Axial Myopia Is Associated with Visual Field Prognosis of Primary Open-Angle Glaucoma

PubMed Central

Qiu, Chen; Qian, Shaohong; Sun, Xinghuai; Zhou, Chuandi; Meng, Fanrong

2015-01-01

Purpose To identify whether myopia was associated with the visual field (VF) progression of primary open-angle glaucoma (POAG). Methods A total of 270 eyes of 270 POAG followed up for more than 3 years with ≥9 reliable VFs by Octopus perimetry were retrospectively reviewed. Myopia was divided into: mild myopia (-2.99 diopter [D], 0), moderate myopia (-5.99, 3.00 D), marked myopia (-9.00, -6.00 D) and non-myopia (0 D or more). An annual change in the mean defect (MD) slope >0.22 dB/y and 0.30 dB/y was defined as fast progression, respectively. Logistic regression was performed to determine prognostic factors for VF progression. Results For the cutoff threshold at 0.22 dB/y, logistic regression showed that vertical cup-to-disk ratio (VCDR; p = 0.004) and the extent of myopia (p = 0.002) were statistically significant. When logistic regression was repeated after excluding the extent of myopia, axial length (AL; p = 0.008, odds ratio [OR] = 0.796) reached significance, as did VCDR (p = 0.001). Compared to eyes with AL≤23 mm, the OR values were 0.334 (p = 0.059), 0.309 (p = 0.044), 0.266 (p = 0.019), 0.260 (p = 0.018), respectively, for 23 26 mm. The significance of vertical cup-to-disk ratio of (p = 0.004) and the extent of myopia (p = 0.008) did not change for the cutoff threshold at 0.30dB/y. Conclusions VCDR and myopia were associated with VF prognosis of POAG. Axial myopia may be a protective factor against VF progression. PMID:26214313

An association between dietary habits and traffic accidents in patients with chronic liver disease: A data-mining analysis

PubMed Central

KAWAGUCHI, TAKUMI; SUETSUGU, TAKURO; OGATA, SHYOU; IMANAGA, MINAMI; ISHII, KUMIKO; ESAKI, NAO; SUGIMOTO, MASAKO; OTSUYAMA, JYURI; NAGAMATSU, AYU; TANIGUCHI, EITARO; ITOU, MINORU; ORIISHI, TETSUHARU; IWASAKI, SHOKO; MIURA, HIROKO; TORIMURA, TAKUJI

2016-01-01

The incidence of traffic accidents in patients with chronic liver disease (CLD) is high in the USA. However, the characteristics of patients, including dietary habits, differ between Japan and the USA. The present study investigated the incidence of traffic accidents in CLD patients and the clinical profiles associated with traffic accidents in Japan using a data-mining analysis. A cross-sectional study was performed and 256 subjects [148 CLD patients (CLD group) and 106 patients with other digestive diseases (disease control group)] were enrolled; 2 patients were excluded. The incidence of traffic accidents was compared between the two groups. Independent factors for traffic accidents were analyzed using logistic regression and decision-tree analyses. The incidence of traffic accidents did not differ between the CLD and disease control groups (8.8 vs. 11.3%). The results of the logistic regression analysis showed that yoghurt consumption was the only independent risk factor for traffic accidents (odds ratio, 0.37; 95% confidence interval, 0.16–0.85; P=0.0197). Similarly, the results of the decision-tree analysis showed that yoghurt consumption was the initial divergence variable. In patients who consumed yoghurt habitually, the incidence of traffic accidents was 6.6%, while that in patients who did not consume yoghurt was 16.0%. CLD was not identified as an independent factor in the logistic regression and decision-tree analyses. In conclusion, the difference in the incidence of traffic accidents in Japan between the CLD and disease control groups was insignificant. Furthermore, yoghurt consumption was an independent negative risk factor for traffic accidents in patients with digestive diseases, including CLD. PMID:27123257

An association between dietary habits and traffic accidents in patients with chronic liver disease: A data-mining analysis.

PubMed

Kawaguchi, Takumi; Suetsugu, Takuro; Ogata, Shyou; Imanaga, Minami; Ishii, Kumiko; Esaki, Nao; Sugimoto, Masako; Otsuyama, Jyuri; Nagamatsu, Ayu; Taniguchi, Eitaro; Itou, Minoru; Oriishi, Tetsuharu; Iwasaki, Shoko; Miura, Hiroko; Torimura, Takuji

2016-05-01

The incidence of traffic accidents in patients with chronic liver disease (CLD) is high in the USA. However, the characteristics of patients, including dietary habits, differ between Japan and the USA. The present study investigated the incidence of traffic accidents in CLD patients and the clinical profiles associated with traffic accidents in Japan using a data-mining analysis. A cross-sectional study was performed and 256 subjects [148 CLD patients (CLD group) and 106 patients with other digestive diseases (disease control group)] were enrolled; 2 patients were excluded. The incidence of traffic accidents was compared between the two groups. Independent factors for traffic accidents were analyzed using logistic regression and decision-tree analyses. The incidence of traffic accidents did not differ between the CLD and disease control groups (8.8 vs. 11.3%). The results of the logistic regression analysis showed that yoghurt consumption was the only independent risk factor for traffic accidents (odds ratio, 0.37; 95% confidence interval, 0.16-0.85; P=0.0197). Similarly, the results of the decision-tree analysis showed that yoghurt consumption was the initial divergence variable. In patients who consumed yoghurt habitually, the incidence of traffic accidents was 6.6%, while that in patients who did not consume yoghurt was 16.0%. CLD was not identified as an independent factor in the logistic regression and decision-tree analyses. In conclusion, the difference in the incidence of traffic accidents in Japan between the CLD and disease control groups was insignificant. Furthermore, yoghurt consumption was an independent negative risk factor for traffic accidents in patients with digestive diseases, including CLD.

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

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

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

A 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.

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…

Are low wages risk factors for hypertension?

PubMed Central

Du, Juan

2012-01-01

Objective: Socio-economic status (SES) is strongly correlated with hypertension. But SES has several components, including income and correlations in cross-sectional data need not imply SES is a risk factor. This study investigates whether wages—the largest category within income—are risk factors. Methods: We analysed longitudinal, nationally representative US data from four waves (1999, 2001, 2003 and 2005) of the Panel Study of Income Dynamics. The overall sample was restricted to employed persons age 25–65 years, n = 17 295. Separate subsamples were constructed of persons within two age groups (25–44 and 45–65 years) and genders. Hypertension incidence was self-reported based on physician diagnosis. Our study was prospective since data from three base years (1999, 2001, 2003) were used to predict newly diagnosed hypertension for three subsequent years (2001, 2003, 2005). In separate analyses, data from the first base year were used to predict time-to-reporting hypertension. Logistic regressions with random effects and Cox proportional hazards regressions were run. Results: Negative and strongly statistically significant correlations between wages and hypertension were found both in logistic and Cox regressions, especially for subsamples containing the younger age group (25–44 years) and women. Correlations were stronger when three health variables—obesity, subjective measures of health and number of co-morbidities—were excluded from regressions. Doubling the wage was associated with 25–30% lower chances of hypertension for persons aged 25–44 years. Conclusions: The strongest evidence for low wages being risk factors for hypertension among working people were for women and persons aged 25–44 years. PMID:22262559

Are low wages risk factors for hypertension?

PubMed

Leigh, J Paul; Du, Juan

2012-12-01

Socio-economic status (SES) is strongly correlated with hypertension. But SES has several components, including income and correlations in cross-sectional data need not imply SES is a risk factor. This study investigates whether wages-the largest category within income-are risk factors. We analysed longitudinal, nationally representative US data from four waves (1999, 2001, 2003 and 2005) of the Panel Study of Income Dynamics. The overall sample was restricted to employed persons age 25-65 years, n = 17 295. Separate subsamples were constructed of persons within two age groups (25-44 and 45-65 years) and genders. Hypertension incidence was self-reported based on physician diagnosis. Our study was prospective since data from three base years (1999, 2001, 2003) were used to predict newly diagnosed hypertension for three subsequent years (2001, 2003, 2005). In separate analyses, data from the first base year were used to predict time-to-reporting hypertension. Logistic regressions with random effects and Cox proportional hazards regressions were run. Negative and strongly statistically significant correlations between wages and hypertension were found both in logistic and Cox regressions, especially for subsamples containing the younger age group (25-44 years) and women. Correlations were stronger when three health variables-obesity, subjective measures of health and number of co-morbidities-were excluded from regressions. Doubling the wage was associated with 25-30% lower chances of hypertension for persons aged 25-44 years. The strongest evidence for low wages being risk factors for hypertension among working people were for women and persons aged 25-44 years.

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 model for national outcome audit in vascular surgery.

PubMed

Prytherch, D R; Ridler, B M; Beard, J D; Earnshaw, J J

2001-06-01

The aim was to model vascular surgical outcome in a national study using POSSUM scoring. One hundred and twenty-one British and Irish surgeons completed data questionnaires on patients undergoing arterial surgery under their care (mean 12 patients, range 1-49) in May/June 1998. A total of 1480 completed data records were available for logistic regression analysis using P-POSSUM methodology. Information collected included all POSSUM data items plus other factors thought to have a significant bearing on patient outcome: "extra items". The main outcome measures were death and major postoperative complications. The data were checked and inconsistent records were excluded. The remaining 1313 were divided into two sets for analysis. The first "training" set was used to obtain logistic regression models that were applied prospectively to the second "test" dataset. using POSSUM data items alone, it was possible to predict both mortality and morbidity after vascular reconstruction using P-POSSUM analysis. The addition of the "extra items" found significant in regression analysis did not significantly improve the accuracy of prediction. It was possible to predict both mortality and morbidity derived from the preoperative physiology components of the POSSUM data items alone. this study has shown that P-POSSUM methodology can be used to predict outcome after arterial surgery across a range of surgeons in different hospitals and could form the basis of a national outcome audit. It was also possible to obtain accurate models for both mortality and major morbidity from the POSSUM physiology scores alone. Copyright 2001 Harcourt Publishers Limited.

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.

Clinical cure and survival in Gram-positive ventilator-associated pneumonia: retrospective analysis of two double-blind studies comparing linezolid with vancomycin.

PubMed

Kollef, Marin H; Rello, Jordi; Cammarata, Sue K; Croos-Dabrera, Rodney V; Wunderink, Richard G

2004-03-01

To assess the effect of baseline variables, including treatment, on clinical cure and survival rates in patients with Gram-positive, ventilator-associated pneumonia (VAP). Retrospective analysis of two randomized, double-blind studies. Multinational study with 134 sites. 544 patients with suspected Gram-positive VAP, including 264 with documented Gram-positive VAP and 91 with methicillin-resistant S. aureus (MRSA) VAP. Linezolid 600 mg or vancomycin 1 g every 12 h for 7-21 days, each with aztreonam. Clinical cure rates assessed 12-28 days after the end of therapy and excluding indeterminate or missing outcomes significantly favored linezolid in the Gram-positive and MRSA subsets. Logistic regression showed that linezolid was an independent predictor of clinical cure with odds ratios of 1.8 for all patients, 2.4 for Gram-positive VAP, and 20.0 for MRSA VAP. Kaplan-Meier survival rates favored linezolid in the MRSA subset. Logistic regression showed that linezolid was an independent predictor of survival with odds ratios of 1.6 for all patients, 2.6 for Gram-positive VAP, and 4.6 for MRSA VAP. Initial linezolid therapy was associated with significantly better clinical cure and survival rates than was initial vancomycin therapy in patients with MRSA VAP.

High preoperative ratio of blood urea nitrogen to creatinine increased mortality in gastrointestinal cancer patients who developed postoperative enteric fistulas.

PubMed

Lin, Hsing-Lin; Chen, Chao-Wen; Lu, Chien-Yu; Sun, Li-Chu; Shih, Ying-Ling; Chuang, Jui-Fen; Huang, Yu-Ho; Sheen, Maw-Chang; Wang, Jaw-Yuan

2012-08-01

Development of an enteric fistula after surgery is a major therapeutic complication. In this study, we retrospectively examined the potential relationship between preoperative laboratory data and patient mortality by collecting patient data from a tertiary medical center. We included patients who developed enteric fistulas after surgery for gastrointestinal (GI) cancer between January 2005 and December 2010. Patient demographics and data on preoperative and pre-parenteral nutritional statuses were compared between surviving and deceased patients. Logistic regression analysis and receiver operating characteristic (ROC) curves were used to determine the predictors and cut-off values, respectively. Patients with incomplete data and preoperative heart, lung, kidney, and liver diseases were excluded from the study; thus, out of 65 patients, 43 were enrolled. Logistic regression analysis showed that blood urea nitrogen-to-creatinine (BUN/Cr) ratio [p = 0.007; OR = 0.443, 95% confidence interval (CI), 0.245-0.802] was an independent predictor of mortality in patients who developed enteric fistulas after surgery for GI cancer. In conclusion, the results of our study showed that a high preoperative BUN/Cr ratio increases the risk of mortality in patients who develop enteric fistulas after surgery for GI cancer. Copyright © 2012. Published by Elsevier B.V.

The Integrative Weaning Index in Elderly ICU Subjects.

PubMed

Azeredo, Leandro M; Nemer, Sérgio N; Barbas, Carmen Sv; Caldeira, Jefferson B; Noé, Rosângela; Guimarães, Bruno L; Caldas, Célia P

2017-03-01

With increasing life expectancy and ICU admission of elderly patients, mechanical ventilation, and weaning trials have increased worldwide. We evaluated a cohort with 479 subjects in the ICU. Patients younger than 18 y, tracheostomized, or with neurologic diseases were excluded, resulting in 331 subjects. Subjects ≥70 y old were considered elderly, whereas those <70 y old were considered non-elderly. Besides the conventional weaning indexes, we evaluated the performance of the integrative weaning index (IWI). The probability of successful weaning was investigated using relative risk and logistic regression. The Hosmer-Lemeshow goodness-of-fit test was used to calibrate and the C statistic was calculated to evaluate the association between predicted probabilities and observed proportions in the logistic regression model. Prevalence of successful weaning in the sample was 83.7%. There was no difference in mortality between elderly and non-elderly subjects ( P = .16), in days of mechanical ventilation ( P = .22) and days of weaning ( P = .55). In elderly subjects, the IWI was the only respiratory variable associated with mechanical ventilation weaning in this population ( P < .001). The IWI was the independent variable found in weaning of elderly subjects that may contribute to the critical moment of this population in intensive care. Copyright © 2017 by Daedalus Enterprises.

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.

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.

London Measure of Unplanned Pregnancy: guidance for its use as an outcome measure

PubMed Central

Hall, Jennifer A; Barrett, Geraldine; Copas, Andrew; Stephenson, Judith

2017-01-01

Background The London Measure of Unplanned Pregnancy (LMUP) is a psychometrically validated measure of the degree of intention of a current or recent pregnancy. The LMUP is increasingly being used worldwide, and can be used to evaluate family planning or preconception care programs. However, beyond recommending the use of the full LMUP scale, there is no published guidance on how to use the LMUP as an outcome measure. Ordinal logistic regression has been recommended informally, but studies published to date have all used binary logistic regression and dichotomized the scale at different cut points. There is thus a need for evidence-based guidance to provide a standardized methodology for multivariate analysis and to enable comparison of results. This paper makes recommendations for the regression method for analysis of the LMUP as an outcome measure. Materials and methods Data collected from 4,244 pregnant women in Malawi were used to compare five regression methods: linear, logistic with two cut points, and ordinal logistic with either the full or grouped LMUP score. The recommendations were then tested on the original UK LMUP data. Results There were small but no important differences in the findings across the regression models. Logistic regression resulted in the largest loss of information, and assumptions were violated for the linear and ordinal logistic regression. Consequently, robust standard errors were used for linear regression and a partial proportional odds ordinal logistic regression model attempted. The latter could only be fitted for grouped LMUP score. Conclusion We recommend the linear regression model with robust standard errors to make full use of the LMUP score when analyzed as an outcome measure. Ordinal logistic regression could be considered, but a partial proportional odds model with grouped LMUP score may be required. Logistic regression is the least-favored option, due to the loss of information. For logistic regression, the cut point for un/planned pregnancy should be between nine and ten. These recommendations will standardize the analysis of LMUP data and enhance comparability of results across studies. PMID:28435343

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.

The Mini Nutritional Assessment (MNA) predicts care need in older Taiwanese: results of a national cohort study.

PubMed

Tsai, Alan C; Hsu, Wei-Chung; Wang, Jiun-Yi

2014-06-14

The present study determined the ability of the Mini Nutritional Assessment (MNA) to predict care need in older people. We analysed the datasets of the Taiwan Longitudinal Study on Aging. The 1999 survey containing the MNA items served as the baseline and the 2003 survey served as the endpoint. Of the 4440 participants, 2890 were aged ≥ 65 years and served as subjects in the present study. After excluding 150 subjects having incomplete data, 2740 were rated for nutritional status with the normalised long-form (LF) and short-form (SF) MNA-Taiwan version 1 (T1) and version 2 (T2) and evaluated with logistic regression analysis for cross-sectional associations of the rated nutritional status with care need, controlled for age, sex, education level, living arrangement and physical activity. Receiver operating characteristic curves were generated for evaluating the ability of the MNA to predict care need. After further excluding 250 subjects who had care need at baseline and seventy-six who were lost to follow-up, 2414 were evaluated for the ability of the MNA to predict subsequent care need with logistic regression analysis. The results demonstrated that all the MNA predicted concurrent and subsequent care need well. The OR for needing subsequent care in the 'at-risk' and 'malnourished' groups were, respectively, 2·04 and 3·33 for the MNA-T1-LF, 2·10 and 5·35 for the MNA-T2-LF, 1·49 and 2·48 MNA-T1-SF, and 1·80 and 3·44 for the MNA-T2-SF (all P< 0·05), and the respective Nagelkerke R 2 values were 0·190, 0·191, 0·184 and 0·192. In conclusion, all the four MNA have the ability to predict future care need, including the MNA-T2-SF, which appears to have great potential for practical applicability.

Analysis of characteristics associated with reinjection of icatibant: Results from the icatibant outcome survey.

PubMed

Longhurst, Hilary J; Aberer, Werner; Bouillet, Laurence; Caballero, Teresa; Fabien, Vincent; Zanichelli, Andrea; Maurer, Marcus

2015-01-01

Phase 3 icatibant trials showed that most hereditary angioedema (HAE) (C1 inhibitor deficiency) acute attacks were treated successfully with one injection of icatibant, a selective bradykinin B2 receptor antagonist. We conducted a post hoc analysis of icatibant reinjection for HAE type I and II attacks in a real-world setting by using data from the Icatibant Outcome Survey, an ongoing observational study that monitors the safety and effectiveness of icatibant treatment. Descriptive retrospective analyses of icatibant reinjection were performed on Icatibant Outcome Survey data (February 2008 to December 2012). New attacks were defined as the onset of new symptoms after full resolution of the previous attack. Potential associations between the patient and attack characteristics and reinjection were explored by using logistic regression analysis. Icatibant was administered for 652 attacks in 170 patients with HAE type I or II. Most attacks (89.1%) were treated with a single icatibant injection. For attacks that required two or three injections, the second injection was given a median of 11.0 hours after the first injection, with 90.4% of second injections administered ≥6 hours after the first injection. Time to resolution and attack duration were significantly longer for two or three injections versus one icatibant injection (p < 0.0001 and p < 0.05, respectively). Multivariate logistic regression analysis identified sex, attack severity, and laryngeal attacks as significantly correlated with reinjection (all p ≤ 0.05). These factors did not remain predictors for reinjection when two outlier patients with distinct patterns of icatibant use were excluded. In this real-world setting, most HAE attacks resolved with one icatibant injection. There was no distinct profile for patients or attacks that required reinjection when outliers with substantially different patterns of use were excluded. Because new attacks were not distinguished from the recurrence of symptoms, reinjection rates may be slightly higher than shown here. Clinical trial identifier: NCT01034969.

