styles in heart failure patients with depressive symptoms.

PubMed

Trivedi, Ranak B; Blumenthal, James A; O'Connor, Christopher; Adams, Kirkwood; Hinderliter, Alan; Dupree, Carla; Johnson, Kristy; Sherwood, Andrew

2009-10-01

Elevated depressive symptoms have been linked to poorer prognosis in heart failure (HF) patients. Our objective was to identify coping styles associated with depressive symptoms in HF patients. A total of 222 stable HF patients (32.75% female, 45.4% non-Hispanic black) completed multiple questionnaires. Beck Depression Inventory (BDI) assessed depressive symptoms, Life Orientation Test (LOT-R) assessed optimism, ENRICHD Social Support Inventory (ESSI) and Perceived Social Support Scale (PSSS) assessed social support, and COPE assessed coping styles. Linear regression analyses were employed to assess the association of coping styles with continuous BDI scores. Logistic regression analyses were performed using BDI scores dichotomized into BDI<10 vs. BDI> or =10, to identify coping styles accompanying clinically significant depressive symptoms. In linear regression models, higher BDI scores were associated with lower scores on the acceptance (beta=-.14), humor (beta=-.15), planning (beta=-.15), and emotional support (beta=-.14) subscales of the COPE, and higher scores on the behavioral disengagement (beta=.41), denial (beta=.33), venting (beta=.25), and mental disengagement (beta=.22) subscales. Higher PSSS and ESSI scores were associated with lower BDI scores (beta=-.32 and -.25, respectively). Higher LOT-R scores were associated with higher BDI scores (beta=.39, P<.001). In logistical regression models, BDI> or =10 was associated with greater likelihood of behavioral disengagement (OR=1.3), denial (OR=1.2), mental disengagement (OR=1.3), venting (OR=1.2), and pessimism (OR=1.2), and lower perceived social support measured by PSSS (OR=.92) and ESSI (OR=.92). Depressive symptoms in HF patients are associated with avoidant coping, lower perceived social support, and pessimism. Results raise the possibility that interventions designed to improve coping may reduce depressive symptoms.

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

Polymorphism Thr160Thr in SRD5A1, involved in the progesterone metabolism, modifies postmenopausal breast cancer risk associated with menopausal hormone therapy.

PubMed

Hein, R; Abbas, S; Seibold, P; Salazar, R; Flesch-Janys, D; Chang-Claude, J

2012-01-01

Menopausal hormone therapy (MHT) is associated with an increased breast cancer risk in postmenopausal women, with combined estrogen-progestagen therapy posing a greater risk than estrogen monotherapy. However, few studies focused on potential effect modification of MHT-associated breast cancer risk by genetic polymorphisms in the progesterone metabolism. We assessed effect modification of MHT use by five coding single nucleotide polymorphisms (SNPs) in the progesterone metabolizing enzymes AKR1C3 (rs7741), AKR1C4 (rs3829125, rs17134592), and SRD5A1 (rs248793, rs3736316) using a two-center population-based case-control study from Germany with 2,502 postmenopausal breast cancer patients and 4,833 matched controls. An empirical-Bayes procedure that tests for interaction using a weighted combination of the prospective and the retrospective case-control estimators as well as standard prospective logistic regression were applied to assess multiplicative statistical interaction between polymorphisms and duration of MHT use with regard to breast cancer risk assuming a log-additive mode of inheritance. No genetic marginal effects were observed. Breast cancer risk associated with duration of combined therapy was significantly modified by SRD5A1_rs3736316, showing a reduced risk elevation in carriers of the minor allele (p (interaction,empirical-Bayes) = 0.006 using the empirical-Bayes method, p (interaction,logistic regression) = 0.013 using logistic regression). The risk associated with duration of use of monotherapy was increased by AKR1C3_rs7741 in minor allele carriers (p (interaction,empirical-Bayes) = 0.083, p (interaction,logistic regression) = 0.029) and decreased in minor allele carriers of two SNPs in AKR1C4 (rs3829125: p (interaction,empirical-Bayes) = 0.07, p (interaction,logistic regression) = 0.021; rs17134592: p (interaction,empirical-Bayes) = 0.101, p (interaction,logistic regression) = 0.038). After Bonferroni correction for multiple testing only SRD5A1_rs3736316 assessed using the empirical-Bayes method remained significant. Postmenopausal breast cancer risk associated with combined therapy may be modified by genetic variation in SRD5A1. Further well-powered studies are, however, required to replicate our finding.

Gastrointestinal-focused panic attacks among Cambodian refugees: associated psychopathology, flashbacks, and catastrophic cognitions.

PubMed

Hinton, Devon E; Chhean, Dara; Fama, Jeanne M; Pollack, Mark H; McNally, Richard J

2007-01-01

Among Cambodian refugees attending a psychiatric clinic, we assessed psychopathology associated with gastrointestinal panic (GIP), and investigated possible causal mechanisms, including "fear of fear" and GIP-associated flashbacks and catastrophic cognitions. GIP (n=46) patients had greater psychopathology (Clinician-Administered PTSD Scale [CAPS] and Symptom Checklist-90-R [SCL]) and "fear of fear" (Anxiety Sensitivity Index [ASI]) than did non-GIP patients (n=84). Logistic regression revealed that general psychopathology (SCL; odds ratio=4.1) and fear of anxiety-related sensations (ASI; odds ratio=2.4) predicted the presence of GIP. Among GIP patients, a hierarchical regression revealed that GIP-associated trauma recall and catastrophic cognitions explained variance in GIP severity beyond a measure of general psychopathology (SCL). A mediational analysis indicated that SCL's effect on GIP severity was mediated by GIP-associated flashbacks and catastrophic cognitions.

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.

Ecology, distribution, and predictive occurrence modeling of Palmers chipmunk (Tamias palmeri): a high-elevation small mammal endemic to the Spring Mountains in southern Nevada, USA

USGS Publications Warehouse

Lowrey, Chris E.; Longshore, Kathleen M.; Riddle, Brett R.; Mantooth, Stacy

2016-01-01

Although montane sky islands surrounded by desert scrub and shrub steppe comprise a large part of the biological diversity of the Basin and Range Province of southwestern North America, comprehensive ecological and population demographic studies for high-elevation small mammals within these areas are rare. Here, we examine the ecology and population parameters of the Palmer’s chipmunk (Tamias palmeri) in the Spring Mountains of southern Nevada, and present a predictive GIS-based distribution and probability of occurrence model at both home range and geographic spatial scales. Logistic regression analyses and Akaike Information Criterion model selection found variables of forest type, slope, and distance to water sources as predictive of chipmunk occurrence at the geographic scale. At the home range scale, increasing population density, decreasing overstory canopy cover, and decreasing understory canopy cover contributed to increased survival rates.

A short generic measure of work stress in the era of globalization: effort-reward imbalance.

PubMed

Siegrist, Johannes; Wege, Natalia; Pühlhofer, Frank; Wahrendorf, Morten

2009-08-01

We evaluate psychometric properties of a short version of the original effort-reward imbalance (ERI) questionnaire. This measure is of interest in the context of assessing stressful work conditions in the era of economic globalization. In a representative sample of 10,698 employed men and women participating in the longitudinal Socio-Economic Panel (SOEP) in Germany, a short version of the ERI questionnaire was included in the 2006 panel wave. Structural equation modeling and logistic regression analysis were applied. In addition to satisfactory internal consistency of scales, a model representing the theoretical structure of the scales provided the best data fit in a competitive test (RMSEA = 0.059, CAIC = 4124.19). Scoring high on the ERI scales was associated with elevated risks of poor self-rated health. This short version of the ERI questionnaire reveals satisfactory psychometric properties, and can be recommended for further use in research and practice.

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

PubMed

Schell, Greggory J; Lavieri, Mariel S; Stein, Joshua D; Musch, David C

2013-12-21

Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.

Computing group cardinality constraint solutions for logistic regression problems.

PubMed

Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

2017-01-01

We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.

Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model.

PubMed

Ren, Yilong; Wang, Yunpeng; Wu, Xinkai; Yu, Guizhen; Ding, Chuan

2016-10-01

Red light running (RLR) has become a major safety concern at signalized intersection. To prevent RLR related crashes, it is critical to identify the factors that significantly impact the drivers' behaviors of RLR, and to predict potential RLR in real time. In this research, 9-month's RLR events extracted from high-resolution traffic data collected by loop detectors from three signalized intersections were applied to identify the factors that significantly affect RLR behaviors. The data analysis indicated that occupancy time, time gap, used yellow time, time left to yellow start, whether the preceding vehicle runs through the intersection during yellow, and whether there is a vehicle passing through the intersection on the adjacent lane were significantly factors for RLR behaviors. Furthermore, due to the rare events nature of RLR, a modified rare events logistic regression model was developed for RLR prediction. The rare events logistic regression method has been applied in many fields for rare events studies and shows impressive performance, but so far none of previous research has applied this method to study RLR. The results showed that the rare events logistic regression model performed significantly better than the standard logistic regression model. More importantly, the proposed RLR prediction method is purely based on loop detector data collected from a single advance loop detector located 400 feet away from stop-bar. This brings great potential for future field applications of the proposed method since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

PubMed

Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A

2013-08-01

As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings.

PubMed

Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay

2009-06-03

Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

NASA Astrophysics Data System (ADS)

Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.

Development of cultural belief scales for mammography screening.

PubMed

Russell, Kathleen M; Champion, Victoria L; Perkins, Susan M

2003-01-01

To develop instruments to measure culturally related variables that may influence mammography screening behaviors in African American women. Instrumentation methodology. Community organizations and public housing in the Indianapolis, IN, area. 111 African American women with a mean age of 60.2 years and 64 Caucasian women with a mean age of 60 years. After item development, scales were administered. Data were analyzed by factor analysis, item analysis via internal consistency reliability using Cronbach's alpha, and independent t tests and logistic regression analysis to test theoretical relationships. Personal space preferences, health temporal orientation, and perceived personal control. Space items were factored into interpersonal and physical scales. Temporal orientation items were loaded on one factor, creating a one-dimensional scale. Control items were factored into internal and external control scales. Cronbach's alpha coefficients for the scales ranged from 0.76-0.88. Interpersonal space preference, health temporal orientation, and perceived internal control scales each were predictive of mammography screening adherence. The three tested scales were reliable and valid. Scales, on average, did not differ between African American and Caucasian populations. These scales may be useful in future investigations aimed at increasing mammography screening in African American and Caucasian women.

Multiple network-constrained regressions expand insights into influenza vaccination responses.

PubMed

Avey, Stefan; Mohanty, Subhasis; Wilson, Jean; Zapata, Heidi; Joshi, Samit R; Siconolfi, Barbara; Tsang, Sui; Shaw, Albert C; Kleinstein, Steven H

2017-07-15

Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology. Here we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability. The R source code described in this article is publicly available at https://bitbucket.org/kleinstein/logminer . steven.kleinstein@yale.edu or stefan.avey@yale.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

The association between maternal serious psychological distress and child obesity at 3 years: a cross-sectional analysis of the UK Millennium Cohort Data.

PubMed

Ramasubramanian, L; Lane, S; Rahman, A

2013-01-01

  The prevalence of child obesity is increasing rapidly worldwide. Early childhood has been identified as a critical time period for the development of obesity. Maternal mental health and early life environment are crucial factors and have been linked to adverse child outcomes. The objective of the study was to examine the relationship between maternal serious psychological distress and obesity in early childhood.   A cross-sectional analysis of data from the Millennium Cohort Study was conducted. Subjects consisted of all natural mothers (n= 10 465) who had complete and plausible data for Kessler-6 scores, socio-demographic and anthropometric variables, and their children for whom anthropometric measurements were completed at age 3. Maternal serious psychological distress was defined as a score of 13 or more on the Kessler-6 scale. Obesity was defined as body mass index ≥95th centile of the 1990 reference chart for age and sex in children. The data were analysed using spss 16. Maternal socio-demographic factors that are known to influence maternal mental health and child obesity were identified and adjusted using multivariate logistic regression.   Of the 10 465 mother-child dyads, 3.5% of mothers had serious psychological distress and 5.5% of children were obese at 3 years of age. Logistic regression analysis showed that maternal serious psychological distress was associated with early childhood obesity (P= 0.01; OR 1.62, 95% CI 1.11, 2.37). After adjusting for potential confounding factors using multivariate logistic regression, maternal serious psychological distress remained significantly associated with early childhood obesity (P= 0.01; OR 1.59, 95% CI 1.08, 2.34).   The results show that maternal serious psychological distress is independently associated with early childhood obesity. © 2011 Blackwell Publishing Ltd.

[Study on the correlation among adolescents' family function, negative life events stress amount and suicide ideation].

PubMed

Zhang, Dongdong; Chen, Ling; Yin, Dan; Miao, Jinping; Sun, Yehuan

2014-07-01

To explore the correlation between suicide ideation and family function & negative life events, as well as other influential factors in adolescents, thus present a theoretical base for clinicians and school staff to develop intervention for those problems. By adopting current situation random sampling method, Self-Rating Idea of Suicide Scale, Adolescent Self-Rating Life Events Check List and Family APGAR Index were used to assess adolescents at random in a hygiene vocational school in Changzhou City, Jiangsu Province and a collage in Wuhu City, Anhui Province. 3700 questionnaires were granted, 3675 questionnaires were collected, among which 3620 were valid. Chi-square test, t-test, and univariate logistic regression were employed in univariate analysis, multivariate logistic regression was used in multivariate analysis. The detection rate of suicide ideation is 7.0%, and the top five suicide ideation characteristics were: poor academic performance (33.6%), serious family functional impairment (25.8%), lower-middle academic performance (11.7%), bad economic conditions (10.8%) and study in Grade Three (9.9%). Multiple logistic regression showed that the following three high-level stress amount in negative life events are most crucial for suicide ideation. They are "relationships" (OR = 1.135, 95% CI 1.071 - 1. 202), "academic pressure" (OR = 1.169, 95% CI 1.101 - 1.241), and "external events" (OR = 1.278, 95% CI 1.187 - 1.376). What' s more, the stress of attending higher grades (OR = 1.980, 95% CI 1.302 - 3.008), poor academic performance (OR = 7.206, 95% CI 1.745 - 9.789), moderate family functional impairment (OR = 2.562, 95% CI 1.527 - 2.892) and its serious level (OR = 8.287, 95% CI 3.154 - 6.917) are also influential factors for suicide ideation. Severe family functional impairment and high-level stress amount of negative life events produced the main factors of suicide ideation. Therefore, necessary and sufficient support should be given to adolescents by families and schools.

Artificial neural network in predicting craniocervical junction injury: an alternative approach to trauma patients.

PubMed

Bektaş, Frat; Eken, Cenker; Soyuncu, Secgin; Kilicaslan, Isa; Cete, Yildiray

2008-12-01

The aim of this study is to determine the efficiency of artificial intelligence in detecting craniocervical junction injuries by using an artificial neural network (ANN) that may be applicable in future studies of different traumatic injuries. Major head trauma patients with Glasgow Coma Scale

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

USGS Publications Warehouse

Staley, Dennis M.; Negri, Jacquelyn A.; Kean, Jason W.; Laber, Jayme L.; Tillery, Anne C.; Youberg, Ann M.

2016-06-30

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can generate dangerous flash floods and debris flows. To reduce public exposure to hazard, the U.S. Geological Survey produces post-fire debris-flow hazard assessments for select fires in the western United States. We use publicly available geospatial data describing basin morphology, burn severity, soil properties, and rainfall characteristics to estimate the statistical likelihood that debris flows will occur in response to a storm of a given rainfall intensity. Using an empirical database and refined geospatial analysis methods, we defined new equations for the prediction of debris-flow likelihood using logistic regression methods. We showed that the new logistic regression model outperformed previous models used to predict debris-flow likelihood.

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.

PubMed

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-06-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. Copyright © 2013 Elsevier Inc. All rights reserved.

A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Association among stress, personality traits, and sleep bruxism in children.

PubMed

Serra-Negra, Junia M; Paiva, Saul M; Flores-Mendoza, Carmen E; Ramos-Jorge, Maria L; Pordeus, Isabela A

2012-01-01

The purpose of this study was to determine the association among stress levels, personality traits, and sleep bruxism in children. A population-based case control study (proportion=1:2) was conducted involving 120 7- to 11-year-olds with sleep bruxism and 240 children without sleep bruxism. The sample was randomly selected from schools in Belo Horizonte, Minas Gerais, Brazil. The following instruments were used for data collection: questionnaire administered to parents; child stress scale; and neuroticism and responsibility scales of the big five questionnaire for children. Psychological tests were administered and evaluated by psychologists. Sleep bruxism was diagnosed from parents' reports. The chi-square test, as well as binary and multivariate logistic regression, was applied for statistical analysis. In the adjusted logistic model, children with a high level of stress, due to psychological reactions (odds ratio=1.8; confidence interval=1.1-2.9) and a high sense of responsibility (OR=1.6; CI=1.0-2.5) vs those with low levels of these psychological traits, presented a nearly 2-fold greater chance of exhibiting the habit of sleep bruxism. High levels of stress and responsibility are key factors in the development of sleep bruxism among children.

Linkages between gender equity and intimate partner violence among urban Brazilian youth.

PubMed

Gomez, Anu Manchikanti; Speizer, Ilene S; Moracco, Kathryn E

2011-10-01

Gender inequity is a risk factor for intimate partner violence (IPV), although there is little research on this relationship that focuses on youth or males. Using survey data collected from 240 male and 198 female youth aged 15-24 in Rio de Janeiro, Brazil, we explore the association between individual-level support for gender equity and IPV experiences in the past 6 months and describe responses to and motivations for IPV. Factor analysis was used to construct gender equity scales for males and females. Logistic and multinomial logistic regression models were used to examine the relationship between gender equity and IPV. About half of female youth reported some form of recent IPV, including any victimization (32%), any perpetration (40%), and both victimization and perpetration (22%). A total of 18% of male youth reported recently perpetrating IPV. In logistic regression models, support for gender equity had a protective effect against any female IPV victimization and any male IPV perpetration and was not associated with female IPV perpetration. Female victims reported leaving the abusive partner, but later returning to him as the most frequent response to IPV. Male perpetrators said the most common response of their victims was to retaliate with violence. Jealousy was the most frequently reported motivation of females perpetrating IPV. Gender equity is an important predictor of IPV among youth. Examining the gendered context of IPV will be useful in the development of targeted interventions to promote gender equity and healthy relationships and to help reduce IPV among youth. Copyright © 2011 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Have infant gross motor abilities changed in 20 years? A re-evaluation of the Alberta Infant Motor Scale normative values

PubMed Central

Darrah, Johanna; Bartlett, Doreen; Maguire, Thomas O; Avison, William R; Lacaze-Masmonteil, Thierry

2014-01-01

Aim To compare the original normative data of the Alberta Infant Motor Scale (AIMS) (n=2202) collected 20 years ago with a contemporary sample of Canadian infants. Method This was a cross-sectional cohort study of 650 Canadian infants (338 males, 312 females; mean age 30.9wks [SD 15.5], range 2wks–18mo) assessed once on the AIMS. Assessments were stratified by age, and infants proportionally represented the ethnic diversity of Canada. Logistic regression was used to place AIMS items on an age scale representing the age at which 50% of the infants passed an item on the contemporary data set and the original data set. Forty-three items met the criterion for stable regression results in both data sets. Results The correlation coefficient between the age locations of items on the original and contemporary data sets was 0.99. The mean age difference between item locations was 0.7 weeks. Age values from the original data set when converted to the contemporary scale differed by less than 1 week. Interpretation The sequence and age at emergence of AIMS items has remained similar over 20 years and current normative values remain valid. Concern that the ‘back to sleep’ campaign has influenced the age at emergence of gross motor abilities is not supported. PMID:24684556

[Development of competency to stand trial rating scale in offenders with mental disorders].

PubMed

Chen, Xiao-Bing; Cai, Wei-Xiong

2013-04-01

According with Chinese legal system, to develop a competency to stand trial rating scale in offenders with mental disorders. Proceeding from the juristical elements, 15 items were extracted and formulated a preliminary instrument named the competency to stand trial rating scale in offenders with mental disorders. The item analysis included six aspects, which were critical ratio, item-total correlation, corrected item-total correlation, alpha value if item deleted, communalities of items, and factor loading. The Logistic regression equation and cut-off score of ROC curve were used to explore the diagnostic efficiency. The data of critical ratio of extreme group were 18.390-46.763; item-total correlation, 0.639-0.952; corrected item-total correlation, 0.582-0.944; communalities of items, 0.377-0.916; and factor loadings, 0.614-0.957. Seven items were included in the regression equation and the accuracy of back substitution test was 96.0%. The score of 33 was ascertained as the cut-off score by ROC fitting curve, the overlapping ratio compared with the expertise was 95.8%. The sensibility and the specificity were 0.938 and 0.966, respectively, while the positive and negative likelihood ratios were 27.67 and 0.06, respectively. With all items satisfied the requirement of homogeneity test, the rating scale has a reasonable construct and excellent diagnostic efficiency.

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

Lower urinary tract symptoms and erectile dysfunction associated with depression among Japanese patients with late-onset hypogonadism symptoms.

PubMed

Takao, Tetsuya; Tsujimura, Akira; Okuda, Hidenobu; Yamamoto, Keisuke; Fukuhara, Shinichiro; Matsuoka, Yasuhiro; Miyagawa, Yasushi; Nonomura, Norio; Okuyama, Akihiko

2011-06-01

The aim of this study was to investigate the relation between lower urinary tract symptoms (LUTS), erectile dysfunction (ED) and depression in Japanese patients with late-onset hypogonadism (LOH) symptoms. The study comprised 87 Japanese patients with LOH symptoms (>27 points on the Aging Males Symptoms Scale). Thirty-four patients were diagnosed as having depression and the remaining 53 patients were diagnosed as not having depression by the Mini International Neuropsychiatric Interview. We compared the International Index of Erectile Function (IIEF) 5, International Prostate Symptom Score (IPSS), IPSS quality-of-life (QOL) index, King's Health Questionnaire (KHQ), endocrinological data, and free uroflow study between depression and non-depression patients and performed multiple logistic regression analysis. IIEF5 scores of depression patients were significantly lower than those of non-depression patients. In KHQ, only the category of general health perceptions was significantly higher in depression patients than non-depression patients. However, IPSS, QOL index, and endocrinological and uroflowmetric data showed no significant difference between the groups. Multiple logistic regression analysis revealed moderate and severe ED to be risk factors for depression. However, LUTS are not related to depression. Moderate and severe ED is correlated with depression, whereas LUTS are not related to depression in Japanese LOH patients.

Prevalence and Risk Factors of Maternal Anxiety in Late Pregnancy in China.

PubMed

Kang, Yu-Ting; Yao, Yan; Dou, Jing; Guo, Xin; Li, Shu-Yue; Zhao, Cai-Ning; Han, Hong-Zhi; Li, Bo

2016-05-04

A large number of studies have shown the adverse neonatal outcomes of maternal psychological ill health. Given the potentially high prevalence of antenatal anxiety and few studies performed among Chinese people, the authors wanted to investigate the prevalence of antenatal anxiety and associated factors among pregnant women and to provide scientific basis to reduce prenatal anxiety effectively. A cross-sectional study was carried out at the Changchun Gynecology and Obstetrics Hospital from January 2015 to march 2015, with 467 participants of at least 38 weeks' gestation enrolled. Antenatal anxiety was measured using the Self-Rating Anxiety Scale (SAS). χ² test and logistic regression analysis were performed to evaluate the association of related factors of antenatal anxiety. Among the 467 participants, the prevalence of antenatal anxiety was 20.6% (96 of 467). After adjustment for women's socio-demographic characteristics (e.g., area, age, household income), multivariate logistical regression analysis revealed that antenatal anxiety showed significant relationship with education level lower than middle school (years ≤ 9), expected natural delivery, anemia during pregnancy, pregnancy-induced hypertension syndrome, disharmony in family relationship and life satisfaction. It is important to prevent or reduce antenatal anxiety from occurring by improving the health status of pregnant women and strengthening prenatal-related education and mental intervention.

Medication adherence among patients in a chronic disease clinic.

PubMed

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

2012-12-01

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

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data

PubMed Central

Sharafi, Zahra

2017-01-01

Background The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. Methods The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Results Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. Conclusions The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed. PMID:29312463

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data.

PubMed

Sharafi, Zahra; Mousavi, Amin; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman

2017-01-01

The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.

Characteristics of aggression in a German psychiatric hospital and predictors of patients at risk.

PubMed

Ketelsen, R; Zechert, C; Driessen, M; Schulz, M

2007-02-01

This study investigated the aggressive behaviour of all mentally ill patients within a whole psychiatric hospital with a catchment area of 325 000 inhabitants over a 1-year period (i) to assess the 1-year prevalence and characteristics of aggressive episodes and index inpatients, and (ii) to identify predictors of patients at risk by a multivariate approach. Staff Observation of Aggression Scale was used to assess aggressive behaviour. Characteristics of index inpatients were compared with those of non-index inpatients. Logistic regression analysis was applied to identify risk factors. A total of 171 out of 2210 admitted patients (7.7%) exhibited 441 aggressive incidents (1.7 incidents per bed per year). Logistic regression analyses revealed as major risk factors of aggression: diagnoses (organic brain syndromes OR = 3.6, schizophrenia OR = 2.9), poor psychosocial living conditions (OR = 2.2), and critical behaviour leading to involuntary admission (OR = 3.3). Predictors of aggressive behaviour can be useful to identify inpatients at risk. Nevertheless, additional situational determinants have to be recognized. Training for professionals should include preventive and de-escalating strategies to reduce the incidence of aggressive behaviour in psychiatric hospitals. The application of de-escalating interventions prior to admission might be effective in preventing aggressive behaviour during inpatient treatment especially for patients with severe mental disorders.

Physician job satisfaction in Saudi Arabia: insights from a tertiary hospital survey.

PubMed

Aldrees, Turki; Al-Eissa, Sami; Badri, Motasim; Aljuhayman, Ahmed; Zamakhshary, Mohammed

2015-01-01

Job satisfaction refers to the extent to which people like or dislike their job. Job satisfaction varies across professions. Few studies have explored this issue among physicians in Saudi Arabia. The objective of this study is to determine the level and factors associated with job satisfaction among Saudi and non-Saudi physicians. In this cross-sectional study conducted in a major tertiary hospital in Riyadh, a 5-point Likert scale structured questionnaire was used to collect data on a wide range of socio-demographic, practice environment characteristics and level and consequences of job satisfaction from practicing physicians (consultants or residents) across different medical specialties. Logistic regression models were fitted to determine factors associated with job satisfaction. Of 344 participants, 300 (87.2%) were Saudis, 252 (73%) males, 255 (74%) married, 188 (54.7%) consultants and age [median (IQR)] was 32 (27-42.7) years. Overall, 104 (30%) respondents were dissatisfied with their jobs. Intensive care physicians were the most dissatisfied physicians (50%). In a multiple logistic regression model, income satisfaction (odds ratio [OR]=0.448 95% CI 0.278-0.723, P < .001) was the only factor independently associated with dissatisfaction. Factors adversely associated with physicians job satisfaction identified in this study should be addressed in governmental strategic planning aimed at improving the healthcare system and patient care.

Detecting Dementia Through Interactive Computer Avatars

PubMed Central

Adachi, Hiroyoshi; Ukita, Norimichi; Ikeda, Manabu; Kazui, Hiroaki; Kudo, Takashi; Nakamura, Satoshi

2017-01-01

This paper proposes a new approach to automatically detect dementia. Even though some works have detected dementia from speech and language attributes, most have applied detection using picture descriptions, narratives, and cognitive tasks. In this paper, we propose a new computer avatar with spoken dialog functionalities that produces spoken queries based on the mini-mental state examination, the Wechsler memory scale-revised, and other related neuropsychological questions. We recorded the interactive data of spoken dialogues from 29 participants (14 dementia and 15 healthy controls) and extracted various audiovisual features. We tried to predict dementia using audiovisual features and two machine learning algorithms (support vector machines and logistic regression). Here, we show that the support vector machines outperformed logistic regression, and by using the extracted features they classified the participants into two groups with 0.93 detection performance, as measured by the areas under the receiver operating characteristic curve. We also newly identified some contributing features, e.g., gap before speaking, the variations of fundamental frequency, voice quality, and the ratio of smiling. We concluded that our system has the potential to detect dementia through spoken dialog systems and that the system can assist health care workers. In addition, these findings could help medical personnel detect signs of dementia. PMID:29018636

Lower limb and associated injuries in frontal-impact road traffic collisions.

PubMed

Ammori, Mohannad B; Eid, Hani O; Abu-Zidan, Fikri M

2016-03-01

To study the relationship between severity of injury of the lower limb and severity of injury of the head, thoracic, and abdominal regions in frontal-impact road traffic collisions. Consecutive hospitalised trauma patients who were involved in a frontal road traffic collision were prospectively studied over 18 months. Patients with at least one Abbreviated Injury Scale (AIS) ≥3 or AIS 2 injuries within two AIS body regions were included. Patients were divided into two groups depending on the severity of injury to the head, chest or abdomen. Low severity group had an AIS < 2 and high severity group had an AIS ≥ 2. Backward likelihood logistic regression models were used to define significant factors affecting the severity of head, chest or abdominal injuries. Eighty-five patients were studied. The backward likelihood logistic regression model defining independent factors affecting severity of head injuries was highly significant (p =0.01, nagelkerke r square = 0.1) severity of lower limb injuries was the only significant factor (p=0.013) having a negative correlation with head injury (Odds ratio of 0.64 (95% CI: 0.45-0.91). Occupants who sustain a greater severity of injury to the lower limb in a frontal-impact collision are likely to be spared from a greater severity of head injury.

Internalized homophobia, mental health, sexual behaviors, and outness of gay/bisexual men from Southwest China.

PubMed

Xu, Wenjian; Zheng, Lijun; Xu, Yin; Zheng, Yong

2017-02-17

Social attitudes toward male homosexuality in China so far are still not optimistic. Sexual minorities in China have reported high levels of internalized homophobia. This Internet-based study examined the associations among internalized homophobia, mental health, sexual behaviors, and outness among 435 gay/bisexual men in Southwest China from 2014 to 2015. Latent profile analysis, confirmatory factor analysis, univariate logistic regression, and separate multivariate logistic regression analyses were conducted. This descriptive study found the Internalized Homophobia Scale to be suitable for use in China. The sample demonstrated a high prevalence of internalized homophobia. Latent profile analysis suggested a 2-class solution as optimal, and a high level of internalized homophobia was significantly associated with greater psychological distress (Wald = 6.49, AOR = 1.66), transactional sex during the previous 6 months (Wald = 5.23, AOR = 2.77), more sexual compulsions (Wald = 14.05, AOR = 2.12), and the concealment of sexual identity from others (Wald = 30.70, AOR = 0.30) and parents (Wald = 6.72, AOR = 0.49). These findings contribute to our understanding of internalized homophobia in China, and highlight the need to decrease gay-related psychological stress/distress and improve public health services.

Impact of major depressive disorder, distinct subtypes, and symptom severity on lifestyle in the BiDirect Study.

PubMed

Rahe, Corinna; Khil, Laura; Wellmann, Jürgen; Baune, Bernhard T; Arolt, Volker; Berger, Klaus

2016-11-30

The aim of this study was to examine associations of major depressive disorder (MDD), its distinct subtypes, and symptom severity with the individual lifestyle factors smoking, diet quality, physical activity, and body mass index as well as with a combined lifestyle index measuring the co-occurrence of these lifestyle factors. A sample of 823 patients with MDD and 597 non-depressed controls was examined. The psychiatric assessment was based on a clinical interview including the Mini International Neuropsychiatric Interview and the Hamilton Depression Rating Scale. Each lifestyle factor was scored as either healthy or unhealthy, and the number of unhealthy lifestyle factors was added up in a combined lifestyle index. Cross-sectional analyses were performed using alternating logistic regression and ordinal logistic regression, adjusted for socio-demographic characteristics. After adjustment, MDD was significantly associated with smoking, low physical activity, and overweight. Likewise, MDD was significantly related to the overall lifestyle index. When stratifying for subtypes, all subtypes showed higher odds for an overall unhealthier lifestyle than controls, but the associations with the individual lifestyle factors were partly different. Symptom severity was associated with the lifestyle index in a dose-response manner. In conclusion, patients with MDD represent an important target group for lifestyle interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Computed tomography pulmonary embolism index for the assessment of survival in patients with pulmonary embolism.

PubMed

Pech, Maciej; Wieners, Gero; Dul, Przemyslaw; Fischbach, Frank; Dudeck, Oliver; Lopez Hänninen, Enrique; Ricke, Jens

2007-08-01

This study was an analysis of the correlation between pulmonary embolism (PE) and patient survival. Among 694 consecutive patients referred to our institution with clinical suspicion of acute PE who underwent CT pulmonary angiography, 188 patients comprised the study group: 87 women (46.3%, median age: 60.7; age range: 19-88 years) and 101 men (53.7%, median age: 66.9; age range: 21-97 years). PE was assessed by two radiologist who were blinded to the results from the follow-up. A PE index was derived for each set of images on the basis of the embolus size and location. Results were analyzed using logistic regression, and correlation with risk factors and patient outcome (survival or death) was calculated. We observed no significant correlation between the CTPE index and patient outcome (p = 0.703). The test of logistic regression with the sum of heart and liver disease or presence of cancer was significantly (p< 0.05) correlated with PE and overall patient outcome. Interobserver agreement showed a significant correlation rate for the assessment of the PE index (0.993; p< 0.001). In our study the CT PE index did not translate into patient outcome. Prospective larger scale studies are needed to confirm the predictive value of the index and refine the index criteria.

The prevalence of postpartum depression: the relative significance of three social status indices.

PubMed

Segre, Lisa S; O'Hara, Michael W; Arndt, Stephan; Stuart, Scott

2007-04-01

Little is known about the prevalence of clinically significant postpartum depression in women of varying social status. The purpose of the present study was to examine the prevalence of postpartum depression as a function of three indices of social status: income, education and occupational prestige. A sample of 4,332 postpartum women completed a demographic interview and the Inventory to Diagnose Depression, a self-report scale developed to identify a major depressive episode in accordance with DSM diagnostic criteria. Logistic regression was used to assess the relative significance of the three social status variables as risk factors for postpartum depression controlling for the effects of correlated demographic variables. In the logistic regression, income, occupational prestige, marital status, and number of children were significant predictors of postpartum depression controlling for the effects of other related demographic characteristics. The Wald Chi Square value for each of these significant predictors indicates that income was the strongest predictor. The prevalence of postpartum depression was significantly higher in financially poor relative to financially affluent women. Maternal depression screening programs targeting women who are financially poor are well placed. Future research is needed to replicate the present findings in a more ethnically diverse sample that includes the full age range of teenage mothers.

Pregnancy and Race/Ethnicity as Predictors of Motivation for Drug Treatment

PubMed Central

Mitchell, Mary M.; Severtson, S. Geoff; Latimer, William W.

2009-01-01

While drug use during pregnancy represents substantial obstetrical risks to mother and baby, little research has examined motivation for drug treatment among pregnant women. We analyzed data collected between 2000 and 2007 from 149 drug-using women located in Baltimore, Maryland. We hypothesized that pregnant drug-using women would be more likely than their non-pregnant counterparts to express greater motivation for treatment. Also, we explored race/ethnicity differences in motivation for treatment. Propensity scores were used to match a sample of 49 pregnant drug-using women with 100 non-pregnant drug-using women. A factor analysis using 11 items from a readiness for treatment scale was used to create a dichotomous outcome variable representing higher and lower levels of motivation for treatment. The first logistic regression model indicated that pregnant women were more than four times as likely as non-pregnant women to express greater motivation for treatment. The second logistic regression analysis indicated a significant interaction between pregnancy status and race/ethnicity, such that white pregnant women were nearly eight times as likely as African-American pregnant women to score higher on the motivation for treatment measure. These results suggest that African-American pregnant drug-using women should be targeted for interventions that increase their motivation for treatment. PMID:18584569

Effects of BMI on the risk and frequency of AIS 3+ injuries in motor-vehicle crashes.

PubMed

Rupp, Jonathan D; Flannagan, Carol A C; Leslie, Andrew J; Hoff, Carrie N; Reed, Matthew P; Cunningham, Rebecca M

2013-01-01

Determine the effects of BMI on the risk of serious-to-fatal injury (Abbreviated Injury Scale ≥ 3 or AIS 3+) to different body regions for adults in frontal, nearside, farside, and rollover crashes. Multivariate logistic regression analysis was applied to a probability sample of adult occupants involved in crashes generated by combining the National Automotive Sampling System (NASS-CDS) with a pseudoweighted version of the Crash Injury Research and Engineering Network database. Logistic regression models were applied to weighted data to estimate the change in the number of occupants with AIS 3+ injuries if no occupants were obese. Increasing BMI increased risk of lower-extremity injury in frontal crashes, decreased risk of lower-extremity injury in nearside impacts, increased risk of upper-extremity injury in frontal and nearside crashes, and increased risk of spine injury in frontal crashes. Several of these findings were affected by interactions with gender and vehicle type. If no occupants in frontal crashes were obese, 7% fewer occupants would sustain AIS 3+ upper-extremity injuries, 8% fewer occupants would sustain AIS 3+ lower-extremity injuries, and 28% fewer occupants would sustain AIS 3+ spine injuries. Results of this study have implications on the design and evaluation of vehicle safety systems. Copyright © 2013 The Obesity Society.

The relationship between depressive symptoms among female workers and job stress and sleep quality.