Association of Discharge Home with Home Health Care and 30-day Readmission after Pancreatectomy

PubMed Central

Sanford, Dominic E; Olsen, Margaret A; Bommarito, Kerry M; Shah, Manish; Fields, Ryan C; Hawkins, William G; Jaques, David P; Linehan, David C

2014-01-01

Background We sought to determine if discharge home with home health care (HHC) is an independent predictor of increased readmission following pancreatectomy. Study Design We examined 30-day readmissions in patients undergoing pancreatectomy using the Healthcare Cost and Utilization Project State Inpatient Database for California from 2009 to 2011. Readmissions were categorized as severe or non-severe using the Modified Accordion Severity Grading System. Multivariable logistic regression models were used to examine the association of discharge home with HHC and 30-day readmission using discharge home without HHC as the reference group. Propensity score matching was used as an additional analysis to compare the rate of 30-day readmission between patients discharged home with HHC to patients discharged home without HHC. Results 3,573 patients underwent pancreatectomy and 752 (21.0%) were readmitted within 30 days of discharge. In a multivariable logistic regression model, discharge home with HHC was an independent predictor of increased 30-day readmission (OR=1.37; 95%CI=1.11-1.69, p=0.004). Using propensity score matching, patients who received HHC had a significantly increased rate of 30-day readmission compared to patients discharged home without HHC (24.3% vs 19.8%, p<0.001). Patients discharged home with HHC had a significantly increased rate of non-severe readmission compared to those discharged home without HHC by univariate comparison (19.2% vs 13.9%, p<0.001), but not severe readmission (6.4% vs 4.7%, p= 0.08). In multivariable logistic regression models, excluding patients discharged to facilities, discharge home with HHC was an independent predictor of increased non-severe readmissions (OR=1.41; 95%CI=1.11-1.79, p=0.005), but not severe readmissions (OR=1.31; 95%CI=0.88-1.93, p=0.18). Conclusions Discharge home with HHC following pancreatectomy is an independent predictor of increased 30-day readmission; specifically, these services are associated with increased non-severe readmissions, but not severe readmissions. PMID:25440026

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

75 FR 11476 - Proposed Amendment of Class D and E Airspace; Victorville, CA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-11

... needed for Instrument Flight Rules (IFR) operations at Southern California Logistics Airport that would... Southern California Logistics Airport, Victorville, CA excluding that airspace within a 1.5-mile radius of... International Airport to Southern California Logistics Airport, in both Class D and E airspace descriptions...

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.

Reliability of a Bayesian network to predict an elevated aldosterone-to-renin ratio.

PubMed

Ducher, Michel; Mounier-Véhier, Claire; Lantelme, Pierre; Vaisse, Bernard; Baguet, Jean-Philippe; Fauvel, Jean-Pierre

2015-05-01

Resistant hypertension is common, mainly idiopathic, but sometimes related to primary aldosteronism. Thus, most hypertension specialists recommend screening for primary aldosteronism. To optimize the selection of patients whose aldosterone-to-renin ratio (ARR) is elevated from simple clinical and biological characteristics. Data from consecutive patients referred between 1 June 2008 and 30 May 2009 were collected retrospectively from five French 'European excellence hypertension centres' institutional registers. Patients were included if they had at least one of: onset of hypertension before age 40 years, resistant hypertension, history of hypokalaemia, efficient treatment by spironolactone, and potassium supplementation. An ARR>32 ng/L and aldosterone>160 ng/L in patients treated without agents altering the renin-angiotensin system was considered as elevated. Bayesian network and stepwise logistic regression were used to predict an elevated ARR. Of 334 patients, 89 were excluded (31 for incomplete data, 32 for taking agents that alter the renin-angiotensin system and 26 for other reasons). Among 245 included patients, 110 had an elevated ARR. Sensitivity reached 100% or 63.3% using Bayesian network or logistic regression, respectively, and specificity reached 89.6% or 67.2%, respectively. The area under the receiver-operating-characteristic curve obtained with the Bayesian network was significantly higher than that obtained by stepwise regression (0.93±0.02 vs. 0.70±0.03; P<0.001). In hypertension centres, Bayesian network efficiently detected patients with an elevated ARR. An external validation study is required before use in primary clinical settings. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

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.

[The relationship of halitosis and Helicobacter pylori].

PubMed

Chen, Xi; Tao, Dan-ying; Li, Qing; Feng, Xi-ping

2007-06-01

The aim of the study was to investigate the relationship between halitosis and Helicobacter pylori infection in stomach. Fifty subjects without periodontal diseases and systematic disease (exclude gastrointestinal diseases) were included. Infection of H.pylori was diagnosed by biopsy and (14)C-urea breath test. SPSS11.5 software package was used to analyze the data. All the subjects were periodontal healthy according to the periodontal index. The prevalence of H.pylori infection in halitosis subjects was significantly higher than that in the normal subjects (57.1% VS 18.2%, P<0.01). Logistic regression analysis showed that H.pylori was the only significant variable in the equation(P<0.05). H.pylori in stomach may be involved in the presence of halitosis in periodontal healthy subjects.

Association between colonic polyps and diverticular disease

PubMed Central

Hirata, Tetsuo; Kawakami, Yuko; Kinjo, Nagisa; Arakaki, Susumu; Arakaki, Tetsu; Hokama, Akira; Kinjo, Fukunori; Fujita, Jiro

2008-01-01

AIM: To evaluate the association between colonic polyps and diverticular disease in Japan. METHODS: We retrospectively reviewed the medical records of 672 consecutive patients who underwent total colonoscopy between August 2006 and April 2007 at Nishinjo Hospital, Okinawa, Japan. Patients with a history of any of the following were excluded from the study: previous polypectomy, colonic resection, and inflammatory bowel diseases. The association between colonic polyps and diverticular disease was analyzed by logistic regression analysis, adjusted for age and sex. RESULTS: Prevalence of colonic polyps in all patients with diverticular disease was significantly higher than that in those without diverticular disease (adjusted odds ratio 1.7). CONCLUSION: Our data showed that patients with diverticular disease have a higher risk of colonic polyps compared to those without. PMID:18416471

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.

Reducing false-positive incidental findings with ensemble genotyping and logistic regression based variant filtering methods.

PubMed

Hwang, Kyu-Baek; Lee, In-Hee; Park, Jin-Ho; Hambuch, Tina; Choe, Yongjoon; Kim, MinHyeok; Lee, Kyungjoon; Song, Taemin; Neu, Matthew B; Gupta, Neha; Kohane, Isaac S; Green, Robert C; Kong, Sek Won

2014-08-01

As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false-positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here, we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false-negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous single nucleotide variants (SNVs); 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery in NA12878, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and an ensemble genotyping would be essential to minimize false-positive DNM candidates. © 2014 WILEY PERIODICALS, INC.

Reducing false positive incidental findings with ensemble genotyping and logistic regression-based variant filtering methods

PubMed Central

Hwang, Kyu-Baek; Lee, In-Hee; Park, Jin-Ho; Hambuch, Tina; Choi, Yongjoon; Kim, MinHyeok; Lee, Kyungjoon; Song, Taemin; Neu, Matthew B.; Gupta, Neha; Kohane, Isaac S.; Green, Robert C.; Kong, Sek Won

2014-01-01

As whole genome sequencing (WGS) uncovers variants associated with rare and common diseases, an immediate challenge is to minimize false positive findings due to sequencing and variant calling errors. False positives can be reduced by combining results from orthogonal sequencing methods, but costly. Here we present variant filtering approaches using logistic regression (LR) and ensemble genotyping to minimize false positives without sacrificing sensitivity. We evaluated the methods using paired WGS datasets of an extended family prepared using two sequencing platforms and a validated set of variants in NA12878. Using LR or ensemble genotyping based filtering, false negative rates were significantly reduced by 1.1- to 17.8-fold at the same levels of false discovery rates (5.4% for heterozygous and 4.5% for homozygous SNVs; 30.0% for heterozygous and 18.7% for homozygous insertions; 25.2% for heterozygous and 16.6% for homozygous deletions) compared to the filtering based on genotype quality scores. Moreover, ensemble genotyping excluded > 98% (105,080 of 107,167) of false positives while retaining > 95% (897 of 937) of true positives in de novo mutation (DNM) discovery, and performed better than a consensus method using two sequencing platforms. Our proposed methods were effective in prioritizing phenotype-associated variants, and ensemble genotyping would be essential to minimize false positive DNM candidates. PMID:24829188

A 3-Year Study of Predictive Factors for Positive and Negative Appendicectomies.

PubMed

Chang, Dwayne T S; Maluda, Melissa; Lee, Lisa; Premaratne, Chandrasiri; Khamhing, Srisongham

2018-03-06

Early and accurate identification or exclusion of acute appendicitis is the key to avoid the morbidity of delayed treatment for true appendicitis or unnecessary appendicectomy, respectively. We aim (i) to identify potential predictive factors for positive and negative appendicectomies; and (ii) to analyse the use of ultrasound scans (US) and computed tomography (CT) scans for acute appendicitis. All appendicectomies that took place at our hospital from the 1st of January 2013 to the 31st of December 2015 were retrospectively recorded. Test results of potential predictive factors of acute appendicitis were recorded. Statistical analysis was performed using Fisher exact test, logistic regression analysis, sensitivity, specificity, and positive and negative predictive values calculation. 208 patients were included in this study. 184 patients had histologically proven acute appendicitis. The other 24 patients had either nonappendicitis pathology or normal appendix. Logistic regression analysis showed statistically significant associations between appendicitis and white cell count, neutrophil count, C-reactive protein, and bilirubin. Neutrophil count was the test with the highest sensitivity and negative predictive values, whereas bilirubin was the test with the highest specificity and positive predictive values (PPV). US and CT scans had high sensitivity and PPV for diagnosing appendicitis. No single test was sufficient to diagnose or exclude acute appendicitis by itself. Combining tests with high sensitivity (abnormal neutrophil count, and US and CT scans) and high specificity (raised bilirubin) may predict acute appendicitis more accurately.

Outcome and prognostic factors in critically ill patients with systemic lupus erythematosus: a retrospective study.

PubMed

Hsu, Chia-Lin; Chen, Kuan-Yu; Yeh, Pu-Sheng; Hsu, Yeong-Long; Chang, Hou-Tai; Shau, Wen-Yi; Yu, Chia-Li; Yang, Pan-Chyr

2005-06-01

Systemic lupus erythematosus (SLE) is an archetypal autoimmune disease, involving multiple organ systems with varying course and prognosis. However, there is a paucity of clinical data regarding prognostic factors in SLE patients admitted to the intensive care unit (ICU). From January 1992 to December 2000, all patients admitted to the ICU with a diagnosis of SLE were included. Patients were excluded if the diagnosis of SLE was established at or after ICU admission. A multivariate logistic regression model was applied using Acute Physiology and Chronic Health Evaluation II scores and variables that were at least moderately associated (P < 0.2) with survival in the univariate analysis. A total of 51 patients meeting the criteria were included. The mortality rate was 47%. The most common cause of admission was pneumonia with acute respiratory distress syndrome. Multivariate logistic regression analysis showed that intracranial haemorrhage occurring while the patient was in the ICU (relative risk = 18.68), complicating gastrointestinal bleeding (relative risk = 6.97) and concurrent septic shock (relative risk = 77.06) were associated with greater risk of dying, whereas causes of ICU admission and Acute Physiology and Chronic Health Evaluation II score were not significantly associated with death. The mortality rate in critically ill SLE patients was high. Gastrointestinal bleeding, intracranial haemorrhage and septic shock were significant prognostic factors in SLE patients admitted to the ICU.

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.

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…

Shifting hospital care to primary care: An evaluation of cardiology care in a primary care setting in the Netherlands.

PubMed

Quanjel, Tessa C C; Struijs, Jeroen N; Spreeuwenberg, Marieke D; Baan, Caroline A; Ruwaard, Dirk

2018-05-09

In an attempt to deal with the pressures on the healthcare system and to guarantee sustainability, changes are needed. This study is focused on a cardiology Primary Care Plus intervention in which cardiologists provide consultations with patients in a primary care setting in order to prevent unnecessary referrals to the hospital. This study explores which patients with non-acute and low-complexity cardiology-related health complaints should be excluded from Primary Care Plus and referred directly to specialist care in the hospital. This is a retrospective observational study based on quantitative data. Data collected between January 1 and December 31, 2015 were extracted from the electronic medical record system. Logistic regression analyses were used to select patient groups that should be excluded from referral to Primary Care Plus. In total, 1525 patients were included in the analyses. Results showed that male patients, older patients, those with the referral indication 'Stable Angina Pectoris' or 'Dyspnoea' and patients whose reason for referral was 'To confirm disease' or 'Screening of unclear pathology' had a significantly higher probability of being referred to hospital care after Primary Care Plus. To achieve efficiency one should exclude patient groups with a significantly higher probability of being referred to hospital care after Primary Care Plus. NTR6629 (Data registered: 25-08-2017) (registered retrospectively).

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.

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.

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.

Racial and ethnic differences in pediatric obesity-prevention counseling: national prevalence of clinician practices.

PubMed

Branner, Christopher M; Koyama, Tatsuki; Jensen, Gordon L

2008-03-01

To assess the frequency of clinician-reported delivery of obesity-prevention counseling (OPC) at well-child visits; evaluating for racial/ethnic discrepancies. Combined, weighted well-child visit data from the National Ambulatory Medical Care Survey (NAMCS) and National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2001 to 2004 were analyzed for patients aged 4-18 years. Obesity-prevention counseling was defined as the combined delivery of diet/nutrition and exercise counseling. Patients receiving over- or underweight related diagnoses were excluded. Counseling frequencies were calculated. Multivariate logistic regression models examined the relationship of OPC with race, ethnicity, region, provider, sex, age, and payor type. Of 55,695,554 (weighted) visits, 24.4% included OPC (90.8% of these from NAMCS). 15.4% of Hispanic patients received OPC compared to 28.8% of non-Hispanics. Frequencies were similar between Whites and Blacks (25.0 and 27.1%). Patients with private insurance received more counseling (26.9%) than Medicaid (19.1%) or self-pay (15.1%). In logistic regression models, non-Hispanics were more likely to receive OPC (odds ratio (OR) = 1.94; confidence interval (CI) = 1.13-3.32), and patients in the West were less likely to receive OPC (OR = 0.39; CI = 0.18-0.85). Payor type was not predictive in regression analysis. Patients in hospital-based practices received less OPC (11.9% vs. 25.7% with OR = 0.40; CI =0.22-0.74). Obesity prevention, like treatment, is a complex and multifactorial process. With the documented racial and ethnic disparities in rates of pediatric obesity, reasons for discrepancies in the provision of OPC must be further investigated as preventive strategies are formulated.

Role of the Egami score to predict immunoglobulin resistance in Kawasaki disease among a Western Mediterranean population.

PubMed

Sánchez-Manubens, Judith; Antón, Jordi; Bou, Rosa; Iglesias, Estíbaliz; Calzada-Hernandez, Joan; Borlan, Sergi; Gimenez-Roca, Clara; Rivera, Josefa

2016-07-01

Kawasaki disease is an acute self-limited systemic vasculitis common in childhood. Intravenous immunoglobulin (IVIG) is an effective treatment, and it reduces the incidence of cardiac complications. Egami score has been validated to identify IVIG non-responder patients in Japanese population, and it has shown high sensitivity and specificity to identify these non-responder patients. Although its effectiveness in Japan, Egami score has shown to be ineffective in non-Japanese populations. The aim of this study was to apply the Egami score in a Western Mediterranean population in Catalonia (Spain). Observational population-based study that includes patients from all Pediatric Units in 33 Catalan hospitals, both public and private management, between January 2004 and March 2014. Sensitivity and specificity for the Egami score was calculated, and a logistic regression analysis of predictors of overall response to IVIG was also developed. Predicting IVIG resistance with a cutoff for Egami score ≥3 obtained 26 % sensitivity and 82 % specificity. Negative predictive value was 85 % and positive predictive value 22 %. This low sensitivity implies that three out of four non-responders will not be identified by the Egami score. Besides, logistic regression models did not found significance for the use of the Egami score to predict IVIG resistance in Catalan population although having an area under the ROC curve of 0.618 (IC 95 % 0.538-0.698, p < 0.001). Although regression models found an area under the ROC curve >0.5 to predict IVIG resistance, the low sensitivity excludes the Egami score as a useful tool to predict IVIG resistance in Catalan population.

Cesarean delivery rates among family physicians versus obstetricians: a population-based cohort study using instrumental variable methods

PubMed Central

Dawe, Russell Eric; Bishop, Jessica; Pendergast, Amanda; Avery, Susan; Monaghan, Kelly; Duggan, Norah; Aubrey-Bassler, Kris

2017-01-01

Background: Previous research suggests that family physicians have rates of cesarean delivery that are lower than or equivalent to those for obstetricians, but adjustments for risk differences in these analyses may have been inadequate. We used an econometric method to adjust for observed and unobserved factors affecting the risk of cesarean delivery among women attended by family physicians versus obstetricians. Methods: This retrospective population-based cohort study included all Canadian (except Quebec) hospital deliveries by family physicians and obstetricians between Apr. 1, 2006, and Mar. 31, 2009. We excluded women with multiple gestations, and newborns with a birth weight less than 500 g or gestational age less than 20 weeks. We estimated the relative risk of cesarean delivery using instrumental-variable-adjusted and logistic regression. Results: The final cohort included 776 299 women who gave birth in 390 hospitals. The risk of cesarean delivery was 27.3%, and the mean proportion of deliveries by family physicians was 26.9% (standard deviation 23.8%). The relative risk of cesarean delivery for family physicians versus obstetricians was 0.48 (95% confidence interval [CI] 0.41-0.56) with logistic regression and 1.27 (95% CI 1.02-1.57) with instrumental-variable-adjusted regression. Interpretation: Our conventional analyses suggest that family physicians have a lower rate of cesarean delivery than obstetricians, but instrumental variable analyses suggest the opposite. Because instrumental variable methods adjust for unmeasured factors and traditional methods do not, the large discrepancy between these estimates of risk suggests that clinical and/or sociocultural factors affecting the decision to perform cesarean delivery may not be accounted for in our database. PMID:29233843

Increased left ventricular mass index is present in patients with type 2 diabetes without ischemic heart disease.

PubMed

Seferovic, Jelena P; Tesic, Milorad; Seferovic, Petar M; Lalic, Katarina; Jotic, Aleksandra; Biering-Sørensen, Tor; Giga, Vojislav; Stankovic, Sanja; Milic, Natasa; Lukic, Ljiljana; Milicic, Tanja; Macesic, Marija; Gajovic, Jelena Stanarcic; Lalic, Nebojsa M

2018-01-17

Left ventricular mass index (LVMI) increase has been described in hypertension (HTN), but less is known about its association with type 2 diabetes (T2DM). As these conditions frequently co-exist, we investigated the association of T2DM, HTN and both with echocardiographic parameters, and hypothesized that patients with both had highest LVMI, followed by patients with only T2DM or HTN. Study population included 101 T2DM patients, 62 patients with HTN and no T2DM, and 76 patients with T2DM and HTN, excluded for ischemic heart disease. Demographic and clinical data, biochemical measurements, stress echocardiography, transthoracic 2D Doppler and tissue Doppler echocardiography were performed. Multivariable logistic regression was used to determine the independent association with T2DM. Linear regression models and Pearson's correlation were used to assess the correlations between LVMI and other parameters. Patients with only T2DM had significantly greater LVMI (84.9 ± 20.3 g/m 2 ) compared to patients with T2DM and HTN (77.9 ± 16 g/m 2 ) and only HTN (69.8 ± 12.4 g/m 2 ). In multivariate logistic regression analysis, T2DM was associated with LVMI (OR 1.033, 95%CI 1.003-1.065, p = 0.029). A positive correlation of LVMI was found with fasting glucose (p < 0.001) and HbA1c (p = 0.0003). Increased LVMI could be a potential, pre-symptomatic marker of myocardial structural change in T2DM.

Geographic variation in inquest rates in Australia.

PubMed

Walter, Simon J; Bugeja, Lyndal; Spittal, Matthew J; Studdert, David M

2012-11-01

This paper examines the relationship between the remoteness of locations in which deaths occur and coroners' decisions to hold inquests. We analysed 16,242 deaths investigated by coroners in three Australian states over 7.5 yrs. We used a choropleth map to show inquest rates in each remoteness locality (excluding deaths for which inquests were mandated by statute). We then used adjusted logistic regression to assess the association between the remoteness of a death's location and the odds coroners would select it for investigation by inquest. We found the remoteness of a death's location strongly and positively predicts the chance that an inquest will be held. Like analogous findings in the delivery of health services, this small-area variation in legal decision making raises questions of appropriateness. Copyright © 2012 Elsevier Ltd. All rights reserved.

Effectiveness of mumps vaccine in a school outbreak.

PubMed

Sullivan, K M; Halpin, T J; Marks, J S; Kim-Farley, R

1985-09-01

An outbreak of mumps in a middle school (grades 6 through 8) in Ohio during 1981 was investigated to determine the effectiveness of mumps vaccine. Of the 481 middle school students on whom questionnaires were completed, 62 (12.4%) exhibited clinical mumps. The overall vaccine efficacy was 81.2% when children with a history of mumps disease are excluded from the analysis. Using a logistic regression model with the presence or absence of clinical mumps as the dependent variable, three factors were found to be significant: mumps vaccine, a history of mumps disease, and sex. Factors that did not significantly affect the rate of disease among vaccinated pupils included whether the mumps vaccine was administered singly or in combination with rubella and/or measles vaccine, age at vaccination, year of vaccination, and month of vaccination.

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.

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

PubMed

Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.

Enrollment Management in Medical School Admissions: A Novel Evidence-Based Approach at One Institution.

PubMed

Burkhardt, John C; DesJardins, Stephen L; Teener, Carol A; Gay, Steven E; Santen, Sally A

2016-11-01

In higher education, enrollment management has been developed to accurately predict the likelihood of enrollment of admitted students. This allows evidence to dictate numbers of interviews scheduled, offers of admission, and financial aid package distribution. The applicability of enrollment management techniques for use in medical education was tested through creation of a predictive enrollment model at the University of Michigan Medical School (U-M). U-M and American Medical College Application Service data (2006-2014) were combined to create a database including applicant demographics, academic application scores, institutional financial aid offer, and choice of school attended. Binomial logistic regression and multinomial logistic regression models were estimated in order to study factors related to enrollment at the local institution versus elsewhere and to groupings of competing peer institutions. A predictive analytic "dashboard" was created for practical use. Both models were significant at P < .001 and had similar predictive performance. In the binomial model female, underrepresented minority students, grade point average, Medical College Admission Test score, admissions committee desirability score, and most individual financial aid offers were significant (P < .05). The significant covariates were similar in the multinomial model (excluding female) and provided separate likelihoods of students enrolling at different institutional types. An enrollment-management-based approach would allow medical schools to better manage the number of students they admit and target recruitment efforts to improve their likelihood of success. It also performs a key institutional research function for understanding failed recruitment of highly desirable candidates.

Relationships between temperaments, occupational stress, and insomnia among Japanese workers.

PubMed

Deguchi, Yasuhiko; Iwasaki, Shinichi; Ishimoto, Hideyuki; Ogawa, Koichiro; Fukuda, Yuichi; Nitta, Tomoko; Mitake, Tomoe; Nogi, Yukako; Inoue, Koki

2017-01-01

Insomnia among workers reduces the quality of life, contributes toward the economic burden of healthcare costs and losses in work performance. The relationship between occupational stress and insomnia has been reported in previous studies, but there has been little attention to temperament in occupational safety and health research. The aim of this study was to clarify the relationships between temperament, occupational stress, and insomnia. The subjects were 133 Japanese daytime local government employees. Temperament was assessed using the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto questionnaire (TEMPS-A). Occupational stress was assessed using the Generic Job Stress Questionnaire (GJSQ). Insomnia was assessed using the Athens Insomnia Scale (AIS). Stepwise multiple logistic regression analyses were conducted. In a stepwise multivariate logistic regression analysis, it was found that the higher subdivided stress group by "role conflict" (OR = 5.29, 95% CI, 1.61-17.32) and anxious temperament score (OR = 1.33; 95% CI, 1.19-1.49) was associated with the presence of insomnia using an adjusted model, whereas other factors were excluded from the model. The study limitations were the sample size and the fact that only Japanese local government employees were surveyed. This study demonstrated the relationships between workers' anxious temperament, role conflict, and insomnia. Recognizing one's own anxious temperament would lead to self-insight, and the recognition of anxious temperament and reduction of role conflict by their supervisors or coworkers would reduce the prevalence of insomnia among workers in the workplace.

[Patients' reaction to pharmacists wearing a mask during their consultations].

PubMed

Tamura, Eri; Kishimoto, Keiko; Fukushima, Noriko

2013-01-01

  This study sought to determine the effect of pharmacists wearing a mask on the consultation intention of patients who do not have a trusting relationship with the pharmacists. We conducted a questionnaire survey of customers at a Tokyo drugstore in August 2012. Subjects answered a questionnaire after watching two medical teaching videos, one in which the pharmacist was wearing a mask and the other in which the pharmacist was not wearing a mask. Data analysis was performed using a paired t-test and multiple logistic regression. The paired t-test revealed a significant difference in 'Maintenance Problem' between the two pharmacist situations. After excluding factors not associated with wearing a mask, multiple logistic regression analysis identified three independent variables with a significant effect on participants not wanting to consult with a pharmacist wearing a mask. Positive factors were 'active-inactive' and 'frequency mask use', a negative factor was 'age'. Our study has shown that pharmacists wearing a mask may be a factor that prevents patients from consulting with pharmacist. Those patients whose intention to consult might be affected by the pharmacists wearing a mask tended to be younger, to have no habit of wearing masks preventively themselves, and to form a negative opinion of such pharmacists. Therefore, it was estimated that pharmacists who wear masks need to provide medical education by asking questions more positively than when they do not wear a mask in order to prevent the patient worrying about oneself.

Association between the severity of symptomatic knee osteoarthritis and cumulative metabolic factors.

PubMed

Yasuda, Emi; Nakamura, Ryuichi; Matsugi, Ryo; Goto, Shinsuke; Ikenaga, Yasunori; Kuroda, Kazunari; Nakamura, Syunsuke; Katsuki, Yasuo; Katsuki, Tatsuo

2018-05-01

The association between cumulative metabolic syndrome (MS) factors and knee osteoarthritis (KOA) has been highlighted over the past two decades. To clarify the relationship between cumulative MS factors and symptomatic KOA. A cross-sectional survey involving 119 women aged 45-88 years who were scheduled to undergo knee surgery was conducted. They were stratified into tertiles of symptoms as assessed by the Japanese Orthopedic Association score for KOA. Multinomial logistic regressions were performed using the severity of symptomatic KOA as the dependent variable and each MS factor or the cumulative MS factors as the independent variables. Logistic regression analyses were performed with the upper tertile of stratified symptoms of subjects used as the reference group. After adjustment for confounders, KOA patients who had two (p = 0.004) or three or more (p < 0.0001) MS factors were significantly more likely to have severe symptoms compared to those who had no MS factors. MS factors excluding obesity were similarly analyzed. Even after additional adjustment for body mass index (BMI), KOA patients who had two or more (p = 0.005) MS factors were significantly more likely to have severe symptoms. Among KOA female patients diagnosed using radiographic definition, the severity of symptomatic KOA was significantly associated with hypertension, dyslipidemia, and the number of MS factors after adjustment for age, BMI, strength of the knee extensor, and Kellgren-Lawrence grade. The severity of radiographic KOA was not associated with any MS factor or cumulative MS factors.

Metabolic syndrome as an independent risk factor of hypoxaemia in influenza A (H1N1) 2009 pandemic.

PubMed

Bijani, Behzad; Pahlevan, Ali Asghar; Qasemi-Barqi, Reza; Jahanihashemi, Hassan

2016-06-01

A swine-origin influenza A (H1N1) emerged as a pandemic in 2009. We investigated the association between the overweight, metabolic syndrome and the severity of disease in the confirmed cases in Qazvin province, Iran. The study sample included all patients over 12 years old with confirmed influenza A (H1N1) in the province of Qazvin, Iran, in the 2009 pandemic, excluding pregnant women. To define overweight, sex and age-specific body mass index (BMI) cutoffs recommended by the International Obesity Task Force were used. Metabolic syndrome was defined by ATP III criteria. Multiple logistic regression analysis was performed to identify statistically independent predictors of hypoxaemia. Out of 55 confirmed cases, 28 (50.9%) were overweight and 24 (45.3%) were identified as having metabolic syndrome by ATP III criteria. Twenty four patients had hypoxaemia (arterial oxygen saturation below 90%) during the course of the disease. In multivariate logistic regression analysis, pulmonary co-morbidity (OR=9.54; 95% CI, 1.36 to 66.88; p= 0.023) and the metabolic syndrome (OR=18.66; 95% CI, 1.60 to 217.47; p= 0.019) were revealed to be independent risk factors for hypoxaemia in influenza A (H1N1) pdm09. The results of the present study reveal the role of the metabolic syndrome on the severity of influenza A (H1N1) pdm09 infection.

Relationships between temperaments, occupational stress, and insomnia among Japanese workers

PubMed Central

Ishimoto, Hideyuki; Ogawa, Koichiro; Fukuda, Yuichi; Nitta, Tomoko; Mitake, Tomoe; Nogi, Yukako; Inoue, Koki

2017-01-01

Insomnia among workers reduces the quality of life, contributes toward the economic burden of healthcare costs and losses in work performance. The relationship between occupational stress and insomnia has been reported in previous studies, but there has been little attention to temperament in occupational safety and health research. The aim of this study was to clarify the relationships between temperament, occupational stress, and insomnia. The subjects were 133 Japanese daytime local government employees. Temperament was assessed using the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto questionnaire (TEMPS-A). Occupational stress was assessed using the Generic Job Stress Questionnaire (GJSQ). Insomnia was assessed using the Athens Insomnia Scale (AIS). Stepwise multiple logistic regression analyses were conducted. In a stepwise multivariate logistic regression analysis, it was found that the higher subdivided stress group by “role conflict” (OR = 5.29, 95% CI, 1.61–17.32) and anxious temperament score (OR = 1.33; 95% CI, 1.19–1.49) was associated with the presence of insomnia using an adjusted model, whereas other factors were excluded from the model. The study limitations were the sample size and the fact that only Japanese local government employees were surveyed. This study demonstrated the relationships between workers’ anxious temperament, role conflict, and insomnia. Recognizing one’s own anxious temperament would lead to self-insight, and the recognition of anxious temperament and reduction of role conflict by their supervisors or coworkers would reduce the prevalence of insomnia among workers in the workplace. PMID:28407025

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.

Exposure to PM10 as a risk factor for the development of nasal obstruction and chronic obstructive pulmonary disease

PubMed Central

Sichletidis, Lazaros; Spyratos, Dionisios; Tsiotsios, Anastasios; Haidich, Anna-Bettina; Chloros, Diamantis; Ganidis, Ioannis; Michailidis, Dimitrios; Triantafyllou, Georgios; Kottakis, George; Melas, Dimitrios

2014-01-01

Objectives: To investigate whether air pollution is a potential risk factor for airways obstruction. Methods: A prospective cohort study (11.3±2.9 years) that took place in two areas (Eordea where concentration of PM10 was high and Grevena, Greece). We used the MRC questionnaire, spirometry, and anterior rhinomanometry at both visits. Results: Initially we examined 3046 subjects. After excluding chronic obstructive pulmonary disease (COPD) patients, we re-examined 872 subjects and 168 of them had developed COPD (Grevena: 24.3%, Eordea: 18.5%). Multivariable logistic regression analysis showed that the area of residence and thus exposure to air pollution was not a risk factor for the development of COPD (OR: 0.51, 95% CI: 0.18–1.46, P = 0.21). On the other hand, residence in Eordea was strongly related to the development of severe nasal obstruction (OR: 11.47, 95% CI: 6.15–21.40, P<0.001). Similar results were found after excluding patients with COPD stage I as well as in the subgroup of never smokers. Conclusion: Air pollution was associated with severe nasal obstruction but not with COPD development. PMID:24804336

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.

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.

PNPLA3 I148M associations with liver carcinogenesis in Japanese chronic hepatitis C patients.

PubMed

Nakaoka, Kazunori; Hashimoto, Senju; Kawabe, Naoto; Nitta, Yoshifumi; Murao, Michihito; Nakano, Takuji; Shimazaki, Hiroaki; Kan, Toshiki; Takagawa, Yuka; Ohki, Masashi; Kurashita, Takamitsu; Takamura, Tomoki; Nishikawa, Toru; Ichino, Naohiro; Osakabe, Keisuke; Yoshioka, Kentaro

2015-01-01

To investigate associations between patatin-like phospholipase domain-containing 3 (PNPLA3) genotypes and fibrosis and hepatocarcinogenesis in Japanese chronic hepatitis C (CHC) patients. Two hundred and thirty-one patients with CHC were examined for PNPLA3 genotypes, liver stiffness measurements (LSM), and hepatocellular carcinoma (HCC) from May 2010 to October 2012 at Fujita Health University Hospital. The rs738409 single nucleotide polymorphism (SNP) encoding for a functional PNPLA3 I148M protein variant was genotyped using a TaqMan predesigned SNP genotyping assay. LSM was determined as the velocity of a shear wave (Vs) with an acoustic radiation force impulse. Vs cut-off values for cirrhosis were set at 1.55 m/s. We excluded CHC patients with a sustained virological response or relapse after interferon treatment. PNPLA3 genotypes were CC, CG, and GG for 118, 72, and 41 patients, respectively. Multivariable logistic regression analysis selected older age (OR = 1.06; 95% CI: 1.03-1.09; p < 0.0001), higher body mass index (BMI) (OR= 1.12; 95% CI: 1.03-1.22; p = 0.0082), and PNPLA3 genotype GG (OR = 2.07; 95% CI: 0.97-4.42; p = 0.0599) as the factors independently associated with cirrhosis. When 137 patients without past history of interferon treatment were separately assessed, multivariable logistic regression analysis selected older age (OR = 1.05; 95% CI: 1.02-1.09; p = 0.0034), and PNPLA3 genotype GG (OR = 3.35; 95% CI: 1.13-9.91; p = 0.0291) as the factors independently associated with cirrhosis. Multivariable logistic regression analysis selected older age (OR = 1.12; 95% CI: 1.07-1.17; p < 0.0001), PNPLA3 genotype GG (OR = 2.62; 95% CI: 1.15-5.96; p = 0.0218), and male gender (OR = 1.83; 95% CI: 0.90-3.71); p = 0.0936) as the factors independently associated with HCC. PNPLA3 genotype I148M is one of risk factors for developing HCC in Japanese CHC patients, and is one of risk factors for progress to cirrhosis in the patients without past history of interferon treatment.

Quantifying the yellow signal driver behavior based on naturalistic data from digital enforcement cameras.

PubMed

Bar-Gera, H; Musicant, O; Schechtman, E; Ze'evi, T

2016-11-01

The yellow signal driver behavior, reflecting the dilemma zone behavior, is analyzed using naturalistic data from digital enforcement cameras. The key variable in the analysis is the entrance time after the yellow onset, and its distribution. This distribution can assist in determining two critical outcomes: the safety outcome related to red-light-running angle accidents, and the efficiency outcome. The connection to other approaches for evaluating the yellow signal driver behavior is also discussed. The dataset was obtained from 37 digital enforcement cameras at non-urban signalized intersections in Israel, over a period of nearly two years. The data contain more than 200 million vehicle entrances, of which 2.3% (∼5million vehicles) entered the intersection during the yellow phase. In all non-urban signalized intersections in Israel the green phase ends with 3s of flashing green, followed by 3s of yellow. In most non-urban signalized roads in Israel the posted speed limit is 90km/h. Our analysis focuses on crossings during the yellow phase and the first 1.5s of the red phase. The analysis method consists of two stages. In the first stage we tested whether the frequency of crossings is constant at the beginning of the yellow phase. We found that the pattern was stable (i.e., the frequencies were constant) at 18 intersections, nearly stable at 13 intersections and unstable at 6 intersections. In addition to the 6 intersections with unstable patterns, two other outlying intersections were excluded from subsequent analysis. Logistic regression models were fitted for each of the remaining 29 intersection. We examined both standard (exponential) logistic regression and four parameters logistic regression. The results show a clear advantage for the former. The estimated parameters show that the time when the frequency of crossing reduces to half ranges from1.7 to 2.3s after yellow onset. The duration of the reduction of the relative frequency from 0.9 to 0.1 ranged from 1.9 to 2.9s. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-01-01

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

Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

PubMed

Donath, Carolin; Graessel, Elmar; Baier, Dirk; Bleich, Stefan; Hillemacher, Thomas

2014-04-26

Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents' suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Three parental variables showed a relevant association with suicide attempts in adolescents - (all protective): mother's warmth and father's warmth in childhood and mother's control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk - as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD.

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.

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.

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.

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.

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.

The Cycle of Social Exclusion for Urban, Young Men of Color in the United States: What is the Role of Incarceration?

PubMed Central

RAMASWAMY, MEGHA; FREUDENBERG, NICHOLAS

2013-01-01

This article explores how incarceration amplifies the disconnection from school and work experienced by urban, young men of color in the United States and ultimately leads to their social exclusion. The authors draw on longitudinal data collected in interviews with 397 men age 16 to 18 in a New York City jail and then again one year after their release. Using logistic regression analysis, the authors found that though incarceration did not appear to exacerbate disconnectedness directly, it was associated with unstable housing, which in turn may contribute to several negative outcomes related to social exclusion. These findings may inform advocates, policy makers, and researchers in their efforts to meet the needs of socially excluded youth, in particular those with criminal justice histories. PMID:24431927

Is Thrombus With Subcutaneous Edema Detected by Ultrasonography Related to Short Peripheral Catheter Failure? A Prospective Observational Study.

PubMed

Takahashi, Toshiaki; Murayama, Ryoko; Oe, Makoto; Nakagami, Gojiro; Tanabe, Hidenori; Yabunaka, Koichi; Arai, Rika; Komiyama, Chieko; Uchida, Miho; Sanada, Hiromi

Short peripheral catheter (SPC) failure is an important clinical problem. The purpose of this study was to clarify the relationship between SPC failure and etiologies such as thrombus, subcutaneous edema, and catheter dislodgment using ultrasonography and to explore the risk factors associated with the etiologies. Two hundred catheters that were in use for infusion, excluding chemotherapy, were observed. Risk factors were examined by logistic regression analysis. Sixty catheters were removed as the result of SPC failure. Frequency of thrombus with subcutaneous edema in SPC failure cases was significantly greater than in those cases where therapy was completed without complications (P < .01). Multivariate analysis demonstrated that 2 or more insertion attempts were significantly associated with thrombus with subcutaneous edema. Results suggest that subsurface skin assessment for catheterization could prevent SPC failure.