PubMed

Cho, Ho-Sung; Kim, Young-Wook; Park, Hyoung-Wook; Lee, Kang-Ho; Jeong, Baek-Geun; Kang, Yune-Sik; Park, Ki-Soo

2013-07-22

Recently, workers' mental health has become important focus in the field of occupational health management. Depression is a psychiatric illness with a high prevalence. The association between job stress and depressive symptoms has been demonstrated in many studies. Recently, studies about the association between sleep quality and depressive symptoms have been reported, but there has been no large-scaled study in Korean female workers. Therefore, this study was designed to investigate the relationship between job stress and sleep quality, and depressive symptoms in female workers. From Mar 2011 to Aug 2011, 4,833 female workers in the manufacturing, finance, and service fields at 16 workplaces in Yeungnam province participated in this study, conducted in combination with a worksite-based health checkup initiated by the National Health Insurance Service (NHIS). In this study, a questionnaire survey was carried out using the Korean Occupational Stress Scale-Short Form(KOSS-SF), Pittsburgh Sleep Quality Index(PSQI) and Center for Epidemiological Studies-Depression Scale(CES-D). The collected data was entered in the system and analyzed using the PASW (version 18.0) program. A correlation analysis, cross analysis, multivariate logistic regression analysis, and hierarchical multiple regression analysis were conducted. Among the 4,883 subjects, 978 subjects (20.0%) were in the depression group. Job stress(OR=3.58, 95% CI=3.06-4.21) and sleep quality(OR=3.81, 95% CI=3.18-4.56) were strongly associated with depressive symptoms. Hierarchical multiple regression analysis revealed that job stress displayed explanatory powers of 15.6% on depression while sleep quality displayed explanatory powers of 16.2%, showing that job stress and sleep quality had a closer relationship with depressive symptoms, compared to the other factors. The multivariate logistic regression analysis yielded odds ratios between the 7 subscales of job stress and depressive symptoms in the range of 1.30-2.72 and the odds ratio for the lack of reward was the highest(OR=2.72, 95% CI=2.32-3.19). In the partial correlation analysis between each of the 7 subscales of sleep quality (PSQI) and depressive symptoms, the correlation coefficient of subjective sleep quality and daytime dysfunction were 0.352 and 0.362, respectively. This study showed that the depressive symptoms of female workers are closely related to their job stress and sleep quality. In particular, the lack of reward and subjective sleep factors are the greatest contributors to depression. In the future, a large-scale study should be performed to augment the current study and to reflect all age groups in a balanced manner. The findings on job stress, sleep, and depression can be utilized as source data to establish standards for mental health management of the ever increasing numbers of female members of the workplace.

Science of Test Research Consortium: Year Two Final Report

DTIC Science & Technology

2012-10-02

July 2012. Analysis of an Intervention for Small Unmanned Aerial System ( SUAS ) Accidents, submitted to Quality Engineering, LQEN-2012-0056. Stone... Systems Engineering. Wolf, S. E., R. R. Hill, and J. J. Pignatiello. June 2012. Using Neural Networks and Logistic Regression to Model Small Unmanned ...Human Retina. 6. Wolf, S. E. March 2012. Modeling Small Unmanned Aerial System Mishaps using Logistic Regression and Artificial Neural Networks. 7

Binary Logistic Regression Analysis for Detecting Differential Item Functioning: Effectiveness of R[superscript 2] and Delta Log Odds Ratio Effect Size Measures

ERIC Educational Resources Information Center

Hidalgo, Mª Dolores; Gómez-Benito, Juana; Zumbo, Bruno D.

2014-01-01

The authors analyze the effectiveness of the R[superscript 2] and delta log odds ratio effect size measures when using logistic regression analysis to detect differential item functioning (DIF) in dichotomous items. A simulation study was carried out, and the Type I error rate and power estimates under conditions in which only statistical testing…

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Treesearch

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Comparison of four methods for deriving hospital standardised mortality ratios from a single hierarchical logistic regression model.

PubMed

Mohammed, Mohammed A; Manktelow, Bradley N; Hofer, Timothy P

2016-04-01

There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable. © The Author(s) 2012.

Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results.

PubMed

Chan, Siew Foong; Deeks, Jonathan J; Macaskill, Petra; Irwig, Les

2008-01-01

To compare three predictive models based on logistic regression to estimate adjusted likelihood ratios allowing for interdependency between diagnostic variables (tests). This study was a review of the theoretical basis, assumptions, and limitations of published models; and a statistical extension of methods and application to a case study of the diagnosis of obstructive airways disease based on history and clinical examination. Albert's method includes an offset term to estimate an adjusted likelihood ratio for combinations of tests. Spiegelhalter and Knill-Jones method uses the unadjusted likelihood ratio for each test as a predictor and computes shrinkage factors to allow for interdependence. Knottnerus' method differs from the other methods because it requires sequencing of tests, which limits its application to situations where there are few tests and substantial data. Although parameter estimates differed between the models, predicted "posttest" probabilities were generally similar. Construction of predictive models using logistic regression is preferred to the independence Bayes' approach when it is important to adjust for dependency of tests errors. Methods to estimate adjusted likelihood ratios from predictive models should be considered in preference to a standard logistic regression model to facilitate ease of interpretation and application. Albert's method provides the most straightforward approach.

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Species Composition at the Sub-Meter Level in Discontinuous Permafrost in Subarctic Sweden

NASA Astrophysics Data System (ADS)

Anderson, S. M.; Palace, M. W.; Layne, M.; Varner, R. K.; Crill, P. M.

2013-12-01

Northern latitudes are experiencing rapid warming. Wetlands underlain by permafrost are particularly vulnerable to warming which results in changes in vegetative cover. Specific species have been associated with greenhouse gas emissions therefore knowledge of species compositional shift allows for the systematic change and quantification of emissions and changes in such emissions. Species composition varies on the sub-meter scale based on topography and other microsite environmental parameters. This complexity and the need to scale vegetation to the landscape level proves vital in our estimation of carbon dioxide (CO2) and methane (CH4) emissions and dynamics. Stordalen Mire (68°21'N, 18°49'E) in Abisko and is located at the edge of discontinuous permafrost zone. This provides a unique opportunity to analyze multiple vegetation communities in a close proximity. To do this, we randomly selected 25 1x1 meter plots that were representative of five major cover types: Semi-wet, wet, hummock, tall graminoid, and tall shrub. We used a quadrat with 64 sub plots and measured areal percent cover for 24 species. We collected ground based remote sensing (RS) at each plot to determine species composition using an ADC-lite (near infrared, red, green) and GoPro (red, blue, green). We normalized each image based on a Teflon white chip placed in each image. Textural analysis was conducted on each image for entropy, angular second momentum, and lacunarity. A logistic regression was developed to examine vegetation cover types and remote sensing parameters. We used a multiple linear regression using forwards stepwise variable selection. We found statistical difference in species composition and diversity indices between vegetation cover types. In addition, we were able to build regression model to significantly estimate vegetation cover type as well as percent cover for specific key vegetative species. This ground-based remote sensing allows for quick quantification of vegetation cover and species and also provides the framework for scaling to satellite image data to estimate species composition and shift on the landscape level. To determine diversity within our plots we calculated species richness and Shannon Index. We found that there were statistically different species composition within each vegetation cover type and also determined which species were indicative for cover type. Our logistical regression was able to significantly classify vegetation cover types based on RS parameters. Our multiple regression analysis indicated Betunla nana (Dwarf Birch) (r2= .48, p=<0.0001) and Sphagnum (r2=0.59, p=<0.0001) were statistically significant with respect to RS parameters. We suggest that ground based remote sensing methods may provide a unique and efficient method to quantify vegetation across the landscape in northern latitude wetlands.

[Suicide attempts among Chilean adolescents].

PubMed

Valdivia, Mario; Silva, Daniel; Sanhueza, Félix; Cova, Félix; Melipillán, Roberto

2015-03-01

Suicide mortality rates are increasing among teenagers. To study the prevalence and predictive factors of suicide attempts among Chilean adolescents. A random sample of 195 teenagers aged 16 ± 1 years (53% males) answered an anonymous survey about their demographic features, substance abuse, the Osaka suicidal ideation questionnaire, Smilksten familial Apgar. Beck hopelessness scale, Beck depression scale and Coppersmith self-esteem inventory. Twenty five percent of respondents had attempted suicide at least in one occasion during their lives. These attempts were significantly associated with female gender, absent parents, family dysfunction, drug abuse, smoking, low self-esteem, hopelessness, depression and recent suicidal ideation. A logistic regression analysis accepted female gender, smoking and recent suicidal ideation as significant independent predictors of suicide attempt. Suicide attempted is common among teenagers and its predictors are female sex, smoking and previous suicidal ideation.

HYPNOTIZABILITY, POSTTRAUMATIC STRESS, AND DEPRESSIVE SYMPTOMS IN METASTATIC BREAST CANCER1

PubMed Central

Keuroghlian, Alex S.; Butler, Lisa D.; Neri, Eric; Spiegel, David

2013-01-01

This study assessed whether high hypnotizability is associated with posttraumatic stress and depressive symptoms in a sample of 124 metastatic breast cancer patients. Hypnotic Induction Profile Scores were dichotomized into low and high categories; posttraumatic intrusion and avoidance symptoms were measured with the Impact of Events Scale (IES); hyperarousal symptoms with items from the Profile of Mood States; and depressive symptoms with the Center for Epidemiologic Studies-Depression Scale. High hypnotizability was significantly related to greater IES total, IES intrusion symptoms, and depressive symptoms. A logistic regression model showed that IES total predicts high hypnotizability after adjusting for depressive symptoms and hyperarousal. The authors relate these results to findings in other clinical populations and discuss implications for the psychosocial treatment of metastatic breast cancer. PMID:20183737

Results of a 100-point scale for evaluating job satisfaction and the Occupational Depression Scale questionnaire survey in workers.

PubMed

Kawada, Tomoyuki; Yoshimura, Miwako

2012-04-01

To evaluate the relationship between the score of job satisfaction and depression. A total of 2737 workers (2198 men and 539 women) participated. A 100-point scale for evaluating job satisfaction and the Occupational Depression Scale were used. A logistic regression analysis was applied with adjustment for age. The mean age of the subjects was 42.2 years for men and 36.0 years for women. When the group with the highest job satisfaction score was set as the control, the odds ratios and 95% confidence intervals for depression in the groups with the lowest and second lowest scores were 16.3 (7.51 to 35.2) and 5.90 (2.70 to 12.9) in men and 8.02 (1.78 to 36.1) and 5.68 (1.26 to 25.7) in women, respectively. Job satisfaction was significantly associated with the depressive state, and causality should be clarified by a follow-up study.

Impact of Non-Suicidal Self-Injury Scale: Initial Psychometric Validation

PubMed Central

Burke, Taylor A.; Ammerman, Brooke A.; Hamilton, Jessica L.; Alloy, Lauren B.

2017-01-01

The current study examined the psychometric properties of the Impact of Non-Suicidal Self-Injury Scale (INS), a scale developed to assess the social, behavioral, and emotional consequences of engaging in non-suicidal self-injury (NSSI). University students (N=128) who endorsed a history of NSSI were administered the INS, as well as measures of hypothesized convergent and divergent validity. Results suggested that the INS is best conceptualized as a one-factor scale, and internal consistency analyses indicated excellent reliability. The INS was significantly correlated with well-known measures of NSSI severity (i.e., NSSI frequency, NSSI recency), and measures of suicide attempt history and emotional reactivity. Logistic regression analyses indicated that the INS contributed unique variance to the prediction of physical disfigurement (i.e., NSSI scarring) and clinically significant social anxiety, even after taking into account NSSI frequency. Furthermore, the INS demonstrated divergent validity. Implications for research on NSSI disorder and clinical practice are discussed. PMID:28824214

Reliability, Factor Structure, and Associations With Measures of Problem Relationship and Behavior of the Personality Inventory for DSM-5 in a Sample of Italian Community-Dwelling Adolescents.

PubMed

Somma, Antonella; Borroni, Serena; Maffei, Cesare; Giarolli, Laura E; Markon, Kristian E; Krueger, Robert F; Fossati, Andrea

2017-10-01

In order to assess the reliability, factorial validity, and criterion validity of the Personality Inventory for DSM-5 (PID-5) among adolescents, 1,264 Italian high school students were administered the PID-5. Participants were also administered the Questionnaire on Relationships and Substance Use as a criterion measure. In the full sample, McDonald's ω values were adequate for the PID-5 scales (median ω = .85, SD = .06), except for Suspiciousness. However, all PID-5 scales showed average inter-item correlation values in the .20-.55 range. Exploratory structural equation modeling analyses provided moderate support for the a priori model of PID-5 trait scales. Ordinal logistic regression analyses showed that selected PID-5 trait scales predicted a significant, albeit moderate (Cox & Snell R 2 values ranged from .08 to .15, all ps < .001) amount of variance in Questionnaire on Relationships and Substance Use variables.

Prevalence and some psychosocial characteristics of social anxiety disorder in an urban population of Turkish children and adolescents.

PubMed

Demir, T; Karacetin, G; Eralp Demir, D; Uysal, O

2013-01-01

To define the prevalence and some of the psychosocial characteristics of social anxiety disorder (SAD) in an urban population of Turkish children and adolescents. This was a two-stage cross-sectional urban-based study conducted in Fatih, Istanbul, Turkey. The initial sample included 1,482 students between the 4th and 8th grades. The first stage involved screening using the Social Anxiety Scale for Children-Revised (SASC-R) and the Capa Social Phobia Scale for Children and Adolescents (CSPSCA). According to the test results, 324 children were interviewed using the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL) in the second stage. The SAD prevalence rate was 3.9%. According to the multiple regression analysis, low paternal education and trait anxiety were associated with SASC-R scores, whereas female gender and trait anxiety were associated with CSPSCA scores. According to logistic regression analysis, the anxiety subscale of the self-concept scale and trait anxiety were associated with SAD. SAD is a relatively common disorder that is associated with lower self-concept in children and adolescents. Low paternal education, trait anxiety, and low self-concept may be the intervention targets for SAD prevention and treatment. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

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

PubMed

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

2018-03-13

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

Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders.

PubMed

Kupek, Emil

2006-03-15

Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.

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.

Long-term psychological outcomes of flood survivors of hard-hit areas of the 1998 Dongting Lake flood in China: Prevalence and risk factors

PubMed Central

Dai, Wenjie; Kaminga, Atipatsa C.; Tan, Hongzhuan; Wang, Jieru; Lai, Zhiwei; Wu, Xin; Liu, Aizhong

2017-01-01

Background Although numerous studies have indicated that exposure to natural disasters may increase survivors’ risk of post-traumatic stress disorder (PTSD) and anxiety, studies focusing on the long-term psychological outcomes of flood survivors are limited. Thus, this study aimed to estimate the prevalence of PTSD and anxiety among flood survivors 17 years after the 1998 Dongting Lake flood and to identify the risk factors for PTSD and anxiety. Methods This cross-sectional study was conducted in December 2015, 17 years after the 1998 Dongting Lake flood. Survivors in hard-hit areas of the flood disaster were enrolled in this study using a stratified, systematic random sampling method. Well qualified investigators conducted face-to-face interviews with participants using the PTSD Checklist-Civilian version, the Zung Self-Rating Anxiety Scale, the Chinese version of the Social Support Rating Scale and the Revised Eysenck Personality Questionnaire-Short Scale for Chinese to assess PTSD, anxiety, social support and personality traits, respectively. Logistic regression analyses were used to identify factors associated with PTSD and anxiety. Results A total of 325 participants were recruited in this study, and the prevalence of PTSD and anxiety was 9.5% and 9.2%, respectively. Multivariable logistic regression analyses indicated that female sex, experiencing at least three flood-related stressors, having a low level of social support, and having the trait of emotional instability were risk factors for long-term adverse psychological outcomes among flood survivors after the disaster. Conclusions PTSD and anxiety were common long-term adverse psychological outcomes among flood survivors. Early and effective psychological interventions for flood survivors are needed to prevent the development of PTSD and anxiety in the long run after a flood, especially for individuals who are female, experience at least three flood-related stressors, have a low level of social support and have the trait of emotional instability. PMID:28170427

Effect of the Modified Glasgow Coma Scale Score Criteria for Mild Traumatic Brain Injury on Mortality Prediction: Comparing Classic and Modified Glasgow Coma Scale Score Model Scores of 13

PubMed Central

Mena, Jorge Humberto; Sanchez, Alvaro Ignacio; Rubiano, Andres M.; Peitzman, Andrew B.; Sperry, Jason L.; Gutierrez, Maria Isabel; Puyana, Juan Carlos

2011-01-01

Objective The Glasgow Coma Scale (GCS) classifies Traumatic Brain Injuries (TBI) as Mild (14–15); Moderate (9–13) or Severe (3–8). The ATLS modified this classification so that a GCS score of 13 is categorized as mild TBI. We investigated the effect of this modification on mortality prediction, comparing patients with a GCS of 13 classified as moderate TBI (Classic Model) to patients with GCS of 13 classified as mild TBI (Modified Model). Methods We selected adult TBI patients from the Pennsylvania Outcome Study database (PTOS). Logistic regressions adjusting for age, sex, cause, severity, trauma center level, comorbidities, and isolated TBI were performed. A second evaluation included the time trend of mortality. A third evaluation also included hypothermia, hypotension, mechanical ventilation, screening for drugs, and severity of TBI. Discrimination of the models was evaluated using the area under receiver operating characteristic curve (AUC). Calibration was evaluated using the Hoslmer-Lemershow goodness of fit (GOF) test. Results In the first evaluation, the AUCs were 0.922 (95 %CI, 0.917–0.926) and 0.908 (95 %CI, 0.903–0.912) for classic and modified models, respectively. Both models showed poor calibration (p<0.001). In the third evaluation, the AUCs were 0.946 (95 %CI, 0.943 – 0.949) and 0.938 (95 %CI, 0.934 –0.940) for the classic and modified models, respectively, with improvements in calibration (p=0.30 and p=0.02 for the classic and modified models, respectively). Conclusion The lack of overlap between ROC curves of both models reveals a statistically significant difference in their ability to predict mortality. The classic model demonstrated better GOF than the modified model. A GCS of 13 classified as moderate TBI in a multivariate logistic regression model performed better than a GCS of 13 classified as mild. PMID:22071923

The English version of the four-dimensional symptom questionnaire (4DSQ) measures the same as the original Dutch questionnaire: a validation study.

PubMed

Terluin, Berend; Smits, Niels; Miedema, Baukje

2014-12-01

Translations of questionnaires need to be carefully validated to assure that the translation measures the same construct(s) as the original questionnaire. The four-dimensional symptom questionnaire (4DSQ) is a Dutch self-report questionnaire measuring distress, depression, anxiety and somatization. To evaluate the equivalence of the English version of the 4DSQ. 4DSQ data of English and Dutch speaking general practice attendees were analysed and compared. The English speaking group consisted of 205 attendees, aged 18-64 years, in general practice, in Canada whereas the Dutch group consisted of 302 general practice attendees in the Netherlands. Differential item functioning (DIF) analysis was conducted using the Mantel-Haenszel method and ordinal logistic regression. Differential test functioning (DTF; i.e., the scale impact of DIF) was evaluated using linear regression analysis. DIF was detected in 2/16 distress items, 2/6 depression items, 2/12 anxiety items, and 1/16 somatization items. With respect to mean scale scores, the impact of DIF on the scale level was negligible for all scales. On the anxiety scale DIF caused the English speaking patients with moderate to severe anxiety to score about one point lower than Dutch patients with the same anxiety level. The English 4DSQ measures the same constructs like the original Dutch 4DSQ. The distress, depression and somatization scales can employ the same cut-off points as the corresponding Dutch scales. However, cut-off points of the English 4DSQ anxiety scale should be lowered by one point to retain the same meaning as the Dutch anxiety cut-off points.