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.

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…

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.

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.

Menstrual pain and risk of epithelial ovarian cancer: Results from the Ovarian Cancer Association Consortium.

PubMed

Babic, Ana; Harris, Holly R; Vitonis, Allison F; Titus, Linda J; Jordan, Susan J; Webb, Penelope M; Risch, Harvey A; Rossing, Mary Anne; Doherty, Jennifer A; Wicklund, Kristine; Goodman, Marc T; Modugno, Francesmary; Moysich, Kirsten B; Ness, Roberta B; Kjaer, Susanne K; Schildkraut, Joellen; Berchuck, Andrew; Pearce, Celeste L; Wu, Anna H; Cramer, Daniel W; Terry, Kathryn L

2018-02-01

Menstrual pain, a common gynecological condition, has been associated with increased risk of ovarian cancer in some, but not all studies. Furthermore, potential variations in the association between menstrual pain and ovarian cancer by histologic subtype have not been adequately evaluated due to lack of power. We assessed menstrual pain using either direct questions about having experienced menstrual pain, or indirect questions about menstrual pain as indication for use of hormones or medications. We used multivariate logistic regression to calculate the odds ratio (OR) for the association between severe menstrual pain and ovarian cancer, adjusting for potential confounders and multinomial logistic regression to calculate ORs for specific histologic subtypes. We observed no association between ovarian cancer and menstrual pain assessed by indirect questions. Among studies using direct question, severe pain was associated with a small but significant increase in overall risk of ovarian cancer (OR = 1.07, 95% CI: 1.01-1.13), after adjusting for endometriosis and other potential confounders. The association appeared to be more relevant for clear cell (OR = 1.48, 95% CI: 1.10-1.99) and serous borderline (OR = 1.31, 95% CI: 1.05-1.63) subtypes. In this large international pooled analysis of case-control studies, we observed a small increase in risk of ovarian cancer for women reporting severe menstrual pain. While we observed an increased ovarian cancer risk with severe menstrual pain, the possibility of recall bias and undiagnosed endometriosis cannot be excluded. Future validation in prospective studies with detailed information on endometriosis is needed. © 2017 UICC.

Radiographic absence of the posterior communicating arteries and the prediction of cognitive dysfunction after carotid endarterectomy.

PubMed

Sussman, Eric S; Kellner, Christopher P; Mergeche, Joanna L; Bruce, Samuel S; McDowell, Michael M; Heyer, Eric J; Connolly, E Sander

2014-09-01

Approximately 25% of patients exhibit cognitive dysfunction 24 hours after carotid endarterectomy (CEA). One of the purported mechanisms of early cognitive dysfunction (eCD) is hypoperfusion due to inadequate collateral circulation during cross-clamping of the carotid artery. The authors assessed whether poor collateral circulation within the circle of Willis, as determined by preoperative CT angiography (CTA) or MR angiography (MRA), could predict eCD. Patients who underwent CEA after preoperative MRA or CTA imaging and full neuropsychometric evaluation were included in this study (n = 42); 4 patients were excluded due to intraoperative electroencephalographic changes and subsequent shunt placement. Thirty-eight patients were included in the statistical analyses. Patients were stratified according to posterior communicating artery (PCoA) status (radiographic visualization of at least 1 PCoA vs of no PCoAs). Variables with p < 0.20 in univariate analyses were included in a stepwise multivariate logistic regression model to identify predictors of eCD after CEA. Overall, 23.7% of patients exhibited eCD. In the final multivariate logistic regression model, radiographic absence of both PCoAs was the only independent predictor of eCD (OR 9.64, 95% CI 1.43-64.92, p = 0.02). The absence of both PCoAs on preoperative radiographic imaging is predictive of eCD after CEA. This finding supports the evidence for an underlying ischemic etiology of eCD. Larger studies are justified to verify the findings of this study. Clinical trial registration no.: NCT00597883 ( http://www.clinicaltrials.gov ).

The impact of OAB on physical activity in the United States: results from OAB-POLL.

PubMed

Coyne, Karin S; Sexton, Chris C; Clemens, J Quentin; Thompson, Christine L; Chen, Chieh-I; Bavendam, Tamara; Dmochowski, Roger

2013-10-01

To provide data on physical activity among those with and without overactive bladder (OAB) in a large, ethnically diverse U.S. sample. A cross-sectional survey was conducted via the Internet among 10,000 men and women aged 18-70 (2000 African Americans, 2000 Hispanics, and 6000 whites) using the lower urinary tract symptoms (LUTS) tool and questions from the 2007-2008 National Health and Nutrition Examination Survey (NHANES). OAB cases and those with no/minimal symptoms (NMS) were compared on federal guidelines of indices of physical activity: 2008 guidelines and 2010 Healthy People. Descriptive statistics were used to evaluate differences between OAB and NMS. Logistic regressions examined the impact of OAB on physical activity. Response rate, 57%; 818 men and 1505 women with OAB, and 1857 men and 1615 women with NMS. Respondents with other LUTS were excluded from this analysis (2302 men and 1904 women). Those with OAB were significantly less likely to report moderate and vigorous physical activities in their leisure time and to satisfy recommended physical activity levels compared to those with NMS. Symptoms of OAB (men and women: urgency and urinary frequency; women: urinary urge incontinence) were associated with limitations in physical activity in the logistic regressions. This study benchmarks physical activity levels among people with OAB. Men and women with OAB were significantly less likely to achieve recommended physical activity levels than people with NMS. More research is needed to further evaluate how OAB affects physical activity and health status and to determine causal relationships. Copyright © 2013 Elsevier Inc. All rights reserved.

[Risk factors for the kidney stones: a hospital-based case-control study in a distric hospital in Beijing].

PubMed

Wang, Jiao; Luo, Gong-tang; Niu, Wei-jing; Gong, Man-man; Liu, Lu; Zhou, Jie; Zhou, Xue-wei; He, Li-hua

2013-12-18

To explore the risk and protective factors of kidney calculi in order to put forward theoretical basis for preventive and control measures. A 1:1 matched case-control study was performed using data from a hospital in Beijing. The case group included 100 inpatients who were diagnosed kidney calculi using B ultrasonic, X-ray and intravenous pyelography during the survey while other 100 urolithiasis and endocrine disease excluded inpatients who shared the same sex, within five years gap to the case group inpatients were for the control group. A face-to-face survey was conducted with self-made questionnaires which covered demographic characteristics, water issues, dietary habits, genetic and medical history. Epidata 3.0 was used to build the database and SPSS 19.0 for the statistical analysis. In the univariate Logistic regression analysis, ten variables were found showing statistical significance. For the multivariate Logistic regression analysis, variables left in the model were labor intensity (OR=0.622, 95%CI: 0.435-0.889), preferring to drink after dinner (OR=0.316, 95%CI: 0.122-0.815), loving drinking (OR=0.232, 95%CI: 0.084-0.642), drinking tea regularly (OR=1.463, 95%CI: 1.033-2.071), eating more vegetables (OR=0.571, 95%CI: 0.328-0.993), the history of the urolithiasis (OR=2.127, 95%CI: 1.065-90.145). Drinking tea regularly, urolithiasis history and brain work are the risk factors of kidney calculi while loving drinking and eating more vegetables for the protection.

Prevalence of cognitive and functional impairment in a community sample in Ribeirão Preto, Brazil.

PubMed

Lopes, Marcos A; Hototian, Sergio R; Bustamante, Sonia E Z; Azevedo, Dionísio; Tatsch, Mariana; Bazzarella, Mário C; Litvoc, Júlio; Bottino, Cássio M C

2007-08-01

This study aimed at estimating the prevalence of cognitive and functional impairment (CFI) in a community sample in Ribeirão Preto, Brazil, evaluating its distribution in relation to various socio-demographic and clinical factors. The population was a representative sample aged 60 and older, from three different socio-economic classes. Cluster sampling was applied. Instruments used to select CFI (a syndromic category that does not exclude dementia): 'Mini Mental State Examination' (MMSE), 'Fuld Object Memory Evaluation' (FOME), 'Informant Questionnaire on Cognitive Decline in the Elderly' (IQCODE), 'Bayer Activities of Daily Living Scale' (B-ADL) and clinical interviews. The data obtained were submitted to bivariate and logistic regression analysis. A sample of 1.145 elderly persons was evaluated, with a mean age of 70.9 years (60-100; DP: 7.7); 63.4% were female, and 52.8% had up to 4 years of schooling. CFI prevalence was 18.9% (n = 217). Following logistic regression analysis, higher age, low education, stroke, epilepsy and depression were associated with CFI. Female sex, widowhood, low social class and head trauma were associated with CFI only on bivariate analysis. CFI prevalence results were similar to those found by studies in Brazil, Puerto Rico and Malaysia. Cognitive and functional impairment is a rather heterogeneous condition which may be associated with various clinical conditions found in the elderly population. Due to its high prevalence and association with higher mortality and disability rates, this clinical syndrome should receive more attention on public health intervention planning.

Can You Ride a Bicycle? The Ability to Ride a Bicycle Prevents Reduced Social Function in Older Adults With Mobility Limitation

PubMed Central

Sakurai, Ryota; Kawai, Hisashi; Yoshida, Hideyo; Fukaya, Taro; Suzuki, Hiroyuki; Kim, Hunkyung; Hirano, Hirohiko; Ihara, Kazushige; Obuchi, Shuichi; Fujiwara, Yoshinori

2016-01-01

Background The health benefits of bicycling in older adults with mobility limitation (ML) are unclear. We investigated ML and functional capacity of older cyclists by evaluating their instrumental activities of daily living (IADL), intellectual activity, and social function. Methods On the basis of interviews, 614 community-dwelling older adults (after excluding 63 participants who never cycled) were classified as cyclists with ML, cyclists without ML, non-cyclists with ML (who ceased bicycling due to physical difficulties), or non-cyclists without ML (who ceased bicycling for other reasons). A cyclist was defined as a person who cycled at least a few times per month, and ML was defined as difficulty walking 1 km or climbing stairs without using a handrail. Functional capacity and physical ability were evaluated by standardized tests. Results Regular cycling was documented in 399 participants, and 74 of them (18.5%) had ML; among non-cyclists, 49 had ML, and 166 did not. Logistic regression analysis for evaluating the relationship between bicycling and functional capacity revealed that non-cyclists with ML were more likely to have reduced IADL and social function compared to cyclists with ML. However, logistic regression analysis also revealed that the risk of bicycle-related falls was significantly associated with ML among older cyclists. Conclusions The ability and opportunity to bicycle may prevent reduced IADL and social function in older adults with ML, although older adults with ML have a higher risk of falls during bicycling. It is important to develop a safe environment for bicycling for older adults. PMID:26902165

Proposed Nomogram Predicting the Individual Risk of Malignancy in the Patients With Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas.

PubMed

Jang, Jin-Young; Park, Taesung; Lee, Selyeong; Kim, Yongkang; Lee, Seung Yeoun; Kim, Sun-Whe; Kim, Song-Cheol; Song, Ki-Byung; Yamamoto, Masakazu; Hatori, Takashi; Hirono, Seiko; Satoi, Sohei; Fujii, Tsutomu; Hirano, Satoshi; Hashimoto, Yasushi; Shimizu, Yashuhiro; Choi, Dong Wook; Choi, Seong Ho; Heo, Jin Seok; Motoi, Fuyuhiko; Matsumoto, Ippei; Lee, Woo Jung; Kang, Chang Moo; Han, Ho-Seong; Yoon, Yoo-Seok; Sho, Masayuki; Nagano, Hiroaki; Honda, Goro; Kim, Sang Geol; Yu, Hee Chul; Chung, Jun Chul; Nagakawa, Yuichi; Seo, Hyung Il; Yamaue, Hiroki

2017-12-01

This study evaluated individual risks of malignancy and proposed a nomogram for predicting malignancy of branch duct type intraductal papillary mucinous neoplasms (BD-IPMNs) using the large database for IPMN. Although consensus guidelines list several malignancy predicting factors in patients with BD-IPMN, those variables have different predictability and individual quantitative prediction of malignancy risk is limited. Clinicopathological factors predictive of malignancy were retrospectively analyzed in 2525 patients with biopsy proven BD-IPMN at 22 tertiary hospitals in Korea and Japan. The patients with main duct dilatation >10 mm and inaccurate information were excluded. The study cohort consisted of 2258 patients. Malignant IPMNs were defined as those with high grade dysplasia and associated invasive carcinoma. Of 2258 patients, 986 (43.7%) had low, 443 (19.6%) had intermediate, 398 (17.6%) had high grade dysplasia, and 431 (19.1%) had invasive carcinoma. To construct and validate the nomogram, patients were randomly allocated into training and validation sets, with fixed ratios of benign and malignant lesions. Multiple logistic regression analysis resulted in five variables (cyst size, duct dilatation, mural nodule, serum CA19-9, and CEA) being selected to construct the nomogram. In the validation set, this nomogram showed excellent discrimination power through a 1000 times bootstrapped calibration test. A nomogram predicting malignancy in patients with BD-IPMN was constructed using a logistic regression model. This nomogram may be useful in identifying patients at risk of malignancy and for selecting optimal treatment methods. The nomogram is freely available at http://statgen.snu.ac.kr/software/nomogramIPMN.

The Discriminatory Ability of the Fibromyalgia Rapid Screening Tool (FiRST): An International Study in Spain and Four Latin American Countries.

PubMed

Collado, Antonio; Torres, Xavier; Messina, Osvaldo D; Vidal, Luis F; Clark, Patricia; Ríos, Carlos; Solé, Emília; Arias, Anna; Perrot, Serge; Salomon, Patricia A

2016-05-01

To assess the transcultural equivalency of the Spanish version of the Fibromyalgia Rapid Screening Tool (FiRST) and its discriminatory ability in different Latin American samples. Validation study. Departments of Rheumatology in general hospitals and private centers; fibromyalgia unit in a university hospital. 350 chronic pain patients from Spain, Argentina, Mexico, Peru, and Ecuador. The cultural relevance of the Spanish version of the FiRST was evaluated. The ability of the FiRST as a screening tool for fibromyalgia was assessed by logistic regression analysis. To determine the degree to which potential confounders, such as differences in demographics, pain, affective distress, catastrophizing, and disability, might affect the discriminatory ability, the tool was reassessed by hierarchical multivariate logistic regression. Slightly different versions of the FiRST were recommended for use in each Latin American subsample. The FiRST showed acceptable criterion validity and was able to discriminate between fibromyalgia and non-fibromyalgia patients even after controlling for the effect of potential confounders. However, low specificities were observed in samples from Spain and Mexico. The Spanish version of the FiRST may be used as a screening tool for fibromyalgia in several Latin American subsamples, even in those patients with high scores on potential confounders. In Spain and Mexico, the low specificity of the FiRST suggests, however, that it would be best used to support a suspected diagnosis of fibromyalgia, rather than to exclude the diagnosis. © 2015 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Subclinical hypothyroidism and diabetes as risk factors for postoperative stiff shoulder.

PubMed

Blonna, Davide; Fissore, Francesca; Bellato, Enrico; La Malfa, Marco; Calò, Michel; Bonasia, Davide Edoardo; Rossi, Roberto; Castoldi, Filippo

2017-07-01

Postoperative stiffness can be a disabling condition after arthroscopic shoulder surgery. The purpose of this study was to analyse the potential contribution of subclinical forms of hypothyroidism and diabetes in the development of postoperative shoulder stiffness. A prospective study was conducted on 65 consecutive patients scheduled for arthroscopic subacromial decompression or rotator cuff tear repair. Patients with preoperative stiffness were excluded. Preoperative measurements of free thyroxine, free triiodothyronine, thyroid-stimulating hormone and fasting glycaemia were taken in all patients to detect subclinical forms of diabetes and hypothyroidism. A follow-up was planned at 30, 60, 90 and 180 days after surgery. According to range of motion measurements, postoperative stiffness was classified as severe or moderate at follow-up. Univariate and logistic regression analyses were performed for the assessment of risk factors for stiffness. The overall incidence of postoperative stiffness was 29 % (19/65) in our cohort. Considering only the arthroscopic rotator cuff repairs, this incidence was 23 % (7/31). A new diagnosis of subclinical forms of diabetes or hypothyroidism was made in five cases. All five of these cases developed postoperative stiffness. The logistic regression analysis demonstrated that hypothyroidism was a risk factor for severe stiffness (RR = 25; p = 0.001) and that diabetes was a risk factor for moderate stiffness (RR = 5.7; p = 0.03). The postoperative stiffness in the majority of patients can be predicted by a careful analysis of past medical history and by detecting subclinical forms of hypothyroidism and diabetes. Prognostic study, Level II.

Do Mixed-Flora Preoperative Urine Cultures Matter?

PubMed

Polin, Michael R; Kawasaki, Amie; Amundsen, Cindy L; Weidner, Alison C; Siddiqui, Nazema Y

2017-06-01

To determine whether mixed-flora preoperative urine cultures, as compared with no-growth preoperative urine cultures, are associated with a higher prevalence of postoperative urinary tract infections (UTIs). This was a retrospective cohort study. Women who underwent urogynecologic surgery were included if their preoperative clean-catch urine culture result was mixed flora or no growth. Women were excluded if they received postoperative antibiotics for reasons other than treatment of a UTI. Women were divided into two cohorts based on preoperative urine culture results-mixed flora or no growth; the prevalence of postoperative UTI was compared between cohorts. Baseline characteristics were compared using χ 2 or Student t tests. A logistic regression analysis then was performed. We included 282 women who were predominantly postmenopausal, white, and overweight. There were many concomitant procedures; 46% underwent a midurethral sling procedure and 68% underwent pelvic organ prolapse surgery. Preoperative urine cultures resulted as mixed flora in 192 (68%) and no growth in 90 (32%) patients. Overall, 14% were treated for a UTI postoperatively. There was no difference in the proportion of patients treated for a postoperative UTI between the two cohorts (25 mixed flora vs 13 no growth, P = 0.77). These results remained when controlling for potentially confounding variables in a logistic regression model (adjusted odds ratio 0.92, 95% confidence interval 0.43-1.96). In women with mixed-flora compared with no-growth preoperative urine cultures, there were no differences in the prevalence of postoperative UTI. The clinical practice of interpreting mixed-flora cultures as negative is appropriate.

Epidemiology of Mild Traumatic Brain Injury with Intracranial Hemorrhage: Focusing Predictive Models for Neurosurgical Intervention.

PubMed

Orlando, Alessandro; Levy, A Stewart; Carrick, Matthew M; Tanner, Allen; Mains, Charles W; Bar-Or, David

2017-11-01

To outline differences in neurosurgical intervention (NI) rates between intracranial hemorrhage (ICH) types in mild traumatic brain injuries and help identify which ICH types are most likely to benefit from creation of predictive models for NI. A multicenter retrospective study of adult patients spanning 3 years at 4 U.S. trauma centers was performed. Patients were included if they presented with mild traumatic brain injury (Glasgow Coma Scale score 13-15) with head CT scan positive for ICH. Patients were excluded for skull fractures, "unspecified hemorrhage," or coagulopathy. Primary outcome was NI. Stepwise multivariable logistic regression models were built to analyze the independent association between ICH variables and outcome measures. The study comprised 1876 patients. NI rate was 6.7%. There was a significant difference in rate of NI by ICH type. Subdural hematomas had the highest rate of NI (15.5%) and accounted for 78% of all NIs. Isolated subarachnoid hemorrhages had the lowest, nonzero, NI rate (0.19%). Logistic regression models identified ICH type as the most influential independent variable when examining NI. A model predicting NI for isolated subarachnoid hemorrhages would require 26,928 patients, but a model predicting NI for isolated subdural hematomas would require only 328 patients. This study highlighted disparate NI rates among ICH types in patients with mild traumatic brain injury and identified mild, isolated subdural hematomas as most appropriate for construction of predictive NI models. Increased health care efficiency will be driven by accurate understanding of risk, which can come only from accurate predictive models. Copyright © 2017 Elsevier Inc. All rights reserved.

A Primer on Logistic Regression.

ERIC Educational Resources Information Center

Woldbeck, Tanya

This paper introduces logistic regression as a viable alternative when the researcher is faced with variables that are not continuous. If one is to use simple regression, the dependent variable must be measured on a continuous scale. In the behavioral sciences, it may not always be appropriate or possible to have a measured dependent variable on a…

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…

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.

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.

Prevalence of intellectual dysfunctioning and its correlates in a community-residing elderly population.

PubMed

Nakanishi, N; Tatara, K; Shinsho, F; Takatorige, T; Murakami, S; Fukuda, H

1998-09-01

To examine the prevalence of intellectual dysfunctioning and its correlates in community-residing elderly people, a randomly selected sample of 1,405 people aged 65 and over living in Settsu, Osaka, were investigated in October 1992. Data for assessing intellectual dysfunctioning were obtained from 1,364 people (97.1%), excluding 21 clinically demented people (1.5%); 17.6/100, 5.6/100, and 3.3/100 of the population showed minor, moderate, and appreciable intellectual dysfunctioning, respectively, and the prevalence of intellectual dysfunctioning increased with age. By multivariate analyses using logistic regression, age over 75, poor general health, including current medical treatment, and psychosocial conditions such as no participation in social activities, no life worth living (no Ikigai), and anxiety about the future were independent risk factors for intellectual dysfunctioning. We conclude that intellectual dysfunctioning is closely associated with health and psychosocial conditions.

Cannabis use and cancer of the head and neck: case-control study.

PubMed

Aldington, Sarah; Harwood, Matire; Cox, Brian; Weatherall, Mark; Beckert, Lutz; Hansell, Anna; Pritchard, Alison; Robinson, Geoffrey; Beasley, Richard

2008-03-01

To investigate whether cannabis smoking increases the risk of head and neck cancer. Case-control study. Cases of head and neck cancer < or =55 years identified from hospital databases and the Cancer Registry, and controls randomly selected from the electoral roll completed interviewer-administered questionnaires. Logistic regression was used to estimate the relative risk of head and neck cancer. There were 75 cases and 319 controls. An increased risk of cancer was found with increasing tobacco use, alcohol consumption, and decreased income but not increasing cannabis use. The highest tertile of cannabis use (>8.3 joint years) was associated with a nonsignificant increased risk of cancer (relative risk = 1.6, 95% confidence interval, 0.5-5.2) after adjustment for confounding variables. Cannabis use did not increase the risk of head and neck cancer; however, because of the limited power and duration of use studied, a small or longer-term effect cannot be excluded.

Habitat interaction between two species of chipmunk in the Basin and Range Province of Nevada

USGS Publications Warehouse

Lowrey, Christopher; Longshore, Kathleen M.

2013-01-01

Interspecies interactions can affect how species are distributed, put constraints on habitat expansion, and reduce the fundamental niche of the affected species. Using logistic regression, we analyzed and compared 174 Tamias palmeri and 94 Tamias panamintinus within an isolated mountain range of the Basin and Range Province of southern Nevada. Tamias panamintinus was more likely to use pinyon/ponderosa/fir mixed forests than pinyon alone, compared to random sites. In the presence of T palmeri, however, interaction analyses indicated T. panamintinus was less likely to occupy the mixed forests and more likely near large rocks on southern aspects. This specie s-by-habitat interaction data suggest that T. palmeri excludes T panamintinus from areas of potentially suitable habitat. Climate change may adversely affect species of restricted distribution. Habitat isolation and species interactions in this region may thus increase survival risks as climate temperatures rise.

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

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 ...

Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population.

PubMed

Yip, T C-F; Ma, A J; Wong, V W-S; Tse, Y-K; Chan, H L-Y; Yuen, P-C; Wong, G L-H

2017-08-01

Non-alcoholic fatty liver disease (NAFLD) affects 20%-40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large-scale epidemiological studies, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. To develop and validate a laboratory parameter-based machine learning model to detect NAFLD for the general population. We randomly divided 922 subjects from a population screening study into training and validation groups; NAFLD was diagnosed by proton-magnetic resonance spectroscopy. On the basis of machine learning from 23 routine clinical and laboratory parameters after elastic net regulation, we evaluated the logistic regression, ridge regression, AdaBoost and decision tree models. The areas under receiver-operating characteristic curve (AUROC) of models in validation group were compared. Six predictors including alanine aminotransferase, high-density lipoprotein cholesterol, triglyceride, haemoglobin A 1c , white blood cell count and the presence of hypertension were selected. The NAFLD ridge score achieved AUROC of 0.87 (95% CI 0.83-0.90) and 0.88 (0.84-0.91) in the training and validation groups respectively. Using dual cut-offs of 0.24 and 0.44, NAFLD ridge score achieved 92% (86%-96%) sensitivity and 90% (86%-93%) specificity with corresponding negative and positive predictive values of 96% (91%-98%) and 69% (59%-78%), and 87% of overall accuracy among 70% of classifiable subjects in the validation group; 30% of subjects remained indeterminate. NAFLD ridge score is a simple and robust reference comparable to existing NAFLD scores to exclude NAFLD patients in epidemiological studies. © 2017 John Wiley & Sons Ltd.

Insulin Resistance and Metabolic Syndrome: Clinical and Laboratory Associations in African Americans Without Diabetes in the Hemochromatosis and Iron Overload Screening Study.

PubMed

Barton, James C; Barton, Jackson Clayborn; Acton, Ronald T

2018-05-31

We sought to determine associations with insulin resistance (IR) and metabolic syndrome (MetS) in African Americans. We studied African American adults without diabetes in a postscreening examination. Participants included Cases: transferrin saturation (TS) >50% and serum ferritin (SF) >300 μg/L (M), and TS >45% and SF >200 μg/L (F), regardless of HFE genotype; and Controls: TS/SF 25th to 75th percentiles and HFE wt/wt (wild type). We excluded participants with fasting <8 h; fasting glucose >126 mg/dL; hepatitis B or C; cirrhosis; pregnancy; or incomplete datasets. We analyzed age; sex; Case/Control; body mass index (BMI); systolic and diastolic blood pressures; neutrophils; lymphocytes; alanine aminotransferase; aspartate aminotransferase; elevated C-reactive protein (CRP >0.5 mg/L); TS; and SF. We computed homeostasis model assessment of insulin resistance (HOMA-IR) using fasting serum glucose and insulin, and defined IR as HOMA-IR fourth quartile (≥2.42). There were 312 Cases and 86 Controls (56.3% men). Ninety-one percent had HFE wt/wt. None had HFE p.C282Y. A significant increasing trend across HOMA-IR quartiles was observed for BMI only. Multivariable regression on HOMA-IR revealed significant positive associations: age; BMI; lymphocytes; SF; and CRP >0.5 mg/L; and significant negative associations: neutrophils and TS. Logistic regression on IR revealed BMI [odds ratio (OR) 1.3 (95% confidence interval 1.2-1.4)] and CRP >0.5 mg/L [OR 2.7 (1.2-6.3)]. Fourteen participants (3.5%) had MetS. Logistic regression on MetS revealed one association: IR [OR 7.4 (2.1-25.2)]. In African Americans without diabetes, IR was associated with BMI and CRP >0.5 mg/L, after adjustment for other variables. MetS was associated with IR alone.

Neutropenia is independently associated with sub-therapeutic serum concentration of vancomycin.

PubMed

Choi, Min Hyuk; Choe, Yeon Hwa; Lee, Sang-Guk; Jeong, Seok Hoon; Kim, Jeong-Ho

2017-02-01

We aimed to identify the impact of the presence of neutropenia on serum vancomycin concentration (SVC). A retrospective study was conducted from January 2005 to December 2015. The study population was comprised of adult patients who were performed serum concentration of vancomycin. Patients with renal failure or using non-conventional dosages of vancomycin were excluded. A total of 1307 adult patients were included in this study, of whom 163 (12.4%) were neutropenic. Patients with neutropenia presented significantly lower SVCs than non-neutropenic patients (P<0.0001). Multiple linear regressions showed significant association between neutropenia and trough SVC (beta coefficients, -2.351; P=0.004). Multiple logistic regression analysis also revealed a significant association between sub-therapeutic vancomycin concentrations (trough SVC values<10mg/l) and neutropenia (odds ratio, 1.75, P=0.029) CONCLUSIONS: The presence of neutropenia is significantly associated with low SVC, even after adjusting for other variables. Therefore, neutropenic patients had a higher risk of sub-therapeutic SVC compared with non-neutropenic patients. We recommended that vancomycin therapy should be monitored with TDM-guided optimization of dosage and intervals, especially in neutropenic patients. Copyright © 2016 Elsevier B.V. All rights reserved.

MULTIPLE NONSPECIFIC SITES OF JOINT PAIN OUTSIDE THE KNEES DEVELOP IN PERSONS WITH KNEE PAIN

PubMed Central

Felson, David T.; Niu, Jingbo; Quinn, Emily K; Neogi, Tuhina; Lewis, Cara; Lewis, Cora E.; Law, Laura Frey; McCulloch, Chuck; Nevitt, Michael; LaValley, Michael

2017-01-01

Objective Many persons with knee pain have joint pain outside the knee but despite the impact and high frequency of this pain, its distribution and causes have not been studied. Those studying gait abnormalities have suggested that knee pain causes pain in adjacent joints but pain adaptation strategies are highly individualized. Methods We studied persons age 50-79 years with or at high risk of knee osteoarthritis drawn from two community-based cohorts, the Multicenter Osteoarthritis Study and the Osteoarthritis Initiative and followed for 5-7 years. We excluded those with knee pain at baseline and compared those who developed and did not develop knee pain at the first follow-up examination (the index visit). We examined pain on most days at joint regions outside the knee in examinations after the index visit. Logistic regression analyses examined the risk of joint specific pain adjusted for age, sex, BMI, depression with sensitivity analyses excluding those with widespread pain. Results In the combined cohorts, there were 693 persons with index visit knee pain vs. 2793 without it. 79.6% of those with bilateral and 63.8% of those with unilateral knee pain had pain during follow-up in a joint region outside the knee vs. 49.9% of those without knee pain. An increased risk of pain was present in most extremity joint sites without a predilection for specific sites. Results were unchanged when those with widespread pain were excluded. Conclusions Persons with chronic knee pain are at increased risk of pain in multiple joints in no specific pattern. PMID:27589036

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,…

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.

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.

Clinical and cytological features predictive of malignancy in thyroid follicular neoplasms.

PubMed

Lubitz, Carrie C; Faquin, William C; Yang, Jingyun; Mekel, Michal; Gaz, Randall D; Parangi, Sareh; Randolph, Gregory W; Hodin, Richard A; Stephen, Antonia E

2010-01-01

The preoperative diagnosis of malignancy in nodules suspicious for a follicular neoplasm remains challenging. A number of clinical and cytological parameters have been previously studied; however, none have significantly impacted clinical practice. The aim of this study was to determine predictive characteristics of follicular neoplasms useful for clinical application. Four clinical (age, sex, nodule size, solitary nodule) and 17 cytological variables were retrospectively reviewed for 144 patients with a nodule suspicious for follicular neoplasm, diagnosed preoperatively by fine-needle aspiration (FNA), from a single institution over a 2-year period (January 2006 to December 2007). The FNAs were examined by a single, blinded pathologist and compared with final surgical pathology. Significance of clinical and cytological variables was determined by univariate analysis and backward stepwise logistic regression. Odds ratios (ORs) for malignancy, a receiver operating characteristic curve, and predicted probabilities of combined features were determined. There was an 11% incidence of malignancy (16/144). On univariate analysis, nodule size >OR=4.0 cm nears significance (p = 0.054) and 9 of 17 cytological features examined were significantly associated with malignancy. Three variables stay in the final model after performing backward stepwise selection in logistic regression: nodule size (OR = 0.25, p = 0.05), presence of a transgressing vessel (OR = 23, p < 0.0001), and nuclear grooves (OR = 4.3, p = 0.03). The predicted probability of malignancy was 88.4% with the presence of all three variables on preoperative FNA. When the two papillary carcinomas were excluded from the analysis, the presence of nuclear grooves was no longer significant, and anisokaryosis (OR = 12.74, p = 0.005) and presence of nucleolus (OR = 0.11, p = 0.04) were significantly associated with malignancy. Excluding the two papillary thyroid carcinomas, a nodule size >or=4 cm, with a transgressing vessel and anisokaryosis and lacking a nucleolus, has a predicted probability of malignancy of 96.5%. A combination of larger nodule size, transgressing vessels, and specific nuclear features are predictive of malignancy in patients with follicular neoplasms. These findings enhance our current limited predictive armamentarium and can be used to guide surgical decision making. Further study may result in the inclusion of these variables to the systematic evaluation of follicular neoplasms.

The association between maternal antioxidant levels in midpregnancy and preeclampsia.

PubMed

Cohen, Jacqueline M; Kramer, Michael S; Platt, Robert W; Basso, Olga; Evans, Rhobert W; Kahn, Susan R

2015-11-01

We sought to determine whether midpregnancy antioxidant levels are associated with preeclampsia, overall and by timing of onset. We carried out a case-control study, nested within a cohort of 5337 pregnant women in Montreal, Quebec, Canada. Blood samples obtained at 24-26 weeks were assayed for nonenzymatic antioxidant levels among cases of preeclampsia (n = 111) and unaffected controls (n = 441). We excluded women diagnosed with gestational hypertension only. We used logistic regression with the z-score of each antioxidant level as the main predictor variable for preeclampsia risk. We further stratified early-onset (<34 weeks) and late-onset preeclampsia and carried out multinomial logistic regression. Finally, we assessed associations between antioxidant biomarkers and timing of onset (in weeks) by Cox regression, with appropriate selection weights. We summed levels of correlated biomarkers (r(2) > 0.3) and log-transformed positively skewed distributions. We adjusted for body mass index, nulliparity, preexisting diabetes, hypertension, smoking, and proxies for ethnicity and socioeconomic status. The odds ratios for α-tocopherol, α-tocopherol:cholesterol, lycopene, lutein, and carotenoids (sum of α-carotene, β-carotene, anhydrolutein, α-cryptoxanthin, and β-cryptoxanthin) suggested an inverse association between antioxidant levels and overall preeclampsia risk; however, only lutein was significantly associated with overall preeclampsia in adjusted models (odds ratio, 0.60; 95% confidence interval, 0.46-0.77) per SD. In multinomial logistic models, the relative risk ratio (RRR) estimates for the early-onset subgroup were farther from the null than those for the late-onset subgroup. The ratio of α-tocopherol to cholesterol and retinol were significantly associated with early- but not late-onset preeclampsia: RRRs (95% confidence intervals) for early-onset preeclampsia 0.67 (0.46-0.99) and 1.61 (1.12-2.33), respectively. Lutein was significantly associated with both early- and late-onset subtypes in adjusted models; RRRs 0.53 (0.35-0.80) and 0.62 (0.47-0.82), respectively. Survival analyses confirmed these trends. Most antioxidants were more strongly associated with early-onset preeclampsia, suggesting that oxidative stress may play a greater role in the pathophysiology of early-onset preeclampsia. Alternatively, reverse causality may explain this pattern. Lutein was associated with both early- and late-onset preeclampsia and may be a promising nutrient to consider in preeclampsia prevention trials, if this finding is corroborated. Copyright © 2015 Elsevier Inc. All rights reserved.

Is parenting style a predictor of suicide attempts in a representative sample of adolescents?

PubMed Central

2014-01-01

Background Suicidal ideation and suicide attempts are serious but not rare conditions in adolescents. However, there are several research and practical suicide-prevention initiatives that discuss the possibility of preventing serious self-harm. Profound knowledge about risk and protective factors is therefore necessary. The aim of this study is a) to clarify the role of parenting behavior and parenting styles in adolescents’ suicide attempts and b) to identify other statistically significant and clinically relevant risk and protective factors for suicide attempts in a representative sample of German adolescents. Methods In the years 2007/2008, a representative written survey of N = 44,610 students in the 9th grade of different school types in Germany was conducted. In this survey, the lifetime prevalence of suicide attempts was investigated as well as potential predictors including parenting behavior. A three-step statistical analysis was carried out: I) As basic model, the association between parenting and suicide attempts was explored via binary logistic regression controlled for age and sex. II) The predictive values of 13 additional potential risk/protective factors were analyzed with single binary logistic regression analyses for each predictor alone. Non-significant predictors were excluded in Step III. III) In a multivariate binary logistic regression analysis, all significant predictor variables from Step II and the parenting styles were included after testing for multicollinearity. Results Three parental variables showed a relevant association with suicide attempts in adolescents – (all protective): mother’s warmth and father’s warmth in childhood and mother’s control in adolescence (Step I). In the full model (Step III), Authoritative parenting (protective: OR: .79) and Rejecting-Neglecting parenting (risk: OR: 1.63) were identified as significant predictors (p < .001) for suicidal attempts. Seven further variables were interpreted to be statistically significant and clinically relevant: ADHD, female sex, smoking, Binge Drinking, absenteeism/truancy, migration background, and parental separation events. Conclusions Parenting style does matter. While children of Authoritative parents profit, children of Rejecting-Neglecting parents are put at risk – as we were able to show for suicide attempts in adolescence. Some of the identified risk factors contribute new knowledge and potential areas of intervention for special groups such as migrants or children diagnosed with ADHD. PMID:24766881

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.

Vancomycin-associated acute kidney injury: A cross-sectional study from a single center in China

PubMed Central

Ma, Lingyun; Xiang, Qian; Li, Xueying; Li, Haixia; Zhou, Ying; Yang, Li; Cui, Yimin

2017-01-01

Objective The objective of this study was to investigate the current situation of vancomycin (VAN)-associated acute kidney injury (VA-AKI) in China and identify the risk factors for VA-AKI, as well as to comprehensively examine the risk related to concurrent drug use. Further, we assessed the outcomes of patients who developed VA-AKI and the risk factors for these outcomes. Finally, we aimed to provide suggestions for improving the prevention and treatment of VA-AKI in China. Methods We conducted a retrospective observational study of inpatients who had been treated with VAN between January 2013 and December 2013 at Peking University First Hospital. AKI was defined as an increase in SCr of ≥0.3 mg/dl (≥26.5 μmol/l) within 48 hours or an increase to ≥1.5 times the baseline certainly or presumably within the past 7 days. VA-AKI was defined as the development of AKI during VAN therapy or within 7 days following the termination of VAN therapy. In addition, we compared patients with NO-AKI, who did not develop AKI during their hospitalization, with those with VA-AKI. Results Of the 934 patients treated with VAN during their hospital stay, 740 were included in this study. Among those excluded, 38.1% (74/194) were excluded because of a lack of data on serum creatinine (SCr). Among the included patients, 120 had confirmed VA-AKI, with an incidence of 16.2% (120/740). Multiple logistic regression analysis revealed that an elevated baseline estimated glomerular filtration rate (eGFR) (odds ratio [OR] = 1.009; p = 0.017) and concomitant vasopressor therapy (OR = 2.942; p = 0.009), nitrate use (OR = 2.869; p = 0.007), imipenem-cilastatin treatment (OR = 4.708; p = 0.000), and contrast medium administration (OR = 6.609 p = 0.005) were independent risk factors for VA-AKI; in addition, the receipt of orthopedic/trauma/burn surgery (OR = 0.3575; p = 0.011) and concomitant compound glycyrrhizin use (OR = 0.290; p = 0.017) were independent protective factors for VA-AKI. Multiple logistic regression analysis also demonstrated that among the patients who developed VA-AKI, coronary heart disease (CHD) (OR = 12.6; p = 0.006) and concomitant vasopressor therapy (OR = 15.4; p = 0.001) were independent risk factors for death. We also evaluated the factors influencing improvement of renal function. Multiple logistic regression analysis demonstrated that CHD (OR = 8.858, p = 0.019) and concomitant contrast medium administration (OR = 9.779, p = 0.005) were independent risk factors and that simultaneous β-blocker treatment (OR = 0.124, p = 0.001) was an independent protective factor for improvement of renal function. Conclusion Patients treated with VAN received insufficient monitoring of SCr and inadequate therapeutic drug monitoring. We recommend that hospitals increase their investment in clinical pharmacists. An elevated baseline eGFR and concomitant vasopressor therapy, nitrate use, imipenem-cilastatin treatment, and contrast medium administration were independent risk factors for VA-AKI; in addition, orthopedic/trauma/burn surgery and concomitant compound glycyrrhizin use were independent protective factors for VA-AKI. PMID:28426688

Vancomycin-associated acute kidney injury: A cross-sectional study from a single center in China.