A Logistic Regression Analysis of Turkey's 15-Year-Olds' Scoring above the OECD Average on the PISA'09 Reading Assessment

ERIC Educational Resources Information Center

Kasapoglu, Koray

2014-01-01

This study aims to investigate which factors are associated with Turkey's 15-year-olds' scoring above the OECD average (493) on the PISA'09 reading assessment. Collected from a total of 4,996 15-year-old students from Turkey, data were analyzed by logistic regression analysis in order to model the data of students who were split into two: (1)…

Upgrade Summer Severe Weather Tool

NASA Technical Reports Server (NTRS)

Watson, Leela

2011-01-01

The goal of this task was to upgrade to the existing severe weather database by adding observations from the 2010 warm season, update the verification dataset with results from the 2010 warm season, use statistical logistic regression analysis on the database and develop a new forecast tool. The AMU analyzed 7 stability parameters that showed the possibility of providing guidance in forecasting severe weather, calculated verification statistics for the Total Threat Score (TTS), and calculated warm season verification statistics for the 2010 season. The AMU also performed statistical logistic regression analysis on the 22-year severe weather database. The results indicated that the logistic regression equation did not show an increase in skill over the previously developed TTS. The equation showed less accuracy than TTS at predicting severe weather, little ability to distinguish between severe and non-severe weather days, and worse standard categorical accuracy measures and skill scores over TTS.

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging.

PubMed

Chen, Jin-Hua; Chen, Chun-Shu; Huang, Meng-Fan; Lin, Hung-Chih

2016-10-01

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback-Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed. © 2016 Society for Risk Analysis.

Evaluating the perennial stream using logistic regression in central Taiwan

NASA Astrophysics Data System (ADS)

Ruljigaljig, T.; Cheng, Y. S.; Lin, H. I.; Lee, C. H.; Yu, T. T.

2014-12-01

This study produces a perennial stream head potential map, based on a logistic regression method with a Geographic Information System (GIS). Perennial stream initiation locations, indicates the location of the groundwater and surface contact, were identified in the study area from field survey. The perennial stream potential map in central Taiwan was constructed using the relationship between perennial stream and their causative factors, such as Catchment area, slope gradient, aspect, elevation, groundwater recharge and precipitation. Here, the field surveys of 272 streams were determined in the study area. The areas under the curve for logistic regression methods were calculated as 0.87. The results illustrate the importance of catchment area and groundwater recharge as key factors within the model. The results obtained from the model within the GIS were then used to produce a map of perennial stream and estimate the location of perennial stream head.

The use of logistic regression to enhance risk assessment and decision making by mental health administrators.

PubMed

Menditto, Anthony A; Linhorst, Donald M; Coleman, James C; Beck, Niels C

2006-04-01

Development of policies and procedures to contend with the risks presented by elopement, aggression, and suicidal behaviors are long-standing challenges for mental health administrators. Guidance in making such judgments can be obtained through the use of a multivariate statistical technique known as logistic regression. This procedure can be used to develop a predictive equation that is mathematically formulated to use the best combination of predictors, rather than considering just one factor at a time. This paper presents an overview of logistic regression and its utility in mental health administrative decision making. A case example of its application is presented using data on elopements from Missouri's long-term state psychiatric hospitals. Ultimately, the use of statistical prediction analyses tempered with differential qualitative weighting of classification errors can augment decision-making processes in a manner that provides guidance and flexibility while wrestling with the complex problem of risk assessment and decision making.

An application in identifying high-risk populations in alternative tobacco product use utilizing logistic regression and CART: a heuristic comparison.

PubMed

Lei, Yang; Nollen, Nikki; Ahluwahlia, Jasjit S; Yu, Qing; Mayo, Matthew S

2015-04-09

Other forms of tobacco use are increasing in prevalence, yet most tobacco control efforts are aimed at cigarettes. In light of this, it is important to identify individuals who are using both cigarettes and alternative tobacco products (ATPs). Most previous studies have used regression models. We conducted a traditional logistic regression model and a classification and regression tree (CART) model to illustrate and discuss the added advantages of using CART in the setting of identifying high-risk subgroups of ATP users among cigarettes smokers. The data were collected from an online cross-sectional survey administered by Survey Sampling International between July 5, 2012 and August 15, 2012. Eligible participants self-identified as current smokers, African American, White, or Latino (of any race), were English-speaking, and were at least 25 years old. The study sample included 2,376 participants and was divided into independent training and validation samples for a hold out validation. Logistic regression and CART models were used to examine the important predictors of cigarettes + ATP users. The logistic regression model identified nine important factors: gender, age, race, nicotine dependence, buying cigarettes or borrowing, whether the price of cigarettes influences the brand purchased, whether the participants set limits on cigarettes per day, alcohol use scores, and discrimination frequencies. The C-index of the logistic regression model was 0.74, indicating good discriminatory capability. The model performed well in the validation cohort also with good discrimination (c-index = 0.73) and excellent calibration (R-square = 0.96 in the calibration regression). The parsimonious CART model identified gender, age, alcohol use score, race, and discrimination frequencies to be the most important factors. It also revealed interesting partial interactions. The c-index is 0.70 for the training sample and 0.69 for the validation sample. The misclassification rate was 0.342 for the training sample and 0.346 for the validation sample. The CART model was easier to interpret and discovered target populations that possess clinical significance. This study suggests that the non-parametric CART model is parsimonious, potentially easier to interpret, and provides additional information in identifying the subgroups at high risk of ATP use among cigarette smokers.

Examining related influential factors for dental calculus scaling utilization among people with disabilities in Taiwan, a nationwide population-based study.

PubMed

Lai, Hsien-Tang; Kung, Pei-Tseng; Su, Hsun-Pi; Tsai, Wen-Chen

2014-09-01

Limited studies with large samples have been conducted on the utilization of dental calculus scaling among people with physical or mental disabilities. This study aimed to investigate the utilization of dental calculus scaling among the national disabled population. This study analyzed the utilization of dental calculus scaling among the disabled people, using the nationwide data between 2006 and 2008. Descriptive analysis and logistic regression were performed to analyze related influential factors for dental calculus scaling utilization. The dental calculus scaling utilization rate among people with physical or mental disabilities was 16.39%, and the annual utilization frequency was 0.2 times. Utilization rate was higher among the female and non-aboriginal samples. Utilization rate decreased with increased age and disability severity while utilization rate increased with income, education level, urbanization of residential area and number of chronic illnesses. Related influential factors for dental calculus scaling utilization rate were gender, age, ethnicity (aboriginal or non-aboriginal), education level, urbanization of residence area, income, catastrophic illnesses, chronic illnesses, disability types, and disability severity significantly influenced the dental calculus scaling utilization rate. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Classification and regression tree analysis of acute-on-chronic hepatitis B liver failure: Seeing the forest for the trees.

PubMed

Shi, K-Q; Zhou, Y-Y; Yan, H-D; Li, H; Wu, F-L; Xie, Y-Y; Braddock, M; Lin, X-Y; Zheng, M-H

2017-02-01

At present, there is no ideal model for predicting the short-term outcome of patients with acute-on-chronic hepatitis B liver failure (ACHBLF). This study aimed to establish and validate a prognostic model by using the classification and regression tree (CART) analysis. A total of 1047 patients from two separate medical centres with suspected ACHBLF were screened in the study, which were recognized as derivation cohort and validation cohort, respectively. CART analysis was applied to predict the 3-month mortality of patients with ACHBLF. The accuracy of the CART model was tested using the area under the receiver operating characteristic curve, which was compared with the model for end-stage liver disease (MELD) score and a new logistic regression model. CART analysis identified four variables as prognostic factors of ACHBLF: total bilirubin, age, serum sodium and INR, and three distinct risk groups: low risk (4.2%), intermediate risk (30.2%-53.2%) and high risk (81.4%-96.9%). The new logistic regression model was constructed with four independent factors, including age, total bilirubin, serum sodium and prothrombin activity by multivariate logistic regression analysis. The performances of the CART model (0.896), similar to the logistic regression model (0.914, P=.382), exceeded that of MELD score (0.667, P<.001). The results were confirmed in the validation cohort. We have developed and validated a novel CART model superior to MELD for predicting three-month mortality of patients with ACHBLF. Thus, the CART model could facilitate medical decision-making and provide clinicians with a validated practical bedside tool for ACHBLF risk stratification. © 2016 John Wiley & Sons Ltd.

Identification of immune correlates of protection in Shigella infection by application of machine learning.

PubMed

Arevalillo, Jorge M; Sztein, Marcelo B; Kotloff, Karen L; Levine, Myron M; Simon, Jakub K

2017-10-01

Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical vaccine studies. Our goal is the development of a methodology to identify immunologic correlates of protection using the Shigella challenge as a model. The proposed methodology utilizes the Random Forests (RF) machine learning algorithm as well as Classification and Regression Trees (CART) to detect immune markers that predict protection, identify interactions between variables, and define optimal cutoffs. Logistic regression modeling is applied to estimate the probability of protection and the confidence interval (CI) for such a probability is computed by bootstrapping the logistic regression models. The results demonstrate that the combination of Classification and Regression Trees and Random Forests complements the standard logistic regression and uncovers subtle immune interactions. Specific levels of immunoglobulin IgG antibody in blood on the day of challenge predicted protection in 75% (95% CI 67-86). Of those subjects that did not have blood IgG at or above a defined threshold, 100% were protected if they had IgA antibody secreting cells above a defined threshold. Comparison with the results obtained by applying only logistic regression modeling with standard Akaike Information Criterion for model selection shows the usefulness of the proposed method. Given the complexity of the immune system, the use of machine learning methods may enhance traditional statistical approaches. When applied together, they offer a novel way to quantify important immune correlates of protection that may help the development of vaccines. Copyright © 2017 Elsevier Inc. All rights reserved.

The quest for conditional independence in prospectivity modeling: weights-of-evidence, boost weights-of-evidence, and logistic regression

NASA Astrophysics Data System (ADS)

Schaeben, Helmut; Semmler, Georg

2016-09-01

The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes 0,1 classification of T. A special case of logistic regression called weights-of-evidence (WofE) is geologists' favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regression view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence (BoostWofE) was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the (initial) purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to "validate" this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking conditional independence whatever the consecutively processing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly compensate violations of joint conditional independence if the predictors are indicators.

[Job retention and nursing practice environment of hospital nurses in Japan applying the Japanese version of the Practice Environment Scale of the Nursing Work Index (PES-NWI)].

PubMed

Ogata, Yasuko; Nagano, Midori; Fukuda, Takashi; Hashimoto, Michio

2011-06-01

The purpose of this study was to examine how the nursing practice environment affects job retention and the turnover rate among hospital nurses. The Practice Environment Scale of the Nursing Work Index (PES-NWI) was applied to investigate the nurse working environment from the viewpoint of hospital nurses in Japan. Methods A postal mail survey was conducted using the PES-NWI questionnaire targeting 2,211 nurses who were working at 91 wards in 5 hospitals situated in the Tokyo metropolitan area from February to March in 2008. In the questionnaire, hospital nurses were asked about characteristics such as sex, age and work experience as a nurse, whether they would work at the same hospital in the next year, the 31 items of the PES-NWI and job satisfaction. Nurse managers were asked to provide staff numbers to calculate the turnover rate of each ward. Logistic regression analyses were carried out, with "intention to retain or leave the workplace next year" as the dependent variable, with composite and 5 sub-scale scores of the PES-NWI and nurse characteristics as independent variables. Correlation coefficients were calculated to investigate the relationship between nurse turnover rates and nursing practice environments. A total of 1,067 full-time nurses (48.3%) from 5 hospitals responded. Almost all of them were men (95.9%), with an average age of 29.2 years old. They had an average of 7.0 years total work experience in hospitals and 5.8 years of experience at their current hospital. Cronbach's alpha coefficients were 0.75 for composite of the PES-NWI, and 0.77-0.85 for the sub-scales. All correlation coefficients between PES-NWI and job satisfaction were significant (P < 0.01). In the logistic regression analysis, a composite of PES-NWI, "Nurse Manager's Ability, Leadership, and Support of Nurses" and "Staffing and Resource Adequacy" among the 5 sub-scales correlated with the intention of nurses to stay on (P < 0.05). The means for turnover rate were 10.4% for nurses and 17.6% for newly hired nurses. These rates were significantly correlated to the composite and some sub-scales of the PES-NWI. The working environment for nurses is important in retaining nurses working at hospitals. We confirmed the reliability and the validity of the PES-NWI scale based on the magnitude of the Cronbach's alpha coefficient and correlation coefficient between the PES-NWI scale and job satisfaction in this study.

[An evaluation of clinical characteristics and prognosis of brain-stem infarction in diabetics].

PubMed

Lu, Zheng-qi; Li, Hai-yan; Hu, Xue-qiang; Zhang, Bing-jun

2011-01-01

To analyze the relationship between diabetics and the onset, clinical outcomes and prognosis of brainstem infarction, and to evaluate the impact of diabetes on brainstem infarction. Compare 172 cases of acute brainstem infarction in patients with or without diabetes. Analyze the associated risk factors of patients with brain-stem infarction in diabetics by multi-variate logistic regression analysis. Compare the National Institutes of Health Stroke Scale (NIHSS) and Modified Rankin scale (mRS) Score, pathogenetic condition and the outcome of the two groups in different times. The systolic blood pressure (SBP), TG, LDL-C, apolipoprotein B (Apo B), glutamyl transpeptidase (γ-GT), fibrinogen (Fb), fasting blood glucose (FPG) and glycosylated hemoglobin(HbA1c)in diabetic group were higher than those in non-diabetic group, which was statistically significant (P < 0.05). From multi-variate logistic regression analysis, γ-GT, Apo B and FPG were the risk predictors of diabetes with brainstem infarction(OR = 1.017, 4.667 and 3.173, respectively), while HDL-C was protective (OR = 0.288). HbA1c was a risk predictor of severity for acute brainstem infarction (OR = 1.299), while Apo A was beneficial (OR = 0.212). Compared with brain-stem infarction in non-diabetic group, NIHSS score and intensive care therapy of diabetic groups on the admission had no statistically significance, while the NIHSS score on discharge and the outcome at 6 months' of follow-up were statistically significant. Diabetes is closely associated with brainstem infarction. Brainstem infarction with diabetes cause more rapid progression, poorer prognosis, higher rates of mortality as well as disability and higher recurrence rate of cerebral infarction.

Perceived resource support for chronic illnesses among diabetics in north-western China.

PubMed

Zhong, Huiqin; Shao, Ya; Fan, Ling; Zhong, Tangshen; Ren, Lu; Wang, Yan

2016-06-01

A high level of social support can improve long-term diabetes self-management. Support from a single source has been evaluated. This study aims to analyze support from multiple and multilevel sources for diabetic patients by using the Chronic Illness Resources Survey (CIRS). Factors influencing the utilization of the CIRS were also evaluated. A total of 297 patients with diabetes were investigated using the CIRS and Perceived Diabetes Self-management Scale in Shihezi City, China. Descriptive statistics were used to explain demographic variables and scores of the scales. Factors affecting the utilization of chronic illness resources were determined through univariate analysis and then examined by multivariate logistic regression analysis. Of the 297 diabetic patients surveyed, 67% failed to reach the standard (more than 3 points) of utilizing chronic illness resources. Moreover, utilization of chronic illness resources was positively moderately correlated with self-management of diabetes (r = 0.75, P < 0.05). According to the multivariate logistic regression analysis, age (OR, 3.42; 95%CI, 1.19-9.84) and monthly income (OR, 5.27; 95%CI, 1.86-14.90) were significantly positively associated with the CIRS score. Individuals with high school (OR, 2.61; 95%CI, 1.13-6.05) and college (OR, 3.02; 95%CI, 1.13-8.04) degrees obtained higher scores in the survey than those with elementary school education. Results indicated that utilization of resources and support for chronic illness self-management, particularly personal adjustment and organization, were not ideal among diabetics in the communities of north-western China. Improved utilization of chronic illness resources was conducive for proper diabetes self-management. Furthermore, the level of utilization of chronic illness resources increased with age, literacy level, and monthly income.

Association of depression with body mass index classification, metabolic disease, and lifestyle: A web-based survey involving 11,876 Japanese people.

PubMed

Hidese, Shinsuke; Asano, Shinya; Saito, Kenji; Sasayama, Daimei; Kunugi, Hiroshi

2018-07-01

Body mass index (BMI) and lifestyle-related physical illnesses have been implicated in the pathology of depression. We aimed to investigate the association of depression wih BMI classification (i.e., underweight, normal, overweight, and obese), metabolic disease, and lifestyle using a web-based survey in a large cohort. Participants were 1000 individuals who have had depression (mean age: 41.4 ± 12.3 years, 501 men) and 10,876 population-based controls (45.1 ± 13.6 years, 5691 men). The six-item Kessler scale (K6) test was used as a psychological distress scale. Compared to in the controls, obesity and hyperlipidemia were more common and frequency of a snack or night meal consumption was higher, whereas frequencies of breakfast consumption and vigorous and moderate physical activities were lower in the patients. K6 test scores were higher for underweight or obese people compared to normal or overweight people. A logistic regression analysis showed that the K6 test cut-off score was positively associated with being underweight, hyperlipidemia, and the frequency of a snack or night meal consumption, whereas it was negatively associated with the frequency of breakfast consumption in the patients. Logistic regression analyses showed that self-reported depression was positively associated with metabolic diseases and the frequency of a snack or night meal consumption, whereas it was negatively associated with the frequency of breakfast consumption. The observed associations of depression with BMI classification, metabolic disease, and lifestyle suggest that lifestyle and related physical conditions are involved in at least a portion of depressive disorders. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predicting Redox Conditions in Groundwater Using Statistical Techniques: Implications for Nitrate Transport in Groundwater and Streams

NASA Astrophysics Data System (ADS)

Tesoriero, A. J.; Terziotti, S.