PubMed

Pan, Kunming; Ma, Lingyun; Xiang, Qian; Li, Xueying; Li, Haixia; Zhou, Ying; Yang, Li; Cui, Yimin

2017-01-01

The objective of this study was to investigate the current situation of vancomycin (VAN)-associated acute kidney injury (VA-AKI) in China and identify the risk factors for VA-AKI, as well as to comprehensively examine the risk related to concurrent drug use. Further, we assessed the outcomes of patients who developed VA-AKI and the risk factors for these outcomes. Finally, we aimed to provide suggestions for improving the prevention and treatment of VA-AKI in China. We conducted a retrospective observational study of inpatients who had been treated with VAN between January 2013 and December 2013 at Peking University First Hospital. AKI was defined as an increase in SCr of ≥0.3 mg/dl (≥26.5 μmol/l) within 48 hours or an increase to ≥1.5 times the baseline certainly or presumably within the past 7 days. VA-AKI was defined as the development of AKI during VAN therapy or within 7 days following the termination of VAN therapy. In addition, we compared patients with NO-AKI, who did not develop AKI during their hospitalization, with those with VA-AKI. Of the 934 patients treated with VAN during their hospital stay, 740 were included in this study. Among those excluded, 38.1% (74/194) were excluded because of a lack of data on serum creatinine (SCr). Among the included patients, 120 had confirmed VA-AKI, with an incidence of 16.2% (120/740). Multiple logistic regression analysis revealed that an elevated baseline estimated glomerular filtration rate (eGFR) (odds ratio [OR] = 1.009; p = 0.017) and concomitant vasopressor therapy (OR = 2.942; p = 0.009), nitrate use (OR = 2.869; p = 0.007), imipenem-cilastatin treatment (OR = 4.708; p = 0.000), and contrast medium administration (OR = 6.609 p = 0.005) were independent risk factors for VA-AKI; in addition, the receipt of orthopedic/trauma/burn surgery (OR = 0.3575; p = 0.011) and concomitant compound glycyrrhizin use (OR = 0.290; p = 0.017) were independent protective factors for VA-AKI. Multiple logistic regression analysis also demonstrated that among the patients who developed VA-AKI, coronary heart disease (CHD) (OR = 12.6; p = 0.006) and concomitant vasopressor therapy (OR = 15.4; p = 0.001) were independent risk factors for death. We also evaluated the factors influencing improvement of renal function. Multiple logistic regression analysis demonstrated that CHD (OR = 8.858, p = 0.019) and concomitant contrast medium administration (OR = 9.779, p = 0.005) were independent risk factors and that simultaneous β-blocker treatment (OR = 0.124, p = 0.001) was an independent protective factor for improvement of renal function. Patients treated with VAN received insufficient monitoring of SCr and inadequate therapeutic drug monitoring. We recommend that hospitals increase their investment in clinical pharmacists. An elevated baseline eGFR and concomitant vasopressor therapy, nitrate use, imipenem-cilastatin treatment, and contrast medium administration were independent risk factors for VA-AKI; in addition, orthopedic/trauma/burn surgery and concomitant compound glycyrrhizin use were independent protective factors for VA-AKI.

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.

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.

A comparative study on entrepreneurial attitudes modeled with logistic regression and Bayes nets.

PubMed

López Puga, Jorge; García García, Juan

2012-11-01

Entrepreneurship research is receiving increasing attention in our context, as entrepreneurs are key social agents involved in economic development. We compare the success of the dichotomic logistic regression model and the Bayes simple classifier to predict entrepreneurship, after manipulating the percentage of missing data and the level of categorization in predictors. A sample of undergraduate university students (N = 1230) completed five scales (motivation, attitude towards business creation, obstacles, deficiencies, and training needs) and we found that each of them predicted different aspects of the tendency to business creation. Additionally, our results show that the receiver operating characteristic (ROC) curve is affected by the rate of missing data in both techniques, but logistic regression seems to be more vulnerable when faced with missing data, whereas Bayes nets underperform slightly when categorization has been manipulated. Our study sheds light on the potential entrepreneur profile and we propose to use Bayesian networks as an additional alternative to overcome the weaknesses of logistic regression when missing data are present in applied research.

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).

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis

PubMed Central

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B.; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain

2017-01-01

Abstract Background: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Results: Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Conclusions: Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. PMID:28327993

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.

PubMed

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain; Jelinsky, Scott A

2017-05-01

The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. © The Author 2017. Published by Oxford University Press.

Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.

PubMed

Lin, Chao-Cheng; Bai, Ya-Mei; Chen, Jen-Yeu; Hwang, Tzung-Jeng; Chen, Tzu-Ting; Chiu, Hung-Wen; Li, Yu-Chuan

2010-03-01

Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder (DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data for these patients were collected between March 2005 and September 2005. The input variables of ANN and logistic regression were limited to demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models. Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean +/- SD AUC was high for both the ANN and logistic regression models (0.934 +/- 0.033 vs 0.922 +/- 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models. Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients. (c) 2010 Physicians Postgraduate Press, Inc.

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

Saba, Luca, E-mail: lucasaba@tiscali.it; Sanfilippo, Roberto; Montisci, Roberto

Purpose: The purpose of this work was to determine whether it is possible to identify a reliable carotid stenosis threshold-measured in millimeters (mm)-that is associated with cerebrovascular symptoms. Methods: Written, informed consent was obtained for each patient; 149 consecutive patients (98 men; median age, 68 years) were studied for suspected pathology of the carotid arteries by using MDCTA. In each patient, carotid artery stenosis was quantified using the mm-method. Continuous data were described as the mean value {+-} standard deviation (SD), and they were compared by using the Student's t test. A ROC curve was calculated to test the studymore » hypothesis and identify a specific mm-stenosis threshold. Logistic regression analysis was performed to include other MDCTA findings, such as plaque type and ulcerations. A P value < 0.05 was considered to indicate statistical significance. Results: Twenty-six patients were excluded. Of those remaining, 75 patients suffered cerebrovascular symptoms (61%). There was a statistically significant difference (P = 0.0046) in the mm-carotid stenosis between patients with symptoms (1.31 {+-} 0.64 mm SD) and without symptoms (1.68 {+-} 0.79 mm SD). Multiple logistic regression analysis confirmed that symptoms were associated with increased luminal stenosis (P = 0.013) and with the presence of fatty plaques (P = 0.0491). Moreover, the ROC curve (Az = 0.669; {+-}0.051 SD; P = 0.0009) indicated that a threshold of 1.6 mm stenosis was associated with a sensitivity to symptoms of 76%. Conclusions: The results of our study suggest an association between luminal stenosis (measure in mm) and the presence of cerebrovascular symptoms. Luminal stenosis of 1.6 mm is associated, with a sensitivity of 76%, with cerebrovascular symptoms.« less

Relation of depression with health behaviors and social conditions of dependent community-dwelling older persons in the Republic of Chile.

PubMed

Sandoval Garrido, Felipe Alfonso; Tamiya, Nanako; Lloyd-Sherlock, Peter; Noguchi, Haruko

2016-12-01

Depressive symptoms are a leading cause of disability and emotional suffering, particularly in old age. However, evidence on depression and old age in developing countries remains largely ignored. The aim of this study was to examine the relation between health behavior and social conditions with depression among dependent community-dwelling older persons in the Republic of Chile. This is a cross-sectional and inferential study, using nationally representative secondary data. Two models used logistic regression on 640 dependent community-dwelling older persons from all over Chile, who personally answered a depression assessment, excluding those taking antidepressants. The geriatric depression scale (GDS-15) was used as outcome. The first model aims at any kind of depression (GDS 5>). The second aims at severe depression (GDS 10>). As exposure, we used the health behavior and social conditions of the older persons. Socio-demographic and physical conditions were used as adjustment. 44.5% of the older persons presented depressive symptoms. Among them, 11% had severe depression. Logistic regression showed that significant detrimental factors for being depressed in both models were visiting the doctor five times or over because of acute diseases, feeling uncomfortable with their living arrangement, and feeling discriminated. On the other hand, every additional day of physical exercise and living alone had a beneficial and detrimental effect only in model one. Analyses on ways to support older persons living alone and the promotion of physical exercise to avoid depression are needed, along with a deeper understanding of the comfort with their living arrangement. Finally, ways to address the discrimination among older persons should be further explored.

Small Vessel Disease/White Matter Disease of the Brain and Its Association With Osteoporosis

PubMed Central

Alagiakrishnan, Kannayiram; Hsueh, Jenny; Zhang, Edwin; Khan, Khurshid; Senthilselvan, Ambikaipakan

2015-01-01

Background Evidence now suggests the role of neural effect on bone mass control. The effect of small vessel disease of the brain on osteoporosis has not been studied. The aim of this study was to investigate the association of white matter disease (WMD) of the brain with osteoporosis in the elderly. Methods In this retrospective cross-sectional study, 780 consecutive patient charts between 2010 and 2011 were reviewed in the Senior’s Outpatient Clinic at the University of Alberta Hospital. Subjects with brain computerized tomography (CT) were included in the study. Subjects with incomplete information, intracranial hemorrhage, acute stroke, cerebral edema, and/or normal pressure hydrocephalus on the CT were excluded. WMD was quantified on CT using Wahlund’s scoring protocol. Osteoporosis information was obtained from the chart, which has been diagnosed based on bone mineral density (BMD) information. Logistic regression analysis was done to determine the association of WMD severity with osteoporosis after controlling for confounding vascular risk factors. Results Of the 505 subjects who were included in the study, 188 (37%) had osteoporosis and 171 (91%) of these osteoporotic subjects were females. The mean age was 79.8 ± 7.04 years. The prevalence of WMD in osteoporosis subjects was 73%. In the unadjusted logistic regression analysis, there was a significant association between WMD severity and osteoporosis (odds ratio (OR): 1.10; 95% confidence interval (CI): 1.05 - 1.14; P < 0.001) and the significance remained in the adjusted model, after correcting for age, sex and all vascular risk factors (OR: 1.11; 95% CI: 1.05 - 1.18; P < 0.001). Conclusion WMD severity of the brain was associated with osteoporosis in the elderly. PMID:25780476

Non-English speakers attend gastroenterology clinic appointments at higher rates than English speakers in a vulnerable patient population

PubMed Central

Sewell, Justin L.; Kushel, Margot B.; Inadomi, John M.; Yee, Hal F.

2009-01-01

Goals We sought to identify factors associated with gastroenterology clinic attendance in an urban safety net healthcare system. Background Missed clinic appointments reduce the efficiency and availability of healthcare, but subspecialty clinic attendance among patients with established healthcare access has not been studied. Study We performed an observational study using secondary data from administrative sources to study patients referred to, and scheduled for an appointment in, the adult gastroenterology clinic serving the safety net healthcare system of San Francisco, California. Our dependent variable was whether subjects attended or missed a scheduled appointment. Analysis included multivariable logistic regression and classification tree analysis. 1,833 patients were referred and scheduled for an appointment between 05/2005 and 08/2006. Prisoners were excluded. All patients had a primary care provider. Results 683 patients (37.3%) missed their appointment; 1,150 (62.7%) attended. Language was highly associated with attendance in the logistic regression; non-English speakers were less likely than English speakers to miss an appointment (adjusted odds ratio 0.42 [0.28,0.63] for Spanish, 0.56 [0.38,0.82] for Asian language, p < 0.001). Other factors were also associated with attendance, but classification tree analysis identified language to be the most highly associated variable. Conclusions In an urban safety net healthcare population, among patients with established healthcare access and a scheduled gastroenterology clinic appointment, not speaking English was most strongly associated with higher attendance rates. Patient related factors associated with not speaking English likely influence subspecialty clinic attendance rates, and these factors may differ from those affecting general healthcare access. PMID:19169147

Risk factors for reinsertion of urinary catheter after early removal in thoracic surgical patients.

PubMed

Young, John; Geraci, Travis; Milman, Steven; Maslow, Andrew; Jones, Richard N; Ng, Thomas

2018-03-08

To reduce the incidence of urinary tract infection, Surgical Care Improvement Project 9 mandates the removal of urinary catheters within 48 hours postoperatively. In patients with thoracic epidural anesthesia, we sought to determine the rate of catheter reinsertion, the complications of reinsertion, and the factors associated with reinsertion. We conducted a prospective observational study of consecutive patients undergoing major pulmonary or esophageal resection with thoracic epidural analgesia over a 2-year period. As per Surgical Care Improvement Project 9, all urinary catheters were removed within 48 hours postoperatively. Excluded were patients with chronic indwelling catheter, patients with urostomy, and patients requiring continued strict urine output monitoring. Multivariable logistic regression analysis was used to identify independent risk factors for urinary catheter reinsertion. Thirteen patients met exclusion criteria. Of the 275 patients evaluated, 60 (21.8%) required reinsertion of urinary catheter. There was no difference in the urinary tract infection rate between patients requiring reinsertion (1/60 [1.7%]) versus patients not requiring reinsertion (1/215 [0.5%], P = .389). Urethral trauma during reinsertion was seen in 1 of 60 patients (1.7%). After reinsertion, discharge with urinary catheter was required in 4 of 60 patients (6.7%). Multivariable logistic regression analysis found esophagectomy, lower body mass index, and benign prostatic hypertrophy to be independent risk factors associated with catheter reinsertion after early removal in the presence of thoracic epidural analgesia. When applying Surgical Care Improvement Project 9 to patients undergoing thoracic procedures with thoracic epidural analgesia, consideration to delayed removal of urinary catheter may be warranted in patients with multiple risk factors for reinsertion. Copyright © 2018 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Social capital and the miniaturization of community among daily and intermittent smokers: a population-based study.

PubMed

Lindström, Martin

2003-02-01

The impact of social participation, trust, and the miniaturization of community on daily and intermittent smoking was investigated. The 2000 public health survey in Scania is a cross-sectional study. A total of 13,715 persons answered a postal questionnaire, which represents 59% of the random sample. A logistic regression model was used to investigate the association between the social capital variables and daily and intermittent smoking. The multivariate analysis was performed by using a logistic regression model to investigate the importance of possible confounders (age, country of origin, education, and snuff consumption) on the differences in daily and intermittent smoking between high versus low social participation, trust, and their four combination categories. The differences in the prevalences of the 13 social participation subitems between the high social capital and miniaturization of community categories were compared by t tests. Daily smoking is negatively associated with both social participation and trust, while intermittent smoking is positively associated with social participation and negatively associated with trust. This latter combination, named "the miniaturization of community," is an indirect measure of the ideologically and culturally increasingly narrow forms of social participation that excludes generalised trust to other people. Study circles, meetings of organisations, theatre/cinema, arts exhibition, and gathering of relatives are more prevalent in the high social capital category, while visit(s) to night club/entertainment is more prevalent in the miniaturization of community category. Low social capital is associated with daily smoking. "The miniaturization of community," i.e., high social participation and low trust, is significantly associated with intermittent smoking. The results have direct implications for smoking prevention strategies.

The prevalence and correlates of buprenorphine inhalation amongst opioid substitution treatment (OST) clients in Australia.

PubMed

Horyniak, Danielle; Dietze, Paul; Larance, Briony; Winstock, Adam; Degenhardt, Louisa

2011-03-01

Diversion and injection of buprenorphine (Subutex(®)) and buprenorphine-naloxone (Suboxone(®)) have been well documented. Recent international research and local anecdotal evidence suggest that these medications are also used by other routes of administration, including smoking and snorting. A cross-sectional sample of 440 opioid substitution treatment (OST) clients was recruited through pharmacies and clinics in three Australian jurisdictions, and interviewed face-to-face using a structured questionnaire. Eligible participants were those aged 18 or over, who had resided in their home state for at least six months, and had been in their current treatment episode for at least 4 weeks. We compared differences in characteristics between clients who had ever inhaled (smoked or snorted) buprenorphine (including buprenorphine-naloxone) and other OST clients. Logistic regression was used to identify correlates of buprenorphine inhalation. Sixty-eight clients who had never used buprenorphine were excluded from analysis. Sixty-five clients (18%) reported having ever inhaled buprenorphine, with Subutex(®) smoking being most common, reported by 50 clients (77%). In multivariable logistic regression, those who reported ever inhaling buprenorphine were significantly more likely to: be aged 35 or younger, have ever been in prison and have ever injected buprenorphine. Clients from New South Wales and Victoria were significantly less likely to have ever inhaled buprenorphine than those from South Australia. Our data indicates that the inhalation of buprenorphine has occurred in a significant minority of Australian OST clients. The motivations, contexts and potential health consequences of buprenorphine use by these atypical routes of administration, particularly in a correctional setting, warrant further exploration. Copyright © 2010 Elsevier B.V. All rights reserved.

Operative management and outcomes in 103 AAST-OIS grades IV and V complex hepatic injuries: trauma surgeons still need to operate, but angioembolization helps.

PubMed

Asensio, Juan A; Roldán, Gustavo; Petrone, Patrizio; Rojo, Esther; Tillou, Areti; Kuncir, Eric; Demetriades, Demetrios; Velmahos, George; Murray, James; Shoemaker, William C; Berne, Thomas V; Chan, Linda

2003-04-01

American Association for the Surgery of Trauma (AAST) Organ Injury Scale (OIS) grades IV and V complex hepatic injuries are highly lethal. Our objectives were to review experience and identify predictors of outcome and to evaluate the role of angioembolization in decreasing mortality. This was a retrospective 8-year study of all patients sustaining AAST-OIS grades IV and V hepatic injuries managed operatively. Statistical analysis was performed using univariate and multivariate logistic regression. The main outcome measure was survival. The study included 103 patients, with a mean Revised Trauma Score of 5.61 +/- 2.55 and a mean Injury Severity Score of 33 +/- 9.5. Mechanism of injury was penetrating in 80 (79%) and blunt in 23 (21%). Emergency department thoracotomy was performed in 21 (25%). AAST grade IV injuries occurred in 51 (47%) and grade V injuries occurred in 52 (53%). Mean estimated blood loss was 9,414 mL. Overall survival was 43%. Adjusted overall survival rate after emergency department thoracotomy patients were excluded was 58%. Results stratified to AAST-OIS injury grade were as follows: grade IV, 32 of 51 (63%); grade V, 12 of 52 (23%); grade IV versus grade V (p < 0.001) odds ratio, 2.06; 95% confidence interval, 2.72 (1.40-3.04). Logistic regression analysis identified as independent predictors of outcome Revised Trauma Score (adjusted p < 0.0002), angioembolization (adjusted p < 0.0177), direct approach to hepatic veins (adjusted p < 0.0096), and packing (adjusted p < 0.0013). Improvements in mortality can be achieved with an appropriate operative approach. Angioembolization as an adjunct procedure decreases mortality in AAST-OIS grades IV and V hepatic injuries.

Characteristics of visiting nurse agencies with high home death rates: A prefecture-wide study in Japan.

PubMed

Kashiwagi, Masayo; Tamiya, Nanako; Murata, Masako

2015-08-01

The purpose of the present study was to identify characteristics of visiting nurse agencies (VNA) in Japan with high home death rates by a prefecture-wide survey. A cross-sectional study of visiting nurse agencies (n = 101) in Ibaraki Prefecture, Japan, was completed. Data included the basic characteristics of each VNA, the type of services provided, level of coordination with other service providers, total number of VNA patients who died per year and place of death and contractual relationship with home-care supporting clinics providing end-of-life care services in the home 24 h a day. The VNA characteristics were analyzed by logistic regression, using the home death rate per VNA as a dependent variable. A total 69 agencies, excluding those that did not report number of deaths (n = 14) and those without deaths during the year (n = 6), were analyzed. The median home death rate of the 69 VNA was 29.8%. The results of logistic regression analysis showed that higher home death rate was significantly associated with lack of attachment to a hospital, existence of a contractual relationship with home-care supporting clinics and existence of an interactive information exchange through telephone/face-to-face communication with attending physicians. In order to increase the home death rate of people using VNA, policymakers must consider establishing home-based service systems within the community that can provide home end-of-life care services 24 h a day, and support the interactive exchange of information between the visiting nurse and the attending physician. © 2014 The Authors. Geriatrics & Gerontology International published by Wiley Publishing Asia Pty Ltd on behalf of Japanese Geriatrics Society.

The relationship between body composition and femoral neck osteoporosis or osteopenia in adults with previous poliomyelitis.

PubMed

Chang, Kwang-Hwa; Tseng, Sung-Hui; Lin, Yu-Ching; Lai, Chien-Hung; Hsiao, Wen-Tien; Chen, Shih-Ching

2015-04-01

Articles in the literature describing the association between body composition and osteoporosis in subjects with poliomyelitis are scarce. To assess the relationship between body composition and femoral neck osteoporosis or osteopenia in adults with previous polio. After excluding postmenopausal women, 44 polio (mean age ± standard deviation, 46.1 ± 3.3 years) and 44 able-bodied control volunteers (47.0 ± 4.0 years) participated in the study. Each participant's femoral neck bone mineral density (FNBMD) and whole body composition were measured using dual-energy X-ray absorptiometry. With local reference BMD values of normal young adults installed in the instrument, we obtained T-score values that depended on each FNBMD value. A T-score value of ≤-1.0 indicated decreased T-score, including osteoporosis (T-score ≤ -2.5) and osteopenia (-1.0 to -2.5). This study conducted logistic regression analyses to find factors associated with osteoporosis and osteopenia. Based on the FNBMD T-score values, 60.0% of middle-aged men with polio had osteoporosis. In adjusted logistic regression analyses, total lean tissue mass (Adjusted odds ratio [95% confidence interval], 0.74 [0.56-0.99], P < 0.05) and male gender (947.16 [6.02-148,926.16], P < 0.01) were important factors associated with decreased T-score in polio group. Osteoporosis or osteopenia is a common medical problem for middle-aged men with polio. Reduced total lean tissue mass seems to be one of the important factors associated with osteoporosis or osteopenia among subjects with polio. Further research for a clinical tool to assess lean tissue mass for subjects with polio is needed. Copyright © 2015 Elsevier Inc. All rights reserved.

Cardiopulmonary resuscitation by trained responders versus lay persons and outcomes of out-of-hospital cardiac arrest: A community observational study.

PubMed

Park, Yoo Mi; Shin, Sang Do; Lee, Yu Jin; Song, Kyoung Jun; Ro, Young Sun; Ahn, Ki Ok

2017-09-01

The study aims to compare bystander processes of care (cardiopulmonary resuscitation (CPR) and defibrillation) and outcomes for witnessed presumed cardiac etiology in OHCA patients in whom initial resuscitation was provided by dedicated trained responder (TR) versus lay person (LP) bystanders. Data on witnessed and presumed cardiac OHCA in adults (15 years or older) from 2011 to 2015 in a metropolitan city with 10 million persons were collected, excluding cases in which the information on TRs, bystander CPR, defibrillation, and clinical outcomes was unknown. Exposure variables were TRs who were legally designated with CPR education and response and LPs who were bystanders who witnessed the OHCA by chance. The primary/secondary/tertiary outcomes were a good cerebral performance category (CPC) of 1 or 2, survival to discharge, and bystander defibrillation. A multivariable logistic regression analysis was used to calculate the adjusted odds ratio (AOR) with 95% confidence intervals (CIs), adjusting for potential confounders. Of 20,984 OHCA events, 6475 cases were ultimately analyzed. The TR group constituted 6.4% of the cases, and the patients showed significantly better survival and a good CPC. From the multivariable logistic regression analysis of the outcomes, by comparing the TR group with the LP group, the AOR (95% CIs) was 1.49 (1.04-2.15) for a good CPC, 1.59 (1.20-2.11) for survival to discharge, and 10.02 (7.04-14.26) for bystander defibrillation. The TR group witnessed a relatively low proportion of OHCA but was associated with better survival outcomes and good neurological recovery through higher CPR rates and defibrillation of adults older than 15 years with witnessed OHCA in a metropolitan city. Copyright © 2017 Elsevier B.V. All rights reserved.

Postoperative Copeptin Concentration Predicts Diabetes Insipidus After Pituitary Surgery.

PubMed

Winzeler, Bettina; Zweifel, Christian; Nigro, Nicole; Arici, Birsen; Bally, Martina; Schuetz, Philipp; Blum, Claudine Angela; Kelly, Christopher; Berkmann, Sven; Huber, Andreas; Gentili, Fred; Zadeh, Gelareh; Landolt, Hans; Mariani, Luigi; Müller, Beat; Christ-Crain, Mirjam

2015-06-01

Copeptin is a stable surrogate marker of vasopressin release; the peptides are stoichiometrically secreted from the neurohypophysis due to elevated plasma osmolality or nonosmotic stress. We hypothesized that following stress from pituitary surgery, patients with neurohypophyseal damage and eventual diabetes insipidus (DI) would not exhibit the expected pronounced copeptin elevation. The objective was to evaluate copeptin's accuracy to predict DI following pituitary surgery. This was a prospective multicenter observational cohort study. Three Swiss or Canadian referral centers were used. Consecutive pituitary surgery patients were included. Copeptin was measured postoperatively daily until discharge. Logistic regression models and diagnostic performance measures were calculated to assess relationships of postoperative copeptin levels and DI. Of 205 patients, 50 (24.4%) developed postoperative DI. Post-surgically, median [25th-75th percentile] copeptin levels were significantly lower in patients developing DI vs those not showing this complication: 2.9 [1.9-7.9] pmol/L vs 10.8 [5.2-30.4] pmol/L; P < .001. Logistic regression analysis revealed strong association between postoperative copeptin concentrations and DI even after considering known predisposing factors for DI: adjusted odds ratio (95% confidence interval) 1.41 (1.16-1.73). DI was seen in 22/27 patients with copeptin <2.5 pmol/L (positive predictive value, 81%; specificity, 97%), but only 1/40 with copeptin >30 pmol/L (negative predictive value, 95%; sensitivity, 94%) on postoperative day 1. Lack of standardized DI diagnostic criteria; postoperative blood samples for copeptin obtained during everyday care vs at fixed time points. In patients undergoing pituitary procedures, low copeptin levels despite surgical stress reflect postoperative DI, whereas high levels virtually exclude it. Copeptin therefore may become a novel tool for early goal-directed management of postoperative DI.

Evaluation of scoring models for identifying the need for therapeutic intervention of upper gastrointestinal bleeding: A new prediction score model for Japanese patients.

PubMed

Iino, Chikara; Mikami, Tatsuya; Igarashi, Takasato; Aihara, Tomoyuki; Ishii, Kentaro; Sakamoto, Jyuichi; Tono, Hiroshi; Fukuda, Shinsaku

2016-11-01

Multiple scoring systems have been developed to predict outcomes in patients with upper gastrointestinal bleeding. We determined how well these and a newly established scoring model predict the need for therapeutic intervention, excluding transfusion, in Japanese patients with upper gastrointestinal bleeding. We reviewed data from 212 consecutive patients with upper gastrointestinal bleeding. Patients requiring endoscopic intervention, operation, or interventional radiology were allocated to the therapeutic intervention group. Firstly, we compared areas under the curve for the Glasgow-Blatchford, Clinical Rockall, and AIMS65 scores. Secondly, the scores and factors likely associated with upper gastrointestinal bleeding were analyzed with a logistic regression analysis to form a new scoring model. Thirdly, the new model and the existing model were investigated to evaluate their usefulness. Therapeutic intervention was required in 109 patients (51.4%). The Glasgow-Blatchford score was superior to both the Clinical Rockall and AIMS65 scores for predicting therapeutic intervention need (area under the curve, 0.75 [95% confidence interval, 0.69-0.81] vs 0.53 [0.46-0.61] and 0.52 [0.44-0.60], respectively). Multivariate logistic regression analysis retained seven significant predictors in the model: systolic blood pressure <100 mmHg, syncope, hematemesis, hemoglobin <10 g/dL, blood urea nitrogen ≥22.4 mg/dL, estimated glomerular filtration rate ≤ 60 mL/min per 1.73 m 2 , and antiplatelet medication. Based on these variables, we established a new scoring model with superior discrimination to those of existing scoring systems (area under the curve, 0.85 [0.80-0.90]). We developed a superior scoring model for identifying therapeutic intervention need in Japanese patients with upper gastrointestinal bleeding. © 2016 Japan Gastroenterological Endoscopy Society.

Risk factors for early disability pension in patients with epilepsy and vocational difficulties - Data from a specialized rehabilitation unit.

PubMed

Specht, Ulrich; Coban, Ingrid; Bien, Christian G; May, Theodor W

2015-10-01

The purpose of this study was to assess the risk factors for early disability pension (EDP) in adult patients with epilepsy in a specialized epilepsy rehabilitation setting. In a retrospective study, 246 patients with epilepsy and employment difficulties leading to referral to an inpatient rehabilitation unit were evaluated with a questionnaire on admission and after a mean of 2.5years after discharge. Patients already receiving EDP at baseline were excluded. Epilepsy-related, demographic, and employment-related data as well as cognitive functioning and psychiatric comorbidity were assessed as risk factors for EDP at follow-up and analyzed using logistic regression models. Seventy-six percent of the patients had uncontrolled epilepsy, and 66.7% had psychiatric comorbidity. At follow-up, 33.7% received an EDP. According to multivariate logistic regression analysis, age>50years (odds ratio (OR) 5.44, compared to age<30years), application for an EDP prior to admission (OR 3.7), sickness absence>3months in the previous year (OR 3.30, compared to sickness absence<3months), and psychiatric comorbidity (OR 2.79) were significant risk factors for an EDP at follow-up, while epilepsy-related factors and cognitive impairment showed an effect only in the univariate analyses. Potential risk factors for EDP in patients with epilepsy were evaluated using multivariate analysis. Knowledge of such factors may help to develop appropriate criteria for rehabilitation candidacy and interventions to reduce the risk for EDP. This might lead to an amelioration of both psychosocial burden of patients and economic burden on society. Copyright © 2015 Elsevier Inc. All rights reserved.

Factors associated with increased incidence of severe toxicities following yttrium-90 resin microspheres in the treatment of hepatic malignancies.

PubMed

Roberson Ii, John D; McDonald, Andrew M; Baden, Craig J; Lin, Chee Paul; Jacob, Rojymon; Burnett Iii, Omer L

2016-03-14

To further define variables associated with increased incidences of severe toxicities following administration of yttrium-90 ((90)Y) microspheres. Fifty-eight patients undergoing 79 treatments were retrospectively assessed for development of clinical and laboratory toxicity incidence following (90)Y administration. Severe toxicity events were defined using Common Terminology Criteria for Adverse Events version 4.03 and defined as grade ≥ 3. Univariate logistic regression analyses were used to evaluate the effect of different factors on the incidence of severe toxicity events. Multicollinearity was assessed for all factors with P < 0.1 using Pearson correlation matrices. All factors not excluded due to multicollinearity were included in a multivariate logistic regression model for each measurement of severe toxicity. Severe (grade ≥ 3) toxicities occurred following 21.5% of the 79 treatments included in our analysis. The most common severe laboratory toxicities were severe alkaline phosphatase (17.7%), albumin (12.7%), and total bilirubin (10.1%) toxicities. Decreased pre-treatment albumin (OR = 26.2, P = 0.010) and increased pre-treatment international normalized ratio (INR) (OR = 17.7, P = 0.048) were associated with development of severe hepatic toxicity. Increased pre-treatment aspartate aminotransferase (AST; OR = 7.4, P = 0.025) and decreased pre-treatment hemoglobin (OR = 12.5, P = 0.025) were associated with severe albumin toxicity. Increasing pre-treatment model for end-stage liver disease (MELD) score (OR = 1.8, P = 0.033) was associated with severe total bilirubin toxicity. Colorectal adenocarcinoma histology was associated with severe alkaline phosphatase toxicity (OR = 5.4, P = 0.043). Clinicians should carefully consider pre-treatment albumin, INR, AST, hemoglobin, MELD, and colorectal histology when choosing appropriate candidates for (90)Y microsphere therapy.

Sex differences in attachment to spouses among older Japanese couples.

PubMed

Yokoyama, Katsunori; Shirakawa, Kazutoyo; Hirao, Tomohiro; Nakatsu, Morihito; Yoda, Takeshi; Suzuki, Hiromi; Okabe, Yugo; Shirakami, Gotaro

2017-05-01

Attachment among older adults can partially explain sex differences in bereavement outcomes, but there is currently little evidence regarding this. The aim was to quantify sex differences in the proportion of spouses as attachment figures among older couples. We carried out a secondary analysis of cross-sectional questionnaire survey data. The dataset included information about 5137 respondents aged 65 years or older in Kanonji and Mitoyo, two rural cities in Kagawa prefecture, Japan; those who were never married or were widowed or divorced were excluded. The questionnaire asked participants whom they most want to be close by when they die (this person was defined as an "attachment figure"), and compared the proportion of older people of each sex who named their spouse. We used multiple logistic regression analyses to examine the independent association of sex with the proportion of spouses as attachment figures. Of the 2513 male respondents, 1494 (59.5%) answered "spouse." Of the 2624 female respondents, 904 (34.5%) answered "spouse." Multiple logistic regression analyses adjusted for age, live-in children, annual income, participation in community activities, presence of a long-term primary care doctor, anxiety about death and preferences for place of death showed that men had a higher probability of attachment to spouses than women (odds ratio 2.83, 95% confidence interval 2.43-3.31). Spouses are more likely to be attachment figures for men than for women. The present study supports the theory that sex differences in attachment might partially explain the differences in the bereavement effect between sexes among older people. Geriatr Gerontol Int 2017; 17: 834-838. © 2016 Japan Geriatrics Society.

High FIB-4 index as an independent risk factor of prevalent chronic kidney disease in patients with nonalcoholic fatty liver disease.

PubMed

Xu, Huang-Wei; Hsu, Yung-Chien; Chang, Chia-Hao; Wei, Kuo-Liang; Lin, Chun-Liang

2016-03-01

Growing evidence suggests that non-alcoholic fatty liver disease (NAFLD) is linked to an increased risk for chronic kidney disease (CKD); liver fibrosis with biopsy-proven NAFLD has also been shown to associate with an increased risk of CKD. This study compares the diagnostic performance of simple noninvasive tests in identifying prevalent CKD among individuals with ultrasonography-diagnosed NAFLD. A total of 755 with ultrasonography-diagnosed NAFLD were included. Estimated glomerular filtration rate and noninvasive markers for hepatic fibrosis: aspartate transaminase to alanine transaminase ratio (AAR), aspartate transaminase to platelet ratio index (APRI), FIB-4 score, NAFLD fibrosis score (NFS) and BARD score were assessed. Binary logistic regression to generate a propensity score and receiver operating characteristic curves were developed for each of the noninvasive markers for predicting CKD, and the area under the receiver operating characteristic curve was greatest for FIB-4 score (0.750), followed by NFS (0.710), AAR (0.594), APRI (0.587), and BARD score (0.561). A cut-off value of 1.100 for FIB-4 score gave a sensitivity of 68.85% and a specificity of 71.07% for predicting CKD. The positive predictive value and negative predictive value were 37.50 and 90.05%, respectively. In multiple logistic regression analysis, only FIB-4 score ≧1.100 (OR 2.660, 95% CI 1.201-5.889; p = .016), older age, higher diastolic blood pressure and higher uric acid were independent predictors of CKD. High noninvasive fibrosis score is associated with an increased risk of prevalent CKD; the FIB-4 is the better predictor. With a cut-off value of 1.100 for FIB-4, it is useful in excluding the presence of CKD in patients with NAFLD.

Intraoperative Magnesium Administration Does Not Reduce Postoperative Atrial Fibrillation After Cardiac Surgery

PubMed Central

Klinger, Rebecca Y.; Thunberg, Christopher A.; White, William D.; Fontes, Manuel; Waldron, Nathan H.; Piccini, Jonathan P.; Hughes, G. Chad; Podgoreanu, Mihai V.; Stafford-Smith, Mark; Newman, Mark F.; Mathew, Joseph P.

2015-01-01

Background Hypomagnesemia has been associated with an increased risk of postoperative atrial fibrillation (POAF). While earlier studies have suggested a beneficial effect of magnesium (Mg) therapy, almost all of these are limited by small sample size and relatively low Mg dose. We hypothesized that high-dose Mg decreases the occurrence of new-onset POAF, and we tested this hypothesis using data from a prospective trial assessing the effect of Mg on cognitive outcomes in cardiac surgical patients. Methods A total of 389 patients undergoing cardiac surgery were enrolled in this double-blind, placebo-controlled trial. Subjects were randomized to receive Mg as a 50 mg/kg bolus immediately after induction of anesthesia followed by another 50 mg/kg as an infusion given over 3 h (total dose 100 mg/kg) or placebo. The effect of Mg therapy on POAF was tested with logistic regression, adjusting for the risk of AF using the Risk Index for Atrial Fibrillation after Cardiac Surgery. Results Among the 363 patients analyzed, after excluding patients with chronic or acute preoperative AF (Placebo: n=177, Mg: n=186), the incidence of new-onset POAF was 42.5% (95% CI: 35 – 50%) in the Mg group compared to 37.9% (95% CI: 31 – 45%) in the placebo group (p=0.40). The 95% confidence interval for this absolute risk difference of 4.6% is −5.5% to 14.7%. The time to onset of POAF was also identical between the groups, and no significant effect of Mg was found in logistic regression analysis adjusting for AF risk (odds ratio 1.09 with 95% CI 0.69 – 1.72, p=0.73). Conclusions High-dose intraoperative Mg therapy did not decrease the incidence of new-onset POAF after cardiac surgery. PMID:26237622

Risk factors for aneurysmal subarachnoid hemorrhage in Aomori, Japan.