2014-12-01

Nitrate trends in streams often do not match expectations based on recent nitrogen source loadings to the land surface. Groundwater discharge with long travel times has been suggested as the likely cause for these observations. The fate of nitrate in groundwater depends to a large extent on the occurrence of denitrification along flow paths. Because denitrification in groundwater is inhibited when dissolved oxygen (DO) concentrations are high, defining the oxic-suboxic interface has been critical in determining pathways for nitrate transport in groundwater and to streams at the local scale. Predicting redox conditions on a regional scale is complicated by the spatial variability of reaction rates. In this study, logistic regression and boosted classification tree analysis were used to predict the probability of oxic water in groundwater in the Chesapeake Bay watershed. The probability of oxic water (DO > 2 mg/L) was predicted by relating DO concentrations in over 3,000 groundwater samples to indicators of residence time and/or electron donor availability. Variables that describe position in the flow system (e.g., depth to top of the open interval), soil drainage and surficial geology were the most important predictors of oxic water. Logistic regression and boosted classification tree analysis correctly predicted the presence or absence of oxic conditions in over 75 % of the samples in both training and validation data sets. Predictions of the percentages of oxic wells in deciles of risk were very accurate (r2>0.9) in both the training and validation data sets. Depth to the bottom of the oxic layer was predicted and is being used to estimate the effect that groundwater denitrification has on stream nitrate concentrations and the time lag between the application of nitrogen at the land surface and its effect on streams.

Influence of warfarin and low-dose aspirin on the outcomes of geriatric patients with traumatic intracranial hemorrhage resulting from ground-level fall.

PubMed

Inamasu, Joji; Nakatsukasa, Masashi; Miyatake, Satoru; Hirose, Yuichi

2012-10-01

Ground-level fall is the most common cause of traumatic intracranial hemorrhage (TICH) in the elderly, and is a major cause of morbidity and mortality in that population. A retrospective study was carried out to evaluate whether the use of warfarin/low-dose aspirin (LDA) is predictive of unfavorable outcomes in geriatric patients who sustain a fall-induced TICH. Charts of 76 geriatric patients (≥ 65 years-of-age) with fall-induced TICH were reviewed. The number of patients taking warfarin and LDA was 12 and 21, respectively, whereas the other 43 took neither medication (non-user group). The frequency of patients with unfavorable outcomes (Glasgow Outcome Scale score of 1-3) at discharge was calculated. Furthermore, variables predictive of unfavorable outcomes were identified by logistic regression analysis. The frequency of patients with unfavorable outcomes was 75% in the warfarin group, 33% in the LDA group and 27% in the non-user group, respectively. The risk of having unfavorable outcomes was significantly higher in the warfarin group compared with the LDA group (P = 0.03) and non-user group (P < 0.01). Logistic regression analysis showed that variables predictive of unfavorable outcomes were: age, initial Glasgow Coma Scale score ≤ 13 and presence of midline shift ≥ 5 mm. The use of warfarin, but not of LDA, might be associated with unfavorable outcomes in elderly with fall-induced TICH. The risk of TICH should be communicated properly to elderly taking warfarin. The information might be important not only to trauma surgeons who take care of injured elderly, but also to geriatric physicians who prescribe warfarin/LDA to them. © 2012 Japan Geriatrics Society.

Factors influencing medication knowledge and beliefs on warfarin adherence among patients with atrial fibrillation in China.

PubMed

Zhao, Shujuan; Zhao, Hongwei; Wang, Xianpei; Gao, Chuanyu; Qin, Yuhua; Cai, Haixia; Chen, Boya; Cao, Jingjing

2017-01-01

Warfarin is often used for ischemic stroke prevention in patients with atrial fibrillation (AF), but the factors affecting patient adherence to warfarin therapy have not been fully understood. A cross-sectional survey was conducted in AF patients undergoing warfarin therapy at least 6 months prior to the study. The clinical data collected using questionnaires by phone interviews included the following: 1) self-reported adherence measured by the Morisky Medication Adherence Scale-8 © ; 2) beliefs about medicines surveyed by Beliefs about Medicines Questionnaire (BMQ); and 3) drug knowledge as measured by the Warfarin Related Knowledge Test (WRKT). Demographic and clinical factors associated with warfarin adherence were identified using a logistic regression model. Two hundred eighty-eight patients completed the survey and 93 (32.3%) of them were classified as nonadherent (Morisky Medication Adherence Scale-8 score <6). Major factors predicting warfarin adherence included age, cardiovascular disorders, WRKT, and BMQ; WRKT and BMQ were independently correlated with adherence to warfarin therapy by multivariate logistic regression analysis. Adherents were more likely to have greater knowledge scores and stronger beliefs in the necessity of their specific medications ([odds ratio {OR} =1.81, 95% confidence interval {CI} =1.51-2.15] and [OR =1.17, 95% CI =1.06-1.29], respectively). Patients with greater concerns about adverse reactions and more negative views of general harm were more likely to be nonadherent ([OR =0.76, 95% CI =0.69-0.84] and [OR =0.82, 95% CI =0.73-0.92], respectively). BMK and WRKT are related with patient behavior toward warfarin adherence. BMQ can be applied to identify patients at increased risk of nonadherence.

Ketamine use for rapid sequence intubation in Australian and New Zealand emergency departments from 2010 to 2015: A registry study.

PubMed

Ferguson, Ian; Alkhouri, Hatem; Fogg, Toby; Aneman, Anders

2018-06-11

This study aimed to quantify the proportion of patients undergoing rapid sequence intubation using ketamine in Australian and New Zealand EDs between 2010 and 2015. The Australian and New Zealand Emergency Department Airway Registry is a multicentre airway registry prospectively capturing data from 43 sites. Data on demographics and physiology, the attending staff and indication for intubation were recorded. The primary outcome was the annual percentage of patients intubated with ketamine. A logistic regression analysis was conducted to evaluate the factors associated with ketamine use. A total of 4658 patients met inclusion criteria. The annual incidence of ketamine use increased from 5% to 28% over the study period (P < 0.0001). In the logistic regression analysis, the presence of an emergency physician as a team leader was the strongest predictor of ketamine use (odds ratio [OR] 1.83, 95% confidence interval [CI] 1.44-2.34). The OR for an increase in one point on the Glasgow Coma Scale was 1.10 (95% CI 1.07-1.12), whereas an increase of 1 mmHg of systolic blood pressure had an OR of 0.98 (95% CI 0.98-0.99). Intubation occurring in a major referral hospital had an OR of 0.68 (95% CI 0.56-0.82), while trauma conferred an OR of 1.38 (95% CI 1.25-1.53). Ketamine use increased between 2010 and 2015. Lower systolic blood pressure, the presence of an emergency medicine team leader, trauma and a higher Glasgow Coma Scale were associated with increased odds of ketamine use. Intubation occurring in a major referral centre was associated with lower odds of ketamine use. © 2018 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine.

Clinical Value of Dorsal Medulla Oblongata Involvement Detected with Conventional MRI for Prediction of Outcome in Children with Enterovirus 71-related Brainstem Encephalitis.

PubMed

Liu, Kun; Zhou, Yongjin; Cui, Shihan; Song, Jiawen; Ye, Peipei; Xiang, Wei; Huang, Xiaoyan; Chen, Yiping; Yan, Zhihan; Ye, Xinjian

2018-04-05

Brainstem encephalitis is the most common neurologic complication after enterovirus 71 infection. The involvement of brainstem, especially the dorsal medulla oblongata, can cause severe sequelae or death in children with enterovirus 71 infection. We aimed to determine the prevalence of dorsal medulla oblongata involvement in children with enterovirus 71-related brainstem encephalitis (EBE) by using conventional MRI and to evaluate the value of dorsal medulla oblongata involvement in outcome prediction. 46 children with EBE were enrolled in the study. All subjects underwent a 1.5 Tesla MR examination of the brain. The disease distribution and clinical data were collected. Dichotomized outcomes (good versus poor) at longer than 6 months were available for 28 patients. Logistic regression was used to determine whether the MRI-confirmed dorsal medulla oblongata involvement resulted in improved clinical outcome prediction when compared with other location involvement. Of the 46 patients, 35 had MRI evidence of dorsal medulla oblongata involvement, 32 had pons involvement, 10 had midbrain involvement, and 7 had dentate nuclei involvement. Patients with dorsal medulla oblongata involvement or multiple area involvement were significantly more often in the poor outcome group than in the good outcome group. Logistic regression analysis showed that dorsal medulla oblongata involvement was the most significant single variable in outcome prediction (predictive accuracy, 90.5%), followed by multiple area involvement, age, and initial glasgow coma scale score. Dorsal medulla oblongata involvement on conventional MRI correlated significantly with poor outcomes in EBE children, improved outcome prediction when compared with other clinical and disease location variables, and was most predictive when combined with multiple area involvement, glasgow coma scale score and age.

Interaction among general practitioners age and patient load in the prediction of job strain, decision latitude and perception of job demands. A cross-sectional study.

PubMed

Vanagas, Giedrius; Bihari-Axelsson, Susanna

2004-12-07

It is widely recognized and accepted that job strain adversely impacts the workforce. Individual responses to stressful situations can vary greatly and it has been shown that certain people are more likely to experience high levels of stress in their job than others. Studies highlighted that there can be age differences in job strain perception. Cross-sectional postal survey of 300 Lithuanian general practitioners. Psychosocial stress was investigated with a questionnaire based on the Reeder scale. Job demands were investigated with the Karasek scale. The analysis included descriptive statistics; logistic regression beta coefficients to find out predictors and interactions between characteristics and predictors. Response rate was 66% (N = 197). Logistic regression as significant predictors for job strain assigned - duration of work in primary care; for job demands- age and duration of working in primary care; for decision latitude- age and patient load.The interactions with regard to job strain showed that GP's age and job strain are negatively associated to a low patient load. Lower decision latitude for older GP age is strongly related to higher patient load. Job demands and GP age are slightly positively related at low patient load. Lithuanian GP's have high patient load and are at risk of stress, they have high job demands and low decision latitude. Older GP's perceive less strain, lower job demands and higher decision latitude in case of low patient load. Young GP's decision latitude has week association to patient load. Regarding to the changes in patient load younger GP's perceive it more sensitively as changes in job demands.

Predictive parameters for return to pre-injury level of sport 6 months following anterior cruciate ligament reconstruction surgery.

PubMed

Müller, Ulrike; Krüger-Franke, Michael; Schmidt, Michael; Rosemeyer, Bernd

2015-12-01

The aim of the study was to find predictive parameters for a successful resumption of pre-injury level of sport 6 months post anterior cruciate ligament (ACL) reconstruction. In a prospective study, 40 patients with a ruptured ACL were surgically treated with semitendinosus tendon autograft. Six months after surgery, strength of knee extensors and flexors, four single-leg hop tests, Anterior Cruciate Ligament-Return to Sport after Injury Scale (ACL-RSI), subjective International Knee Documentation Committee (IKDC) 2000 and the Tampa Scale of Kinesiophobia-11 (TSK-11) were assessed. Seven months post-operatively, a standardized interview was conducted to identify "return to sport" (RS) and "non-return to sport" (nRS) patients. Logistic regression and "Receiver Operating Characteristic" (ROC) analyses were used to determine predictive parameters. No significant differences could be detected between RS and nRS patients concerning socio-demographic data, muscle tests, square hop and TSK-11. In nRS patients, the Limb Symmetry Index (LSI) of single hop for distance (p = 0.005), crossover hop (p = 0.008) and triple hop (p = 0.001) were significantly lower, in addition to the ACL-RSI (p = 0.013) and IKDC 2000 (p = 0.037). The cut-off points for LSI single hop for distance were 75.4 % (sensitivity 0.74; specificity 0.88), and for ACL-RSI 51.3 points (sensitivity 0.97; specificity 0.63). Logistic regression distinguished between RS and nRS subjects (sensitivity 0.97; specificity 0.63). The single hop for distance and ACL-RSI were found to be the strongest predictive parameters, assessing both the objective functional and the subjective psychological aspects of returning to sport. Both tests may help to identify patients at risk of not returning to pre-injury sport. II.

Suicidality in obsessive-compulsive disorder: prevalence and relation to symptom dimensions and comorbid conditions.

PubMed

Torres, Albina R; Ramos-Cerqueira, Ana Teresa A; Ferrão, Ygor A; Fontenelle, Leonardo F; do Rosário, Maria Conceição; Miguel, Euripedes C

2011-01-01

Suicidal thoughts and behaviors, also known as suicidality, are a fairly neglected area of study in patients with obsessive-compulsive disorder (OCD). To evaluate several aspects of suicidality in a large multicenter sample of OCD patients and to compare those with and without suicidal ideation, plans, and attempts according to demographic and clinical variables, including symptom dimensions and comorbid disorders. This cross-sectional study included 582 outpatients with primary OCD (DSM-IV) recruited between August 2003 and March 2008 from 7 centers of the Brazilian Research Consortium on Obsessive-Compulsive Spectrum Disorders. The following assessment instruments were used: the Yale-Brown Obsessive Compulsive Scale, the Dimensional Yale-Brown Obsessive Compulsive Scale, the Beck Depression and Anxiety Inventories, the Structured Clinical Interview for DSM-IV Axis I Disorders, and 6 specific questions to investigate suicidality. After univariate analyses, logistic regression analyses were performed to adjust the associations between the dependent and explanatory variables for possible confounders. Thirty-six percent of the patients reported lifetime suicidal thoughts, 20% had made suicidal plans, 11% had already attempted suicide, and 10% presented current suicidal thoughts. In the logistic regression, only lifetime major depressive disorder and posttraumatic stress disorder (PTSD) remained independently associated with all aspects of suicidal behaviors. The sexual/religious dimension and comorbid substance use disorders remained associated with suicidal thoughts and plans, while impulse-control disorders were associated with current suicidal thoughts and with suicide plans and attempts. The risk of suicidal behaviors must be carefully investigated in OCD patients, particularly those with symptoms of the sexual/religious dimension and comorbid major depressive disorder, PTSD, substance use disorders, and impulse-control disorders. © Copyright 2011 Physicians Postgraduate Press, Inc.

Factors influencing medication knowledge and beliefs on warfarin adherence among patients with atrial fibrillation in China

PubMed Central

Zhao, Shujuan; Zhao, Hongwei; Wang, Xianpei; Gao, Chuanyu; Qin, Yuhua; Cai, Haixia; Chen, Boya; Cao, Jingjing

2017-01-01

Objectives Warfarin is often used for ischemic stroke prevention in patients with atrial fibrillation (AF), but the factors affecting patient adherence to warfarin therapy have not been fully understood. Methods A cross-sectional survey was conducted in AF patients undergoing warfarin therapy at least 6 months prior to the study. The clinical data collected using questionnaires by phone interviews included the following: 1) self-reported adherence measured by the Morisky Medication Adherence Scale-8©; 2) beliefs about medicines surveyed by Beliefs about Medicines Questionnaire (BMQ); and 3) drug knowledge as measured by the Warfarin Related Knowledge Test (WRKT). Demographic and clinical factors associated with warfarin adherence were identified using a logistic regression model. Results Two hundred eighty-eight patients completed the survey and 93 (32.3%) of them were classified as nonadherent (Morisky Medication Adherence Scale-8 score <6). Major factors predicting warfarin adherence included age, cardiovascular disorders, WRKT, and BMQ; WRKT and BMQ were independently correlated with adherence to warfarin therapy by multivariate logistic regression analysis. Adherents were more likely to have greater knowledge scores and stronger beliefs in the necessity of their specific medications ([odds ratio {OR} =1.81, 95% confidence interval {CI} =1.51–2.15] and [OR =1.17, 95% CI =1.06–1.29], respectively). Patients with greater concerns about adverse reactions and more negative views of general harm were more likely to be nonadherent ([OR =0.76, 95% CI =0.69–0.84] and [OR =0.82, 95% CI =0.73–0.92], respectively). Conclusion BMK and WRKT are related with patient behavior toward warfarin adherence. BMQ can be applied to identify patients at increased risk of nonadherence. PMID:28223782

Vitamin D status and 3-month Glasgow Outcome Scale scores in patients in neurocritical care: prospective analysis of 497 patients.

PubMed

Guan, Jian; Karsy, Michael; Brock, Andrea A; Eli, Ilyas M; Manton, Gabrielle M; Ledyard, Holly K; Hawryluk, Gregory W J; Park, Min S

2018-06-01

OBJECTIVE Vitamin D deficiency has been associated with a variety of negative outcomes in critically ill patients, but little focused study on the effects of hypovitaminosis D has been performed in the neurocritical care population. In this study, the authors examined the effect of vitamin D deficiency on 3-month outcomes after discharge from a neurocritical care unit (NCCU). METHODS The authors prospectively analyzed 25-hydroxy vitamin D levels in patients admitted to the NCCU of a quaternary care center over a 6-month period. Glasgow Outcome Scale (GOS) scores were used to evaluate their 3-month outcome, and univariate and multivariate logistic regression was used to evaluate the effects of vitamin D deficiency. RESULTS Four hundred ninety-seven patients met the inclusion criteria. In the binomial logistic regression model, patients without vitamin D deficiency (> 20 ng/dl) were significantly more likely to have a 3-month GOS score of 4 or 5 than those who were vitamin D deficient (OR 1.768 [95% CI 1.095-2.852]). Patients with a higher Simplified Acute Physiology Score (SAPS II) (OR 0.925 [95% CI 0.910-0.940]) and those admitted for stroke (OR 0.409 [95% CI 0.209-0.803]) or those with an "other" diagnosis (OR 0.409 [95% CI 0.217-0.772]) were significantly more likely to have a 3-month GOS score of 3 or less. CONCLUSIONS Vitamin D deficiency is associated with worse 3-month postdischarge GOS scores in patients admitted to an NCCU. Additional study is needed to determine the role of vitamin D supplementation in the NCCU population.

Black Hole Sign Predicts Poor Outcome in Patients with Intracerebral Hemorrhage.

PubMed

Li, Qi; Yang, Wen-Song; Chen, Sheng-Li; Lv, Fu-Rong; Lv, Fa-Jin; Hu, Xi; Zhu, Dan; Cao, Du; Wang, Xing-Chen; Li, Rui; Yuan, Liang; Qin, Xin-Yue; Xie, Peng

2018-01-01

In spontaneous intracerebral hemorrhage (ICH), black hole sign has been proposed as a promising imaging marker that predicts hematoma expansion in patients with ICH. The aim of our study was to investigate whether admission CT black hole sign predicts hematoma growth in patients with ICH. From July 2011 till February 2016, patients with spontaneous ICH who underwent baseline CT scan within 6 h of symptoms onset and follow-up CT scan were recruited into the study. The presence of black hole sign on admission non-enhanced CT was independently assessed by 2 readers. The functional outcome was assessed using the modified Rankin Scale (mRS) at 90 days. Univariate and multivariable logistic regression analyses were performed to assess the association between the presence of the black hole sign and functional outcome. A total of 225 patients (67.6% male, mean age 60.3 years) were included in our study. Black hole sign was identified in 32 of 225 (14.2%) patients on admission CT scan. The multivariate logistic regression analysis demonstrated that age, intraventricular hemorrhage, baseline ICH volume, admission Glasgow Coma Scale score, and presence of black hole sign on baseline CT independently predict poor functional outcome at 90 days. There are significantly more patients with a poor functional outcome (defined as mRS ≥4) among patients with black hole sign than those without (84.4 vs. 32.1%, p < 0.001; OR 8.19, p = 0.001). The CT black hole sign independently predicts poor outcome in patients with ICH. Early identification of black hole sign is useful in prognostic stratification and may serve as a potential therapeutic target for anti-expansion clinical trials. © 2018 S. Karger AG, Basel.

Predicting children's behaviour during dental treatment under oral sedation.

PubMed

Lourenço-Matharu, L; Papineni McIntosh, A; Lo, J W

2016-06-01

The primary aim of this study was to assess whether parents' own anxiety and their perception of their child's dental fear and child's general fear can predict preoperatively their child's behaviour during dental treatment under oral sedation. The secondary aim was to assess whether the child's age, gender and ASA classification grade are associated with a child's behaviour under oral sedation. Cross-sectional prospective study. The Corah's Dental Anxiety Scale (DAS), Children's Fear Survey Schedule Dental-Subscale (CFSS-DS) and Children's Fear Survey Schedule Short-Form (CFSS-SF) questionnaires were completed by parents of children undergoing dental treatment with oral midazolam. Behaviour was rated by a single clinician using the overall behaviour section of the Houpt-Scale and scores dichotomised into acceptable or unacceptable behaviour. Data were analysed using χ (2), t test and logistic regression analysis. In total 404 children (215 girls, 53 %) were included, with the mean age of 4.57 years, SD = 1.9. Behaviour was scored as acceptable in 336 (83 %) and unacceptable in 68 (17 %) children. The level of a child's dental fear, as perceived by their parent, was significantly associated with the behaviour outcome (p = 0.001). Logistic regression analysis revealed that if the parentally perceived child's dental fear (CFSS-DS) rating was high, the odds of the child exhibiting unacceptable behaviour under oral sedation was two times greater than if their parents scored them a low dental fear rating (OR 2.27, 95 % CI 1.33-3.88, p = 0.003). CFSS-DS may be used preoperatively to help predict behaviour outcome when children are treated under oral sedation and facilitate treatment planning.

Implementation of Neurocritical Care Is Associated With Improved Outcomes in Traumatic Brain Injury.

PubMed

Sekhon, Mypinder S; Gooderham, Peter; Toyota, Brian; Kherzi, Navid; Hu, Vivien; Dhingra, Vinay K; Hameed, Morad S; Chittock, Dean R; Griesdale, Donald E

2017-07-01

Background Traditionally, the delivery of dedicated neurocritical care (NCC) occurs in distinct NCC units and is associated with improved outcomes. Institution-specific logistical challenges pose barriers to the development of distinct NCC units; therefore, we developed a consultancy NCC service coupled with the implementation of invasive multimodal neuromonitoring, within a medical-surgical intensive care unit. Our objective was to evaluate the effect of a consultancy NCC program on neurologic outcomes in severe traumatic brain injury patients. We conducted a single-center quasi-experimental uncontrolled pre- and post-NCC study in severe traumatic brain injury patients (Glasgow Coma Scale ≤8). The NCC program includes consultation with a neurointensivist and neurosurgeon and multimodal neuromonitoring. Demographic, injury severity metrics, neurophysiologic data, and therapeutic interventions were collected. Glasgow Outcome Scale (GOS) at 6 months was the primary outcome. Multivariable ordinal logistic regression was used to model the association between NCC implementation and GOS at 6 months. A total of 113 patients were identified: 76 pre-NCC and 37 post-NCC. Mean age was 39 years (standard deviation [SD], 2) and 87 of 113 (77%) patients were male. Median admission motor score was 3 (interquartile ratio, 1-4). Daily mean arterial pressure was higher (95 mmHg [SD, 10]) versus (88 mmHg [SD, 10], p<0.001) and daily mean core body temperature was lower (36.6°C [SD, 0.90]) versus (37.2°C [SD, 1.0], p=0.001) post-NCC compared with pre-NCC, respectively. Multivariable regression modelling revealed the NCC program was associated with a 2.5 increased odds (odds ratios, 2.5; 95% confidence interval, 1.1-5.3; p=0.022) of improved 6-month GOS. Implementation of a NCC program is associated with improved 6 month GOS in severe TBI patients.

Evaluating construct validity of the second version of the Copenhagen Psychosocial Questionnaire through analysis of differential item functioning and differential item effect.

PubMed

Bjorner, Jakob Bue; Pejtersen, Jan Hyld

2010-02-01

To evaluate the construct validity of the Copenhagen Psychosocial Questionnaire II (COPSOQ II) by means of tests for differential item functioning (DIF) and differential item effect (DIE). We used a Danish general population postal survey (n = 4,732 with 3,517 wage earners) with a one-year register based follow up for long-term sickness absence. DIF was evaluated against age, gender, education, social class, public/private sector employment, and job type using ordinal logistic regression. DIE was evaluated against job satisfaction and self-rated health (using ordinal logistic regression), against depressive symptoms, burnout, and stress (using multiple linear regression), and against long-term sick leave (using a proportional hazards model). We used a cross-validation approach to counter the risk of significant results due to multiple testing. Out of 1,052 tests, we found 599 significant instances of DIF/DIE, 69 of which showed both practical and statistical significance across two independent samples. Most DIF occurred for job type (in 20 cases), while we found little DIF for age, gender, education, social class and sector. DIE seemed to pertain to particular items, which showed DIE in the same direction for several outcome variables. The results allowed a preliminary identification of items that have a positive impact on construct validity and items that have negative impact on construct validity. These results can be used to develop better shortform measures and to improve the conceptual framework, items and scales of the COPSOQ II. We conclude that tests of DIF and DIE are useful for evaluating construct validity.

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

PubMed

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

2012-03-01

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

Separation in Logistic Regression: Causes, Consequences, and Control.

PubMed

Mansournia, Mohammad Ali; Geroldinger, Angelika; Greenland, Sander; Heinze, Georg

2018-04-01

Separation is encountered in regression models with a discrete outcome (such as logistic regression) where the covariates perfectly predict the outcome. It is most frequent under the same conditions that lead to small-sample and sparse-data bias, such as presence of a rare outcome, rare exposures, highly correlated covariates, or covariates with strong effects. In theory, separation will produce infinite estimates for some coefficients. In practice, however, separation may be unnoticed or mishandled because of software limits in recognizing and handling the problem and in notifying the user. We discuss causes of separation in logistic regression and describe how common software packages deal with it. We then describe methods that remove separation, focusing on the same penalized-likelihood techniques used to address more general sparse-data problems. These methods improve accuracy, avoid software problems, and allow interpretation as Bayesian analyses with weakly informative priors. We discuss likelihood penalties, including some that can be implemented easily with any software package, and their relative advantages and disadvantages. We provide an illustration of ideas and methods using data from a case-control study of contraceptive practices and urinary tract infection.

Modeling the dynamics of urban growth using multinomial logistic regression: a case study of Jiayu County, Hubei Province, China

NASA Astrophysics Data System (ADS)

Nong, Yu; Du, Qingyun; Wang, Kun; Miao, Lei; Zhang, Weiwei

2008-10-01

Urban growth modeling, one of the most important aspects of land use and land cover change study, has attracted substantial attention because it helps to comprehend the mechanisms of land use change thus helps relevant policies made. This study applied multinomial logistic regression to model urban growth in the Jiayu county of Hubei province, China to discover the relationship between urban growth and the driving forces of which biophysical and social-economic factors are selected as independent variables. This type of regression is similar to binary logistic regression, but it is more general because the dependent variable is not restricted to two categories, as those previous studies did. The multinomial one can simulate the process of multiple land use competition between urban land, bare land, cultivated land and orchard land. Taking the land use type of Urban as reference category, parameters could be estimated with odds ratio. A probability map is generated from the model to predict where urban growth will occur as a result of the computation.

Predictors of hopelessness among clinically depressed youth.

PubMed

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

2009-05-01

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

A New Lebanese Medication Adherence Scale: Validation in Lebanese Hypertensive Adults

PubMed Central

Wakim, N.; Issa, C.; Kassem, B.; Abou Jaoude, L.; Saleh, N.

2018-01-01

Background A new Lebanese scale measuring medication adherence considered socioeconomic and cultural factors not taken into account by the eight-item Morisky Medication Adherence Scale (MMAS-8). Objectives were to validate the new adherence scale and its prediction of hypertension control, compared to MMAS-8, and to assess adherence rates and factors. Methodology A cross-sectional study, including 405 patients, was performed in outpatient cardiology clinics of three hospitals in Beirut. Blood pressure was measured, a questionnaire filled, and sodium intake estimated by a urine test. Logistic regression defined predictors of hypertension control and adherence. Results 54.9% had controlled hypertension. 82.4% were adherent by the new scale, which showed good internal consistency, adequate questions (KMO coefficient = 0.743), and four factors. It predicted hypertension control (OR = 1.217; p value = 0.003), unlike MMAS-8, but the scores were correlated (ICC average measure = 0.651; p value < 0.001). Stress and smoking predicted nonadherence. Conclusion This study elaborated a validated, practical, and useful tool measuring adherence to medications in Lebanese hypertensive patients. PMID:29887993

[Attention deficit-hyperactivity disorder (ADHD) and comorbid mental disorders : ADHD-specific self-rating scales in differential diagnostics].

PubMed

Paucke, M; Stark, T; Exner, C; Kallweit, C; Hegerl, U; Strauß, M

2018-06-18

It is still unclear how well the established attention deficit-hyperactive disorder (ADHD)-specific rating scales can differentiate between ADHD symptoms and symptoms of other mental disorders. A total of 274 patients with suspected adult ADHD were extensively examined clinically and guideline-conform in an ADHD outpatient clinic. In 190 patients the diagnosis of ADHD could be made with certainty. The patients were also subsequently assessed according to the DSM IV criteria by self-rating scales on current (ADHS-SB, ASRS, CAARS) and retrospective (WURS-K) complaints. A binary logistic regression analysis was performed in order to extract from the questionnaires, which could best distinguish the diagnosis of ADHD from other mental disorders. The results showed that two self-rating scales (WURS-K and ADHS-SB) were sufficient to correctly diagnose ADHD in 83% of the patients examined with a sensitivity of 94% and specificity of 56%. The ADHD-specific self-rating scales are additionally useful for the diagnostic differentiation between ADHD-specific and other psychiatric symptoms in the clinical practice and can improve the safety of the diagnosis.

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking.

PubMed

Lages, Martin; Scheel, Anne

2016-01-01

We investigated the proposition of a two-systems Theory of Mind in adults' belief tracking. A sample of N = 45 participants predicted the choice of one of two opponent players after observing several rounds in an animated card game. Three matches of this card game were played and initial gaze direction on target and subsequent choice predictions were recorded for each belief task and participant. We conducted logistic regressions with mixed effects on the binary data and developed Bayesian logistic mixed models to infer implicit and explicit mentalizing in true belief and false belief tasks. Although logistic regressions with mixed effects predicted the data well a Bayesian logistic mixed model with latent task- and subject-specific parameters gave a better account of the data. As expected explicit choice predictions suggested a clear understanding of true and false beliefs (TB/FB). Surprisingly, however, model parameters for initial gaze direction also indicated belief tracking. We discuss why task-specific parameters for initial gaze directions are different from choice predictions yet reflect second-order perspective taking.

Model selection for logistic regression models

NASA Astrophysics Data System (ADS)

Duller, Christine

2012-09-01

Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.

Radiomorphometric analysis of frontal sinus for sex determination.

PubMed

Verma, Saumya; Mahima, V G; Patil, Karthikeya

2014-09-01

Sex determination of unknown individuals carries crucial significance in forensic research, in cases where fragments of skull persist with no likelihood of identification based on dental arch. In these instances sex determination becomes important to rule out certain number of possibilities instantly and helps in establishing a biological profile of human remains. The aim of the study is to evaluate a mathematical method based on logistic regression analysis capable of ascertaining the sex of individuals in the South Indian population. The study was conducted in the department of Oral Medicine and Radiology. The right and left areas, maximum height, width of frontal sinus were determined in 100 Caldwell views of 50 women and 50 men aged 20 years and above, with the help of Vernier callipers and a square grid with 1 square measuring 1mm(2) in area. Student's t-test, logistic regression analysis. The mean values of variables were greater in men, based on Student's t-test at 5% level of significance. The mathematical model based on logistic regression analysis gave percentage agreement of total area to correctly predict the female gender as 55.2%, of right area as 60.9% and of left area as 55.2%. The areas of the frontal sinus and the logistic regression proved to be unreliable in sex determination. (Logit = 0.924 - 0.00217 × right area).

Genetic prediction of type 2 diabetes using deep neural network.

PubMed

Kim, J; Kim, J; Kwak, M J; Bajaj, M

2018-04-01

Type 2 diabetes (T2DM) has strong heritability but genetic models to explain heritability have been challenging. We tested deep neural network (DNN) to predict T2DM using the nested case-control study of Nurses' Health Study (3326 females, 45.6% T2DM) and Health Professionals Follow-up Study (2502 males, 46.5% T2DM). We selected 96, 214, 399, and 678 single-nucleotide polymorphism (SNPs) through Fisher's exact test and L1-penalized logistic regression. We split each dataset randomly in 4:1 to train prediction models and test their performance. DNN and logistic regressions showed better area under the curve (AUC) of ROC curves than the clinical model when 399 or more SNPs included. DNN was superior than logistic regressions in AUC with 399 or more SNPs in male and 678 SNPs in female. Addition of clinical factors consistently increased AUC of DNN but failed to improve logistic regressions with 214 or more SNPs. In conclusion, we show that DNN can be a versatile tool to predict T2DM incorporating large numbers of SNPs and clinical information. Limitations include a relatively small number of the subjects mostly of European ethnicity. Further studies are warranted to confirm and improve performance of genetic prediction models using DNN in different ethnic groups. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Unconditional or Conditional Logistic Regression Model for Age-Matched Case-Control Data?

PubMed

Kuo, Chia-Ling; Duan, Yinghui; Grady, James

2018-01-01

Matching on demographic variables is commonly used in case-control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case-control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case-control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls.

Unconditional or Conditional Logistic Regression Model for Age-Matched Case–Control Data?

PubMed Central

Kuo, Chia-Ling; Duan, Yinghui; Grady, James

2018-01-01

Matching on demographic variables is commonly used in case–control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case–control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case–control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls. PMID:29552553

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

PubMed

Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G

2007-08-01

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.

Analysis of a database to predict the result of allergy testing in vivo in patients with chronic nasal symptoms.

PubMed

Lacagnina, Valerio; Leto-Barone, Maria S; La Piana, Simona; Seidita, Aurelio; Pingitore, Giuseppe; Di Lorenzo, Gabriele

2014-01-01

This article uses the logistic regression model for diagnostic decision making in patients with chronic nasal symptoms. We studied the ability of the logistic regression model, obtained by the evaluation of a database, to detect patients with positive allergy skin-prick test (SPT) and patients with negative SPT. The model developed was validated using the data set obtained from another medical institution. The analysis was performed using a database obtained from a questionnaire administered to the patients with nasal symptoms containing personal data, clinical data, and results of allergy testing (SPT). All variables found to be significantly different between patients with positive and negative SPT (p < 0.05) were selected for the logistic regression models and were analyzed with backward stepwise logistic regression, evaluated with area under the curve of the receiver operating characteristic curve. A second set of patients from another institution was used to prove the model. The accuracy of the model in identifying, over the second set, both patients whose SPT will be positive and negative was high. The model detected 96% of patients with nasal symptoms and positive SPT and classified 94% of those with negative SPT. This study is preliminary to the creation of a software that could help the primary care doctors in a diagnostic decision making process (need of allergy testing) in patients complaining of chronic nasal symptoms.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

Psychological factors influence the gastroesophageal reflux disease (GERD) and their effect on quality of life among firefighters in South Korea.

PubMed

Jang, Seung-Ho; Ryu, Han-Seung; Choi, Suck-Chei; Lee, Sang-Yeol

2016-10-01

The purpose of this study was to examine psychosocial factors related to gastroesophageal reflux disease (GERD) and their effects on quality of life (QOL) in firefighters. Data were collected from 1217 firefighters in a Korean province. We measured psychological symptoms using the scale. In order to observe the influence of the high-risk group on occupational stress, we conduct logistic multiple linear regression. The correlation between psychological factors and QOL was also analyzed and performed a hierarchical regression analysis. GERD was observed in 32.2% of subjects. Subjects with GERD showed higher depressive symptom, anxiety and occupational stress scores, and lower self-esteem and QOL scores relative to those observed in GERD - negative subject. GERD risk was higher for the following occupational stress subcategories: job demand, lack of reward, interpersonal conflict, and occupational climate. The stepwise regression analysis showed that depressive symptoms, occupational stress, self-esteem, and anxiety were the best predictors of QOL. The results suggest that psychological and medical approaches should be combined in GERD assessment.

Psychological factors influence the gastroesophageal reflux disease (GERD) and their effect on quality of life among firefighters in South Korea

PubMed Central

Jang, Seung-Ho; Ryu, Han-Seung; Choi, Suck-Chei; Lee, Sang-Yeol

2016-01-01

Objectives The purpose of this study was to examine psychosocial factors related to gastroesophageal reflux disease (GERD) and their effects on quality of life (QOL) in firefighters. Methods Data were collected from 1217 firefighters in a Korean province. We measured psychological symptoms using the scale. In order to observe the influence of the high-risk group on occupational stress, we conduct logistic multiple linear regression. The correlation between psychological factors and QOL was also analyzed and performed a hierarchical regression analysis. Results GERD was observed in 32.2% of subjects. Subjects with GERD showed higher depressive symptom, anxiety and occupational stress scores, and lower self-esteem and QOL scores relative to those observed in GERD – negative subject. GERD risk was higher for the following occupational stress subcategories: job demand, lack of reward, interpersonal conflict, and occupational climate. The stepwise regression analysis showed that depressive symptoms, occupational stress, self-esteem, and anxiety were the best predictors of QOL. Conclusions The results suggest that psychological and medical approaches should be combined in GERD assessment. PMID:27691373

Combining logistic regression with classification and regression tree to predict quality of care in a home health nursing data set.

PubMed

Guo, Huey-Ming; Shyu, Yea-Ing Lotus; Chang, Her-Kun

2006-01-01

In this article, the authors provide an overview of a research method to predict quality of care in home health nursing data set. The results of this study can be visualized through classification an regression tree (CART) graphs. The analysis was more effective, and the results were more informative since the home health nursing dataset was analyzed with a combination of the logistic regression and CART, these two techniques complete each other. And the results more informative that more patients' characters were related to quality of care in home care. The results contributed to home health nurse predict patient outcome in case management. Improved prediction is needed for interventions to be appropriately targeted for improved patient outcome and quality of care.

Attention Deficit Hyperactivity Disorder, Aggression, and Illicit Stimulant Use: Is This Self-Medication?

PubMed

Odell, Annie P; Reynolds, Grace L; Fisher, Dennis G; Huckabay, Loucine M; Pedersen, William C; Xandre, Pamela; Miočević, Milica

2017-05-01

This study compares adults with and without attention deficit hyperactivity disorder (ADHD) on measures of direct and displaced aggression and illicit drug use. Three hundred ninety-six adults were administered the Wender Utah Rating Scale, the Risk Behavior Assessment, the Aggression Questionnaire (AQ), and the Displaced Aggression Questionnaire (DAQ). Those with ADHD were higher on all scales of the AQ and DAQ, were younger at first use of amphetamines, and were more likely to have ever used crack and amphetamines. A Structural Equation Model found a significant interaction in that for those with medium and high levels of verbal aggression, ADHD predicts crack and amphetamine. Follow-up logistic regression models suggest that blacks self-medicate with crack and whites and Hispanics self-medicate with amphetamine when they have ADHD and verbal aggression.

A general framework for the use of logistic regression models in meta-analysis.

PubMed

Simmonds, Mark C; Higgins, Julian Pt

2016-12-01

Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. © The Author(s) 2014.

Asthma exacerbation and proximity of residence to major roads: a population-based matched case-control study among the pediatric Medicaid population in Detroit, Michigan

PubMed Central

2011-01-01

Background The relationship between asthma and traffic-related pollutants has received considerable attention. The use of individual-level exposure measures, such as residence location or proximity to emission sources, may avoid ecological biases. Method This study focused on the pediatric Medicaid population in Detroit, MI, a high-risk population for asthma-related events. A population-based matched case-control analysis was used to investigate associations between acute asthma outcomes and proximity of residence to major roads, including freeways. Asthma cases were identified as all children who made at least one asthma claim, including inpatient and emergency department visits, during the three-year study period, 2004-06. Individually matched controls were randomly selected from the rest of the Medicaid population on the basis of non-respiratory related illness. We used conditional logistic regression with distance as both categorical and continuous variables, and examined non-linear relationships with distance using polynomial splines. The conditional logistic regression models were then extended by considering multiple asthma states (based on the frequency of acute asthma outcomes) using polychotomous conditional logistic regression. Results Asthma events were associated with proximity to primary roads with an odds ratio of 0.97 (95% CI: 0.94, 0.99) for a 1 km increase in distance using conditional logistic regression, implying that asthma events are less likely as the distance between the residence and a primary road increases. Similar relationships and effect sizes were found using polychotomous conditional logistic regression. Another plausible exposure metric, a reduced form response surface model that represents atmospheric dispersion of pollutants from roads, was not associated under that exposure model. Conclusions There is moderately strong evidence of elevated risk of asthma close to major roads based on the results obtained in this population-based matched case-control study. PMID:21513554

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.

Correlational analysis of neck/shoulder pain and low back pain with the use of digital products, physical activity and psychological status among adolescents in Shanghai.

PubMed

Shan, Zhi; Deng, Guoying; Li, Jipeng; Li, Yangyang; Zhang, Yongxing; Zhao, Qinghua

2013-01-01

This study investigates the neck/shoulder pain (NSP) and low back pain (LBP) among current high school students in Shanghai and explores the relationship between these pains and their possible influences, including digital products, physical activity, and psychological status. An anonymous self-assessment was administered to 3,600 students across 30 high schools in Shanghai. This questionnaire examined the prevalence of NSP and LBP and the level of physical activity as well as the use of mobile phones, personal computers (PC) and tablet computers (Tablet). The CES-D (Center for Epidemiological Studies Depression) scale was also included in the survey. The survey data were analyzed using the chi-square test, univariate logistic analyses and a multivariate logistic regression model. Three thousand sixteen valid questionnaires were received including 1,460 (48.41%) from male respondents and 1,556 (51.59%) from female respondents. The high school students in this study showed NSP and LBP rates of 40.8% and 33.1%, respectively, and the prevalence of both influenced by the student's grade, use of digital products, and mental status; these factors affected the rates of NSP and LBP to varying degrees. The multivariate logistic regression analysis revealed that Gender, grade, soreness after exercise, PC using habits, tablet use, sitting time after school and academic stress entered the final model of NSP, while the final model of LBP consisted of gender, grade, soreness after exercise, PC using habits, mobile phone use, sitting time after school, academic stress and CES-D score. High school students in Shanghai showed high prevalence of NSP and LBP that were closely related to multiple factors. Appropriate interventions should be implemented to reduce the occurrences of NSP and LBP.

[Relationship between social-psycological factors and quality of life in old women with coronary heart disease].

PubMed

Wu, Lin-Na; Yang, Guo-Yun; Ge, Ning

2013-03-01

To investigate the influence of depression, social supports and quality of sleep and quality of life on old women who were 60 years or older and postmenopause with coronary heart disease. 125 old women with coronary heart disease completed questionnaires of Seattle Angina Questionnaire (SAQ), Social Support Scale (SSRS) and Self-rating Depression Scale (SDS). Logistic regression analysis and Spearman correlation analysis were performed to evaluate the relationship between social-psycological factors and quality of life. 120 of questionnaires wereeffective (representing 96% of all collected questionnaires). Regression analysis showed that marital status (OR = 2.450), education (OR = 0.520), income (OR = 19.541) and course of disease (OR = 0.309) were associated with QOL in CHD (P < 0.05). Spearman analysis demonstrated that there were negative correlations between SQA score and PSQI and depression scores (r = -0.771, P < 0.01; r = -0.703, P < 0.05); and positive correlation between SQA score and Social support score (r = 0.565, P < 0.05). Social-psychological factors might influence the quality of life in old women with coronary heart disease, it is important that physicians pay attention to these factors when they treat old women with coronary heart disease.

An association between the internalization of body image, depressive symptoms and restrictive eating habits among young males.

PubMed

Fortes, Leonardo de Sousa; Meireles, Juliana Fernandes Filgueiras; Paes, Santiago Tavares; Dias, Fernanda Coelho; Cipriani, Flávia Marcele; Ferreira, Maria Elisa Caputo

2015-11-01

The scope of this study was to analyze the relationship between the internalization of body image and depressive symptoms with restrictive eating habits among young males. Three hundred and eighty-three male adolescents, aged between twelve and seventeen, took part in this survey. The "Overall Internalization" and "Athletic Internalization" sub-scales taken from the Sociocultural Attitudes Towards Appearance Questionnaire-3 (SATAQ-3) were used to evaluate the internalization of body images. The Major Depression Inventory (MDI) was used to evaluate depressive symptoms. The "Diet" sub-scale from the Eating Attitudes Test (EAT-26) was used to evaluate restrictive eating habits. The logistic regression findings indicated 2.01 times greater chances of youngsters with a high level of overall internalization adopting restrictive eating habits (Wald = 6.16; p = 0.01) when compared with those with low levels. On the other hand, the regression model found no significant association between "Athletic Internalization" (Wald = 1.16; p = 0.23) and depressive symptoms (Wald = 0.81; p = 0.35) with eating restrictions. The findings made it possible to conclude that only overall internalization was related to eating restrictions among young males.

Cultural consensus modeling to measure transactional sex in Swaziland: Scale building and validation.

PubMed

Fielding-Miller, Rebecca; Dunkle, Kristin L; Cooper, Hannah L F; Windle, Michael; Hadley, Craig

2016-01-01

Transactional sex is associated with increased risk of HIV and gender based violence in southern Africa and around the world. However the typical quantitative operationalization, "the exchange of gifts or money for sex," can be at odds with a wide array of relationship types and motivations described in qualitative explorations. To build on the strengths of both qualitative and quantitative research streams, we used cultural consensus models to identify distinct models of transactional sex in Swaziland. The process allowed us to build and validate emic scales of transactional sex, while identifying key informants for qualitative interviews within each model to contextualize women's experiences and risk perceptions. We used logistic and multinomial logistic regression models to measure associations with condom use and social status outcomes. Fieldwork was conducted between November 2013 and December 2014 in the Hhohho and Manzini regions. We identified three distinct models of transactional sex in Swaziland based on 124 Swazi women's emic valuation of what they hoped to receive in exchange for sex with their partners. In a clinic-based survey (n = 406), consensus model scales were more sensitive to condom use than the etic definition. Model consonance had distinct effects on social status for the three different models. Transactional sex is better measured as an emic spectrum of expectations within a relationship, rather than an etic binary relationship type. Cultural consensus models allowed us to blend qualitative and quantitative approaches to create an emicly valid quantitative scale grounded in qualitative context. Copyright © 2015 Elsevier Ltd. All rights reserved.

Statistical considerations in the development of injury risk functions.

PubMed

McMurry, Timothy L; Poplin, Gerald S

2015-01-01

We address 4 frequently misunderstood and important statistical ideas in the construction of injury risk functions. These include the similarities of survival analysis and logistic regression, the correct scale on which to construct pointwise confidence intervals for injury risk, the ability to discern which form of injury risk function is optimal, and the handling of repeated tests on the same subject. The statistical models are explored through simulation and examination of the underlying mathematics. We provide recommendations for the statistically valid construction and correct interpretation of single-predictor injury risk functions. This article aims to provide useful and understandable statistical guidance to improve the practice in constructing injury risk functions.

Prospective in-patient cohort study of moves between levels of therapeutic security: the DUNDRUM-1 triage security, DUNDRUM-3 programme completion and DUNDRUM-4 recovery scales and the HCR-20.

PubMed

Davoren, Mary; O'Dwyer, Sarah; Abidin, Zareena; Naughton, Leena; Gibbons, Olivia; Doyle, Elaine; McDonnell, Kim; Monks, Stephen; Kennedy, Harry G

2012-07-13

We examined whether new structured professional judgment instruments for assessing need for therapeutic security, treatment completion and recovery in forensic settings were related to moves from higher to lower levels of therapeutic security and added anything to assessment of risk. This was a prospective naturalistic twelve month observational study of a cohort of patients in a forensic hospital placed according to their need for therapeutic security along a pathway of moves from high to progressively less secure units in preparation for discharge. Patients were assessed using the DUNDRUM-1 triage security scale, the DUNDRUM-3 programme completion scale and the DUNDRUM-4 recovery scale and assessments of risk of violence, self harm and suicide, symptom severity and global function. Patients were subsequently observed for positive moves to less secure units and negative moves to more secure units. There were 86 male patients at baseline with mean follow-up 0.9 years, 11 positive and 9 negative moves. For positive moves, logistic regression indicated that along with location at baseline, the DUNDRUM-1, HCR-20 dynamic and PANSS general symptom scores were associated with subsequent positive moves. The receiver operating characteristic was significant for the DUNDRUM-1 while ANOVA co-varying for both location at baseline and HCR-20 dynamic score was significant for DUNDRUM-1. For negative moves, logistic regression showed DUNDRUM-1 and HCR-20 dynamic scores were associated with subsequent negative moves, along with DUNDRUM-3 and PANSS negative symptoms in some models. The receiver operating characteristic was significant for the DUNDRUM-4 recovery and HCR-20 dynamic scores with DUNDRUM-1, DUNDRUM-3, PANSS general and GAF marginal. ANOVA co-varying for both location at baseline and HCR-20 dynamic scores showed only DUNDRUM-1 and PANSS negative symptoms associated with subsequent negative moves. Clinicians appear to decide moves based on combinations of current and imminent (dynamic) risk measured by HCR-20 dynamic score and historical seriousness of risk as measured by need for therapeutic security (DUNDRUM-1) in keeping with Scott's formulation of risk and seriousness. The DUNDRUM-3 programme completion and DUNDRUM-4 recovery scales have utility as dynamic measures that can off-set perceived 'dangerousness'.

Prospective in-patient cohort study of moves between levels of therapeutic security: the DUNDRUM-1 triage security, DUNDRUM-3 programme completion and DUNDRUM-4 recovery scales and the HCR-20

PubMed Central

2012-01-01

Background We examined whether new structured professional judgment instruments for assessing need for therapeutic security, treatment completion and recovery in forensic settings were related to moves from higher to lower levels of therapeutic security and added anything to assessment of risk. Methods This was a prospective naturalistic twelve month observational study of a cohort of patients in a forensic hospital placed according to their need for therapeutic security along a pathway of moves from high to progressively less secure units in preparation for discharge. Patients were assessed using the DUNDRUM-1 triage security scale, the DUNDRUM-3 programme completion scale and the DUNDRUM-4 recovery scale and assessments of risk of violence, self harm and suicide, symptom severity and global function. Patients were subsequently observed for positive moves to less secure units and negative moves to more secure units. Results There were 86 male patients at baseline with mean follow-up 0.9 years, 11 positive and 9 negative moves. For positive moves, logistic regression indicated that along with location at baseline, the DUNDRUM-1, HCR-20 dynamic and PANSS general symptom scores were associated with subsequent positive moves. The receiver operating characteristic was significant for the DUNDRUM-1 while ANOVA co-varying for both location at baseline and HCR-20 dynamic score was significant for DUNDRUM-1. For negative moves, logistic regression showed DUNDRUM-1 and HCR-20 dynamic scores were associated with subsequent negative moves, along with DUNDRUM-3 and PANSS negative symptoms in some models. The receiver operating characteristic was significant for the DUNDRUM-4 recovery and HCR-20 dynamic scores with DUNDRUM-1, DUNDRUM-3, PANSS general and GAF marginal. ANOVA co-varying for both location at baseline and HCR-20 dynamic scores showed only DUNDRUM-1 and PANSS negative symptoms associated with subsequent negative moves. Conclusions Clinicians appear to decide moves based on combinations of current and imminent (dynamic) risk measured by HCR-20 dynamic score and historical seriousness of risk as measured by need for therapeutic security (DUNDRUM-1) in keeping with Scott's formulation of risk and seriousness. The DUNDRUM-3 programme completion and DUNDRUM-4 recovery scales have utility as dynamic measures that can off-set perceived 'dangerousness'. PMID:22794187

Have infant gross motor abilities changed in 20 years? A re-evaluation of the Alberta Infant Motor Scale normative values.

PubMed

Darrah, Johanna; Bartlett, Doreen; Maguire, Thomas O; Avison, William R; Lacaze-Masmonteil, Thierry

2014-09-01

To compare the original normative data of the Alberta Infant Motor Scale (AIMS) (n=2202) collected 20 years ago with a contemporary sample of Canadian infants. This was a cross-sectional cohort study of 650 Canadian infants (338 males, 312 females; mean age 30.9 wks [SD 15.5], range 2 wks-18 mo) assessed once on the AIMS. Assessments were stratified by age, and infants proportionally represented the ethnic diversity of Canada. Logistic regression was used to place AIMS items on an age scale representing the age at which 50% of the infants passed an item on the contemporary data set and the original data set. Forty-three items met the criterion for stable regression results in both data sets. The correlation coefficient between the age locations of items on the original and contemporary data sets was 0.99. The mean age difference between item locations was 0.7 weeks. Age values from the original data set when converted to the contemporary scale differed by less than 1 week. The sequence and age at emergence of AIMS items has remained similar over 20 years and current normative values remain valid. Concern that the 'back to sleep' campaign has influenced the age at emergence of gross motor abilities is not supported. © 2014 The Authors. Developmental Medicine & Child Neurology published by John Wiley & Sons Ltd on behalf of Mac Keith Press.

Maternal impulse control disability and developmental disorder traits are risk factors for child maltreatment.

PubMed

Tachibana, Yoshiyuki; Takehara, Kenji; Kakee, Naoko; Mikami, Masashi; Inoue, Eisuke; Mori, Rintaro; Ota, Erika; Koizumi, Tomoe; Okuyama, Makiko; Kubo, Takahiko

2017-11-14

Previous work has suggested that maternal developmental disorder traits related to autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are significantly associated with child maltreatment. However, there may be other important maternal characteristics that contribute to child maltreatment. We hypothesized that maternal impulse control disability may also affect child maltreatment in addition to maternal developmental disorder traits. We aimed to test this hypothesis via a cohort study performed in Tokyo (n = 1,260). Linear regression analyses using the Behavioural Inhibition/Behavioural Activation Scales, the self-administered short version of the Pervasive Developmental Disorders Autism Society Japan Rating Scale, the short form of the Adult Attention-Deficit Hyperactivity Disorder Self-Report Scale, and the Child Maltreatment Scale, revealed that excessive inhibition of behaviour and affect, which is impulse control disability, is significantly associated with child maltreatment (b = 0.031, p = 0.018) in addition to maternal developmental disorder traits (ASD: b = 0.052, p = 0.004; ADHD: b = 0.178, p < 0.001). Logistic regression analyses revealed that ASD (adjusted odds ratio [AOR] = 1.083, p = 0.014) and high behavioural inhibition (AOR = 1.068, p = 0.016) were significantly associated with moderate child maltreatment, while ADHD was associated (AOR = 1.034, p = 0.022) with severe child maltreatment. These maternal characteristics may inform the best means for prevention and management of child maltreatment cases.

2012 Workplace and Gender Relations Survey of Reserve Component Members: Statistical Methodology Report

DTIC Science & Technology

2012-09-01

3,435 10,461 9.1 3.1 63 Unmarried with Children+ Unmarried without Children 439,495 0.01 10,350 43,870 10.1 2.2 64 Married with Children+ Married ...logistic regression model was used to predict the probability of eligibility for the survey (known eligibility vs . unknown eligibility). A second logistic...regression model was used to predict the probability of response among eligible sample members (complete response vs . non-response). CHAID (Chi

Impact of fall-related behaviors as risk factors for falls among the elderly patients with dementia in a geriatric facility in Japan.

PubMed

Suzuki, Mizue; Kurata, Sadami; Yamamoto, Emiko; Makino, Kumiko; Kanamori, Masao

2012-09-01

The purpose of this study was to clarify potential fall-related behaviors as fall risk factors that may predict the potential for falls among the elderly patients with dementia at a geriatric facility in Japan. This study was conducted from April 2008 to May 2009. A baseline study was conducted in April 2008 to evaluate Mini-Mental State Examination, Physical Self-Maintenance Scale, fall-related behaviors, and other factors. For statistical analysis, paired t test and logistic analysis were used to compare each item between fallers and nonfallers. A total of 135 participants were followed up for 1 year; 50 participants (37.04%) fell during that period. Results of multiple logistic regression analysis showed that the total score for fall-related behaviors was significantly related to falls. It was suggested that 11 fall-related behaviors may be effective indicators to predict falls among the elderly patients with dementia.

Habitat features and predictive habitat modeling for the Colorado chipmunk in southern New Mexico

USGS Publications Warehouse

Rivieccio, M.; Thompson, B.C.; Gould, W.R.; Boykin, K.G.

2003-01-01

Two subspecies of Colorado chipmunk (state threatened and federal species of concern) occur in southern New Mexico: Tamias quadrivittatus australis in the Organ Mountains and T. q. oscuraensis in the Oscura Mountains. We developed a GIS model of potentially suitable habitat based on vegetation and elevation features, evaluated site classifications of the GIS model, and determined vegetation and terrain features associated with chipmunk occurrence. We compared GIS model classifications with actual vegetation and elevation features measured at 37 sites. At 60 sites we measured 18 habitat variables regarding slope, aspect, tree species, shrub species, and ground cover. We used logistic regression to analyze habitat variables associated with chipmunk presence/absence. All (100%) 37 sample sites (28 predicted suitable, 9 predicted unsuitable) were classified correctly by the GIS model regarding elevation and vegetation. For 28 sites predicted suitable by the GIS model, 18 sites (64%) appeared visually suitable based on habitat variables selected from logistic regression analyses, of which 10 sites (36%) were specifically predicted as suitable habitat via logistic regression. We detected chipmunks at 70% of sites deemed suitable via the logistic regression models. Shrub cover, tree density, plant proximity, presence of logs, and presence of rock outcrop were retained in the logistic model for the Oscura Mountains; litter, shrub cover, and grass cover were retained in the logistic model for the Organ Mountains. Evaluation of predictive models illustrates the need for multi-stage analyses to best judge performance. Microhabitat analyses indicate prospective needs for different management strategies between the subspecies. Sensitivities of each population of the Colorado chipmunk to natural and prescribed fire suggest that partial burnings of areas inhabited by Colorado chipmunks in southern New Mexico may be beneficial. These partial burnings may later help avoid a fire that could substantially reduce habitat of chipmunks over a mountain range.

The logistic model for predicting the non-gonoactive Aedes aegypti females.

PubMed

Reyes-Villanueva, Filiberto; Rodríguez-Pérez, Mario A

2004-01-01

To estimate, using logistic regression, the likelihood of occurrence of a non-gonoactive Aedes aegypti female, previously fed human blood, with relation to body size and collection method. This study was conducted in Monterrey, Mexico, between 1994 and 1996. Ten samplings of 60 mosquitoes of Ae. aegypti females were carried out in three dengue endemic areas: six of biting females, two of emerging mosquitoes, and two of indoor resting females. Gravid females, as well as those with blood in the gut were removed. Mosquitoes were taken to the laboratory and engorged on human blood. After 48 hours, ovaries were dissected to register whether they were gonoactive or non-gonoactive. Wing-length in mm was an indicator for body size. The logistic regression model was used to assess the likelihood of non-gonoactivity, as a binary variable, in relation to wing-length and collection method. Of the 600 females, 164 (27%) remained non-gonoactive, with a wing-length range of 1.9-3.2 mm, almost equal to that of all females (1.8-3.3 mm). The logistic regression model showed a significant likelihood of a female remaining non-gonoactive (Y=1). The collection method did not influence the binary response, but there was an inverse relationship between non-gonoactivity and wing-length. Dengue vector populations from Monterrey, Mexico display a wide-range body size. Logistic regression was a useful tool to estimate the likelihood for an engorged female to remain non-gonoactive. The necessity for a second blood meal is present in any female, but small mosquitoes are more likely to bite again within a 2-day interval, in order to attain egg maturation. The English version of this paper is available too at: http://www.insp.mx/salud/index.html.

The Application of the Cumulative Logistic Regression Model to Automated Essay Scoring

ERIC Educational Resources Information Center

Haberman, Shelby J.; Sinharay, Sandip

2010-01-01

Most automated essay scoring programs use a linear regression model to predict an essay score from several essay features. This article applied a cumulative logit model instead of the linear regression model to automated essay scoring. Comparison of the performances of the linear regression model and the cumulative logit model was performed on a…

[Risk factors for surgical site infections in patients undergoing craniotomy].

PubMed

Cha, Kyeong-Sook; Cho, Ok-Hee; Yoo, So-Yeon

2010-04-01

The objectives of this study were to determine the prevalence, incidence, and risk factors for postoperative surgical site infections (SSIs) after craniotomy. This study was a retrospective case-control study of 103 patients who had craniotomies between March 2007 and December 2008. A retrospective review of prospectively collected databases of consecutive patients who underwent craniotomy was done. SSIs were defined by using the Centers for Disease Control criteria. Twenty-six cases (infection) and 77 controls (no infection) were matched for age, gender and time of surgery. Descriptive analysis, t-test, X(2)-test and logistic regression analyses were used for data analysis. The statistical difference between cases and controls was significant for hospital length of stay (>14 days), intensive care unit stay more than 15 days, Glasgrow Coma Scale (GCS) score (< or = 7 days), extra-ventricular drainage and coexistent infection. Risk factors were identified by logistic regression and included hospital length of stay of more than 14 days (odds ratio [OR]=23.39, 95% confidence interval [CI]=2.53-216.11) and GCS score (< or = 7 scores) (OR=4.71, 95% CI=1.64-13.50). The results of this study show that patients are at high risk for infection when they have a low level of consciousness or their length hospital stay is long term. Nurses have to take an active and continuous approach to infection control to help with patients having these risk factors.

Risk factors for hospital readmission of elderly patients.

PubMed

Franchi, Carlotta; Nobili, Alessandro; Mari, Daniela; Tettamanti, Mauro; Djade, Codjo D; Pasina, Luca; Salerno, Francesco; Corrao, Salvatore; Marengoni, Alessandra; Iorio, Alfonso; Marcucci, Maura; Mannucci, Pier Mannuccio

2013-01-01

The aim of this study was to identify which factors were associated with a risk of hospital readmission within 3 months after discharge of a sample of elderly patients admitted to internal medicine and geriatric wards. Of the 1178 patients aged 65 years or more and discharged from one of the 66 wards of the 'Registry Politerapie SIMI (REPOSI)' during 2010, 766 were followed up by phone interview 3 months after discharge and were included in this analysis. Univariate and multivariate logistic regression models were used to evaluate the association of several variables with rehospitalization within 3 months from discharge. Nineteen percent of patients were readmitted at least once within 3 months after discharge. By univariate analysis in-hospital clinical adverse events (AEs), a previous hospital admission, number of diagnoses and drugs, comorbidity and severity index (according to Cumulative Illness Rating Scale-CIRS), vascular and liver diseases with a level of impairment at discharge of 3 or more at CIRS were significantly associated with risk of readmission. Multivariate logistic regression analysis showed that only AEs during hospitalization, previous hospital admission, and vascular and liver diseases were significantly associated with the likelihood of readmission. The results demonstrate the need for increased medical attention towards elderly patients discharged from hospital with characteristics such as AEs during the hospitalization, previous admission, vascular and liver diseases. Copyright © 2012 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Subclinical Hypothyroidism after 131I-Treatment of Graves' Disease: A Risk Factor for Depression?

PubMed

Yu, Jing; Tian, Ai-Juan; Yuan, Xin; Cheng, Xiao-Xin

2016-01-01

Although it is well accepted that there is a close relationship between hypothyroidism and depression, previous studies provided inconsistent or even opposite results in whether subclinical hypothyroidism (SCH) increased the risk of depression. One possible reason is that the etiology of SCH in these studies was not clearly distinguished. We therefore investigated the relationship between SCH resulting from 131I treatment of Graves' disease and depression. The incidence of depression among 95 patients with SCH and 121 euthyroid patients following 131I treatment of Graves' disease was studied. The risk factors of depression were determined with multivariate logistic regression analysis. Thyroid hormone replacement therapy was performed in patients with thyroid-stimulating hormone (TSH) levels exceeding 10 mIU/L. Patients with SCH had significantly higher Hamilton Depression Scale scores, serum TSH and thyroid peroxidase antibody (TPOAb) levels compared with euthyroid patients. Multivariate logistic regression analysis revealed SCH, Graves' eye syndrome and high serum TPO antibody level as risk factors for depression. L-thyroxine treatment is beneficial for SCH patients with serum TSH levels exceeding 10 mIU/L. The results of the present study demonstrated that SCH is prevalent among 131I treated Graves' patients. SCH might increase the risk of developing depression. L-thyroxine replacement therapy helps to resolve depressive disorders in SCH patients with TSH > 10mIU/L. These data provide insight into the relationship between SCH and depression.

Orofacial pain and quality of life in early adolescents in India.

PubMed

Kumar, Sandeep; Badiyani, Bhumika K; Kumar, Amit; Dixit, Garima; Sharma, Prachi; Agrawal, Sugandha

2016-08-18

Orofacial pain may have an impact on quality of life. It may affect the overall well-being of an individual. To assess the prevalence of orofacial pain and its impact on quality of life in early adolescents in Indore city, India. This was a cross-sectional study which included a total of 800 children selected from various public and private schools located in Indore city, India. A questionnaire was developed which collected information on sociodemographic characteristics and previous dental visits. The severity of pain was assessed using Von Korff pain scale and quality of life using the General Health Questionnaire 12 (GHQ-12). The chi-square test and logistic regression analysis were performed. The overall prevalence of orofacial pain was found to be 17.9%. Toothache (10.1%) was found to be the most prevalent orofacial pain followed by temporomandibular joint pain (4.3%). The highest severity of pain (Grades 3 and 4) was reported for toothache followed by temporomandibular joint pain. The results of the logistic regression model showed that the prevalence of orofacial pain (odds ratio=7.18, p-value<0.0001a) was strongly associated with poor quality of life. The orofacial pain has a negative influence on the quality of life of adolescents. Effective policies should be created to improve the quality of life of adolescents focusing on oral health education and prevention of oral diseases.

[Relationship between personality characteristics and turnover intention of medical staff in an infectious disease hospital].

PubMed

Ma, K H; Cui, Z Y; Li, L; Chao, H; Wang, Y

2017-12-20

Objective: To investigate the relationship between personality characteristics and turnover intention of the medical staff in an infectious diseases hospital. Methods: Using the cluster sampling method, a total of 366 members of medical staff were selected from different departments in an infectious disease hospital from May to August, 2013. The general information, such as sex, age, education level, and professional title, were collected and they were subjected to a survey using Cattell's 16 Personality Factor Questionnaire and Turnover Intention Scale. The data were subjected to logistic regression analysis. Results: Compared with the Chinese norm, the medical staff in the infectious disease hospital had significantly higher scores of intelligence, stability, bullying, excitability, perseverance, social boldness, fantasy, privateness, independence, and self-discipline and significantly lower scores of gregariousness, sensitivity, suspicion, anxiety, and tension ( P <0.05). Of the 366 members of medical staff, 22 (6.01%) had a very low turnover intention, low in 152 (41.53%) , high in 61 (16.67%) , and very high in 131 (35.79%). The logistic regression analysis showed that sensitivity, suspicion, fantasy, privateness, anxiety, openness to change, and independence were the risk factors for turnover intention ( P <0.05) . Conclusion: Compared with the Chinese norm, the medical staff in the infectious disease hospital have a better mental quality and a higher turnover intention. The individuals with sensitivity, suspicion, fantasy, and anxiety are prone to having turnover intention.

Predicting Visual Distraction Using Driving Performance Data

PubMed Central

Kircher, Katja; Ahlstrom, Christer

2010-01-01

Behavioral variables are often used as performance indicators (PIs) of visual or internal distraction induced by secondary tasks. The objective of this study is to investigate whether visual distraction can be predicted by driving performance PIs in a naturalistic setting. Visual distraction is here defined by a gaze based real-time distraction detection algorithm called AttenD. Seven drivers used an instrumented vehicle for one month each in a small scale field operational test. For each of the visual distraction events detected by AttenD, seven PIs such as steering wheel reversal rate and throttle hold were calculated. Corresponding data were also calculated for time periods during which the drivers were classified as attentive. For each PI, means between distracted and attentive states were calculated using t-tests for different time-window sizes (2 – 40 s), and the window width with the smallest resulting p-value was selected as optimal. Based on the optimized PIs, logistic regression was used to predict whether the drivers were attentive or distracted. The logistic regression resulted in predictions which were 76 % correct (sensitivity = 77 % and specificity = 76 %). The conclusion is that there is a relationship between behavioral variables and visual distraction, but the relationship is not strong enough to accurately predict visual driver distraction. Instead, behavioral PIs are probably best suited as complementary to eye tracking based algorithms in order to make them more accurate and robust. PMID:21050615

Knowledge, awareness, and behaviors of endocrinologists and dentists for the relationship between diabetes and periodontitis.

PubMed

Lin, Hanxiao; Zhang, Hua; Yan, Yuxia; Liu, Duan; Zhang, Ruyi; Liu, Yeungyeung; Chen, Pei; Zhang, Jincai; Xuan, Dongying

2014-12-01

This study aimed to compare the opinions of dentists and endocrinologists regarding diabetes mellitus (DM) and periodontitis, and to investigate the possible effects on their practice. Cross-sectional data were collected from 297 endocrinologists and 134 dentists practicing in southern China using two separated questionnaires. Questions were close-ended or Likert-scaled. Statistical analyses were done by descriptive statistics, bivariate and binary logistic regression analysis. Compared with endocrinologists, dentists presented more favorable attitudes for the relationship of DM and periodontitis (P<0.001). 61.2% of dentists reported they would frequently refer patients with severe periodontitis for DM evaluation, while only 26.6% of endocrinologists reported they would frequently advise patients with DM to visit a dentist. Nearly all of the respondents (94.4%) agreed that the interdisciplinary collaboration should be strengthened. The logistic regression analysis exhibited that respondents with more favorable attitudes were more likely to advise a dental visit (P=0.003) or to screen for DM (P=0.006). Endocrinologists and dentists are not equally equipped with the knowledge about the relationship between DM and periodontitis, and there is a wide gap between their practice and the current evidence, especially for endocrinologists. It's urgent to take measures to develop the interdisciplinary education and collaboration among the health care providers. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Unhealthy lifestyle factors and depressive symptoms: A Japanese general adult population survey.

PubMed

Furihata, Ryuji; Konno, Chisato; Suzuki, Masahiro; Takahashi, Sakae; Kaneita, Yoshitaka; Ohida, Takashi; Uchiyama, Makoto

2018-07-01

To investigate the relationship between unhealthy lifestyles factors and depressive symptoms among the general adult population in Japan. Participants were randomly selected from the Japanese general adult population. Data from 2334 people aged 20 years or older were analyzed. This cross-sectional survey was conducted in August and September 2009. Participants completed a face-to-face interview about unhealthy lifestyle factors, including lack of exercise, skipping breakfast, a poorly balanced diet, snacking between meals, insufficient sleep, current smoking, alcohol drinking, and obesity. Presence of depressive symptoms was defined as a score of ≥ 16 on the Japanese version of the Center for Epidemiologic Studies Depression Scale (CES-D). Relationships between unhealthy lifestyle factors and depressive symptoms were evaluated by multivariate logistic regression analysis adjusting for sociodemographic variables and other unhealthy lifestyle factors. Multivariate logistic regression analysis revealed that insufficient sleep, a poorly balanced diet, snacking between meals and lack of exercise were significantly associated with the prevalence of depressive symptoms, with odds ratios ranging from 1.56 for lack of exercise to 3.98 for insufficient sleep. Since this study was a cross-sectional study, causal relationships could not be determined. These results suggest that promoting a healthy lifestyle focused on sleep, food intake and exercise may be important for individuals with depressive symptoms. Copyright © 2018 Elsevier B.V. All rights reserved.

Widen NomoGram for multinomial logistic regression: an application to staging liver fibrosis in chronic hepatitis C patients.

PubMed

Ardoino, Ilaria; Lanzoni, Monica; Marano, Giuseppe; Boracchi, Patrizia; Sagrini, Elisabetta; Gianstefani, Alice; Piscaglia, Fabio; Biganzoli, Elia M

2017-04-01

The interpretation of regression models results can often benefit from the generation of nomograms, 'user friendly' graphical devices especially useful for assisting the decision-making processes. However, in the case of multinomial regression models, whenever categorical responses with more than two classes are involved, nomograms cannot be drawn in the conventional way. Such a difficulty in managing and interpreting the outcome could often result in a limitation of the use of multinomial regression in decision-making support. In the present paper, we illustrate the derivation of a non-conventional nomogram for multinomial regression models, intended to overcome this issue. Although it may appear less straightforward at first sight, the proposed methodology allows an easy interpretation of the results of multinomial regression models and makes them more accessible for clinicians and general practitioners too. Development of prediction model based on multinomial logistic regression and of the pertinent graphical tool is illustrated by means of an example involving the prediction of the extent of liver fibrosis in hepatitis C patients by routinely available markers.

Regularization Paths for Conditional Logistic Regression: The clogitL1 Package.

PubMed

Reid, Stephen; Tibshirani, Rob

2014-07-01

We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso [Formula: see text] and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by.

Computational tools for exact conditional logistic regression.

PubMed

Corcoran, C; Mehta, C; Patel, N; Senchaudhuri, P

Logistic regression analyses are often challenged by the inability of unconditional likelihood-based approximations to yield consistent, valid estimates and p-values for model parameters. This can be due to sparseness or separability in the data. Conditional logistic regression, though useful in such situations, can also be computationally unfeasible when the sample size or number of explanatory covariates is large. We review recent developments that allow efficient approximate conditional inference, including Monte Carlo sampling and saddlepoint approximations. We demonstrate through real examples that these methods enable the analysis of significantly larger and more complex data sets. We find in this investigation that for these moderately large data sets Monte Carlo seems a better alternative, as it provides unbiased estimates of the exact results and can be executed in less CPU time than can the single saddlepoint approximation. Moreover, the double saddlepoint approximation, while computationally the easiest to obtain, offers little practical advantage. It produces unreliable results and cannot be computed when a maximum likelihood solution does not exist. Copyright 2001 John Wiley & Sons, Ltd.

Regularization Paths for Conditional Logistic Regression: The clogitL1 Package

PubMed Central

Reid, Stephen; Tibshirani, Rob

2014-01-01

We apply the cyclic coordinate descent algorithm of Friedman, Hastie, and Tibshirani (2010) to the fitting of a conditional logistic regression model with lasso (ℓ1) and elastic net penalties. The sequential strong rules of Tibshirani, Bien, Hastie, Friedman, Taylor, Simon, and Tibshirani (2012) are also used in the algorithm and it is shown that these offer a considerable speed up over the standard coordinate descent algorithm with warm starts. Once implemented, the algorithm is used in simulation studies to compare the variable selection and prediction performance of the conditional logistic regression model against that of its unconditional (standard) counterpart. We find that the conditional model performs admirably on datasets drawn from a suitable conditional distribution, outperforming its unconditional counterpart at variable selection. The conditional model is also fit to a small real world dataset, demonstrating how we obtain regularization paths for the parameters of the model and how we apply cross validation for this method where natural unconditional prediction rules are hard to come by. PMID:26257587

Ordinal logistic regression analysis on the nutritional status of children in KarangKitri village

NASA Astrophysics Data System (ADS)

Ohyver, Margaretha; Yongharto, Kimmy Octavian

2015-09-01

Ordinal logistic regression is a statistical technique that can be used to describe the relationship between ordinal response variable with one or more independent variables. This method has been used in various fields including in the health field. In this research, ordinal logistic regression is used to describe the relationship between nutritional status of children with age, gender, height, and family status. Nutritional status of children in this research is divided into over nutrition, well nutrition, less nutrition, and malnutrition. The purpose for this research is to describe the characteristics of children in the KarangKitri Village and to determine the factors that influence the nutritional status of children in the KarangKitri village. There are three things that obtained from this research. First, there are still children who are not categorized as well nutritional status. Second, there are children who come from sufficient economic level which include in not normal status. Third, the factors that affect the nutritional level of children are age, family status, and height.