PubMed

Ohkuma, Hiroki; Tabata, Hidefumi; Suzuki, Shigeharu; Islam, Md Shafiqul

2003-01-01

Japan is known to have an incidence of aneurysmal subarachnoid hemorrhage (SAH) as high as that in Finland, where SAH is especially common. However, the risk factors for SAH in Japan are unknown. The purpose of this study was to identify the risk factors and then examine their possible roles in cases of SAH in Japan. Case-control data were collected in the Aomori prefecture between June 2000 and May 2001 and in the Shimokita area between 1989 and 1998. A history of hypertension, cigarette smoking, alcohol consumption, hypercholesterolemia, and diabetes mellitus were examined as possible risk factors for SAH by using stepwise logistic regression analysis. Stepwise logistic regression analysis showed that a history of hypertension and current smoking increased the risk of SAH and that a history of hypercholesterolemia decreased the risk of SAH. Alcohol consumption and a history of diabetes mellitus were excluded from the model, because their log-likelihood ratios were not significant. The adjusted odds ratios, obtained by forcing matching factors, were 2.29 for a history of hypertension (95% CI, 1.66 to 3.16), 3.12 for current smoking (95% CI, 2.05 to 4.77), and 0.41 for a history of hypercholesterolemia (95% CI, 0.24 to 0.71). The prevalence of hypertension in control subjects was 27% in men and 31% in women, whereas the prevalence of cigarette smoking in control subjects was 46% in men and 9% in women. Hypertension and cigarette smoking seem to be independent risk factors for SAH in Japan. The high prevalence of hypertension in both sexes and the high prevalence of cigarette smoking in men in the general population might contribute to the high incidence of SAH in Japan.

Is preeclampsia an independent predictor of diastolic dysfunction? A retrospective cohort study.

PubMed

Guirguis, George F; Aziz, Michael M; Boccia Liang, Claire; Williams, Shauna F; Apuzzio, Joseph J; Bilinski, Robyn; Mornan, Adenieki J D; Shah, Leena P

2015-10-01

To determine if preeclampsia is an independent predictor of diastolic dysfunction and what factors among patients with preeclampsia are associated with diastolic dysfunction. This is a retrospective cohort study of patients who delivered between 2008 and 2013 at a single institution who had a maternal echocardiogram during their pregnancy or within 5months of delivery. Patients with structural heart disease, ejection fraction less than 45%, pulmonary embolus, or age over 45years were excluded. Medical records were reviewed for medical and obstetric complications and echocardiogram findings. Demographic characteristics and rate of diastolic dysfunction were compared between patients with preeclampsia and without preeclampsia. Multivariate logistic regression was performed controlling for age, ethnicity, gestational age at delivery, diabetes, preeclampsia, intrauterine growth restriction (IUGR), antihypertensive use and magnesium sulfate administration. Sixty-six patients were identified, of which 39 (59%) had preeclampsia. Past history of preeclampsia, IUGR in the current pregnancy, antihypertensive use and magnesium sulfate use were higher in the preeclampsia group. Fifteen patients (39%) in the preeclampsia group were African-American compared to 2 (3%) in the control group (p<0.01). Seventeen (44%) of the patients with preeclampsia were found to have diastolic dysfunction compared to 3 (11%) controls (OR=6.18, 95% CI 1.59,24.02; p=0.006). Logistic regression analysis did not reveal other independent predictors of diastolic dysfunction. In the patients with preeclampsia, history of preeclampsia with severe features and IUGR were not associated with diastolic dysfunction. Our study supports previous findings that preeclampsia is associated with diastolic dysfunction. Copyright © 2015 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.

Predictors of Donor Heart Utilization for Transplantation in United States.

PubMed

Trivedi, Jaimin R; Cheng, Allen; Gallo, Michele; Schumer, Erin M; Massey, H Todd; Slaughter, Mark S

2017-06-01

Optimum use of donor organs can increase the reach of the transplantation therapy to more patients on waiting list. The heart transplantation (HTx) has remained stagnant in United States over the past decade at approximately 2,500 HTx annually. With the use of the United Network of Organ Sharing (UNOS) deceased donor database (DCD) we aimed to evaluate donor factors predicting donor heart utilization. UNOS DCD was queried from 2005 to 2014 to identify total number of donors who had at least one of their organs donated. We then generated a multivariate logistic regression model using various demographic and clinical donor factors to predict donor heart use for HTx. Donor hearts not recovered due to consent or family issues or recovered for nontransplantation reasons were excluded from the analysis. During the study period there were 80,782 donors of which 23,606 (29%) were used for HTx, and 38,877 transplants (48%) were not used after obtaining consent because of poor organ function (37%), donor medical history (13%), and organ refused by all programs (5%). Of all, 22,791 donors with complete data were used for logistic regression (13,389 HTx, 9,402 no-HTx) which showed significant predictors of donor heart use for HTx. From this model we assigned probability of donor heart use and identified 3,070 donors with HTx-eligible unused hearts for reasons of poor organ function (28%), organ refused by all programs (15%), and recipient not located (9%). An objective system based on donor factors can predict donor heart use for HTx and may help increase availability of hearts for transplantation from existing donor pool. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

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

Estimating interaction on an additive scale between continuous determinants in a logistic regression model.

PubMed

Knol, Mirjam J; van der Tweel, Ingeborg; Grobbee, Diederick E; Numans, Mattijs E; Geerlings, Mirjam I

2007-10-01

To determine the presence of interaction in epidemiologic research, typically a product term is added to the regression model. In linear regression, the regression coefficient of the product term reflects interaction as departure from additivity. However, in logistic regression it refers to interaction as departure from multiplicativity. Rothman has argued that interaction estimated as departure from additivity better reflects biologic interaction. So far, literature on estimating interaction on an additive scale using logistic regression only focused on dichotomous determinants. The objective of the present study was to provide the methods to estimate interaction between continuous determinants and to illustrate these methods with a clinical example. and results From the existing literature we derived the formulas to quantify interaction as departure from additivity between one continuous and one dichotomous determinant and between two continuous determinants using logistic regression. Bootstrapping was used to calculate the corresponding confidence intervals. To illustrate the theory with an empirical example, data from the Utrecht Health Project were used, with age and body mass index as risk factors for elevated diastolic blood pressure. The methods and formulas presented in this article are intended to assist epidemiologists to calculate interaction on an additive scale between two variables on a certain outcome. The proposed methods are included in a spreadsheet which is freely available at: http://www.juliuscenter.nl/additive-interaction.xls.

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…

Equitable access to health insurance for socially excluded children? The case of the National Health Insurance Scheme (NHIS) in Ghana.

PubMed

Williams, Gemma A; Parmar, Divya; Dkhimi, Fahdi; Asante, Felix; Arhinful, Daniel; Mladovsky, Philipa

2017-08-01

To help reduce child mortality and reach universal health coverage, Ghana extended free membership of the National Health Insurance Scheme (NHIS) to children (under-18s) in 2008. However, despite the introduction of premium waivers, a substantial proportion of children remain uninsured. Thus far, few studies have explored why enrolment of children in NHIS may remain low, despite the absence of significant financial barriers to membership. In this paper we therefore look beyond economic explanations of access to health insurance to explore additional wider determinants of enrolment in the NHIS. In particular, we investigate whether social exclusion, as measured through a sociocultural, political and economic lens, can explain poor enrolment rates of children. Data were collected from a cross-sectional survey of 4050 representative households conducted in Ghana in 2012. Household indices were created to measure sociocultural, political and economic exclusion, and logistic regressions were conducted to study determinants of enrolment at the individual and household levels. Our results indicate that socioculturally, economically and politically excluded children are less likely to enrol in the NHIS. Furthermore, households excluded in all dimensions were more likely to be non-enrolled or partially-enrolled (i.e. not all children enrolled within the household) than fully-enrolled. These results suggest that equity in access for socially excluded children has not yet been achieved. Efforts should be taken to improve coverage by removing the remaining small, annually renewable registration fee, implementing and publicising the new clause that de-links premium waivers from parental membership, establishing additional scheme administrative offices in remote areas, holding regular registration sessions in schools and conducting outreach sessions and providing registration support to female guardians of children. Ensuring equitable access to NHIS will contribute substantially to improving child health and reducing child mortality in Ghana. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

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.

Multiple Nonspecific Sites of Joint Pain Outside the Knees Develop in Persons With Knee Pain.

PubMed

Felson, David T; Niu, Jingbo; Quinn, Emily K; Neogi, Tuhina; Lewis, Cara L; Lewis, Cora E; Frey Law, Laura; McCulloch, Chuck; Nevitt, Michael; LaValley, Michael

2017-02-01

Many persons with knee pain have joint pain outside the knee, but despite the impact and high frequency of this pain, its distribution and causes have not been studied. We undertook this study to test the hypothesis of those studying gait abnormalities who have suggested that knee pain causes pain in adjacent joints but that pain adaptation strategies are highly individualized. We studied persons ages 50-79 years with or at high risk of knee osteoarthritis who were recruited from 2 community-based cohorts, the Multicenter Osteoarthritis Study and the Osteoarthritis Initiative, and we followed them up for 5-7 years. We excluded those with knee pain at baseline and compared those who had developed knee pain at the first follow-up examination (the index visit) with those who had not. We examined pain on most days at joint regions outside the knee in examinations after the index visit. Logistic regression analyses examined the risk of joint-specific pain adjusted for age, sex, body mass index, and symptoms of depression, and we performed sensitivity analyses excluding those with widespread pain. In the combined cohorts, 693 persons had knee pain at the index visit and 2,793 did not. A total of 79.6% of those with bilateral knee pain and 63.8% of those with unilateral knee pain had pain during follow-up in a joint region outside the knee, compared with 49.9% of those without knee pain. There was an increased risk of pain at most extremity joint sites, without a predilection for specific sites. Results were unchanged when those with widespread pain were excluded. Persons with chronic knee pain are at increased risk of pain in multiple joints in no specific pattern. © 2016, American College of Rheumatology.

Weight Status in the First 2 Years of Life and Neurodevelopmental Impairment in Extremely Low Gestational Age Newborns.

PubMed

Belfort, Mandy B; Kuban, Karl C K; O'Shea, T Michael; Allred, Elizabeth N; Ehrenkranz, Richard A; Engelke, Stephen C; Leviton, Alan

2016-01-01

To examine the extent to which weight gain and weight status in the first 2 years of life relate to the risk of neurodevelopmental impairment in extremely preterm infants. In a cohort of 1070 infants born between 23 and 27 weeks' gestation, we examined weight gain from 7-28 days of life (in quartiles) and weight z-score at 12 and 24 months corrected age (in 4 categories: <-2; ≥-2, <-1; ≥1, <1; and ≥1) in relation to these adverse neurodevelopmental outcomes: Bayley-II mental development index <55, Bayley-II psychomotor development index <55, cerebral palsy, Gross Motor Function Classification System ≥1 (cannot walk without assistance), microcephaly. We adjusted for confounders in logistic regression, stratified by sex, and performed separate analyses including the entire sample, and excluding children unable to walk without assistance (motor impairment). Weight gain in the lowest quartile from 7-28 days was not associated with higher risk of adverse outcomes. Children with a 12-month weight z-score <-2 were at increased risk for all adverse outcomes in girls, and for microcephaly and Gross Motor Function Classification System ≥1 in boys. However, excluding children with motor impairment attenuated all associations except that of weight z-score <-2 with microcephaly in girls. Similarly, most associations of low weight z-score at 24 months with adverse outcomes were attenuated with exclusion of children with motor impairment. Excluding children who have gross motor impairment appears to eliminate the association of low weight status with neurodevelopmental impairments at 2 years in extremely preterm infants. Copyright © 2016 Elsevier Inc. All rights reserved.

A FABP-ulous 'rule out' strategy? Heart fatty acid binding protein and troponin for rapid exclusion of acute myocardial infarction.

PubMed

Body, Richard; McDowell, Garry; Carley, Simon; Wibberley, Christopher; Ferguson, Jamie; Mackway-Jones, Kevin

2011-08-01

Many Emergency Departments (EDs) utilise 'triple marker' testing with CK-MB, myoglobin and troponin I (cTnI) to exclude acute myocardial infarction (AMI) within hours of presentation. We evaluated the ability of 8 biomarkers to rapidly exclude AMI at the point of presentation and investigated whether 'triple marker' testing represents the optimal multimarker strategy. We recruited patients who presented to the ED with suspected cardiac chest pain occurring within 24 h. Blood was drawn at the time of presentation. Diagnostic value was assessed by calculating the area under the ROC curve (AUC) and a multivariate model was constructed by logistic regression. The primary outcome was a diagnosis of AMI, established by ≥12-h troponin testing in all patients. 705 included patients underwent venepuncture a median of 3.5 h after symptom onset. Heart fatty acid binding protein (H-FABP) had an AUC of 0.86 (95% CI 0.82-0.90), which was significantly higher than any other biomarker including cTnI. While no single biomarker could enable exclusion of AMI, multivariate analysis identified cTnI and H-FABP as the optimal biomarker combination. Combined with clinical risk stratification, this strategy had a sensitivity of 96.9%, specificity of 54.7%, PPV 32.4% and NPV 98.8%. We have derived an algorithm that would enable AMI to be immediately excluded in 315 (44.7%) patients at the cost of missing 6 AMIs per 1000 patients treated. While the risk is likely to be unacceptable for clinical implementation, we have highlighted an area for future development using serial testing and increasingly sensitive assays. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Simulated effect of pneumococcal vaccination in the Netherlands on existing rules constructed in a non-vaccinated cohort predicting sequelae after bacterial meningitis

PubMed Central

2010-01-01

Background Previously two prediction rules identifying children at risk of hearing loss and academic or behavioral limitations after bacterial meningitis were developed. Streptococcus pneumoniae as causative pathogen was an important risk factor in both. Since 2006 Dutch children receive seven-valent conjugate vaccination against S. pneumoniae. The presumed effect of vaccination was simulated by excluding all children infected by S. pneumoniae with the serotypes included in the vaccine, from both previous collected cohorts (between 1990-1995). Methods Children infected by one of the vaccine serotypes were excluded from both original cohorts (hearing loss: 70 of 628 children; academic or behavioral limitations: 26 of 182 children). All identified risk factors were included in multivariate logistic regression models. The discriminative ability of both new models was calculated. Results The same risk factors as in the original models were significant. The discriminative ability of the original hearing loss model was 0.84 and of the new model 0.87. In the academic or behavioral limitations model it was 0.83 and 0.84 respectively. Conclusion It can be assumed that the prediction rules will also be applicable on a vaccinated population. However, vaccination does not provide 100% coverage and evidence is available that serotype replacement will occur. The impact of vaccination on serotype replacement needs to be investigated, and the prediction rules must be validated externally. PMID:20815866

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.

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.

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.

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.

The Impact of Laparoscopic Approaches on Short-term Outcomes in Patients Undergoing Liver Surgery for Metastatic Tumors.

PubMed

Karagkounis, Georgios; Seicean, Andreea; Berber, Eren

2015-06-01

To compare the perioperative outcomes associated with open and laparoscopic (LAP) surgical approaches for liver metastases. The American College of Surgeons National Surgical Quality Improvement Program database was used to identify all adult patients who underwent surgical therapy for metastatic liver tumors between 2006 and 2012 (N=7684). Patients who underwent >1 procedure were excluded. Logistic regression after matching on propensity scores was used to assess the association between surgical approaches and perioperative outcomes. A total of 4555 patients underwent open resection, 387 LAP resection, 297 open radiofrequency ablation (RFA), and 265 LAP RFA. In propensity-matched samples (over 95% of patients successfully matched), there was no significant difference between LAP resection and LAP RFA in perioperative complications and length of stay and both compared favorably with their open counterparts. Minimally invasive approaches for secondary hepatic malignancies were associated with improved postoperative morbidity and length of stay and should be preferred in appropriate patients.

Does delayed pushing in the second stage of labor impact perinatal outcomes?

PubMed

Frey, Heather A; Tuuli, Methodius G; Cortez, Sarah; Odibo, Anthony O; Roehl, Kimberly A; Shanks, Anthony L; Macones, George A; Cahill, Alison G

2012-11-01

To estimate maternal, neonatal, and labor outcomes associated with delayed pushing. A retrospective cohort study of all consecutive women admitted to a single institution in labor at term who reached the second stage of labor. Pregnancies with multiple fetuses or major anomalies were excluded. Delayed pushing was defined as initiation of pushing ≥60 minutes after complete dilatation. Primary outcome was mode of delivery. Multivariable logistic regression was used to control for confounding. Of the 5290 women who met inclusion criteria, 471 (8.9%) employed delayed pushing, and 4819 (91.1%) pushed immediately. Delayed pushing was associated with increased rates of cesarean, operative vaginal delivery, maternal fever, and lower arterial cord pH. Duration of the second stage and length of time spent pushing were significantly longer with delayed pushing. Delayed pushing is associated with lower rates of spontaneous vaginal delivery and increased adverse maternal and neonatal outcomes. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

The association between overactive bladder and fibromyalgia syndrome: a community survey.

PubMed

Chung, Jae Hoon; Kim, Shin Ah; Choi, Bo Youl; Lee, Hye-Soon; Lee, Seung Wook; Kim, Yong Tae; Lee, Tchun Yong; Moon, Hong Sang

2013-01-01

Fibromyalgia syndrome (FMS) is the most common disease causing chronic generalized pain, and FMS patients often complain of urinary symptoms such as frequency or urgency. This study focuses on the association of overactive bladder (OAB) and FMS in adults aged 40 and over. A survey of adults aged 40s and over was conducted in the Guri and Yangpyeong areas of South Korea. The response rate was 74.2% (940/1,266). After excluding subjects with incomplete questionnaires (n = 20), 920 were included in the final analysis. The association of FMS and OAB was analyzed by univariate and multivariate logistic regression analysis. Individuals with FMS had a significantly increased symptoms of OAB after adjustment for gender, age group, and area of residence (odds ratio (OR) 3.39, 95% confidence interval (CI) 1.82-6.31). The association between FMS and severity of OAB was statistical significant (P for trend <0.0001). OAB is associated with FMS. Moreover FMS increases with severity of OAB. Copyright © 2012 Wiley Periodicals, Inc.

Effects of shoulder dystocia training on the incidence of brachial plexus injury.

PubMed

Inglis, Steven R; Feier, Nikolaus; Chetiyaar, Jyothi B; Naylor, Margaret H; Sumersille, Melanie; Cervellione, Kelly L; Predanic, Mladen

2011-04-01

We sought to determine whether implementation of shoulder dystocia training reduces the incidence of obstetric brachial plexus injury (OBPI). After implementing training for maternity staff, the incidence of OBPI was compared between pretraining and posttraining periods using both univariate and multivariate analyses in deliveries complicated by shoulder dystocia. The overall incidence of OBPI in vaginal deliveries decreased from 0.40% pretraining to 0.14% posttraining (P < .01). OBPI after shoulder dystocia dropped from 30% to 10.67% posttraining (P < .01). Maternal body mass index (P < .01) and neonatal weight (P = .02) decreased and head-to-body delivery interval increased in the posttraining period (P = .03). Only shoulder dystocia training remained associated with reduced OBPI (P = .02) after logistic regression analysis. OBPI remained less in the posttraining period (P = .01), even after excluding all neonates with birthweights >2 SD above the mean. Shoulder dystocia training was associated with a lower incidence of OBPI and the incidence of OBPI in births complicated by shoulder dystocia. Copyright © 2011 Mosby, Inc. All rights reserved.

Foster care, externalizing disorders, and antipsychotic use among Medicaid-enrolled youths.

PubMed

Vanderwerker, Lauren; Akincigil, Ayse; Olfson, Mark; Gerhard, Tobias; Neese-Todd, Sheree; Crystal, Stephen

2014-10-01

The authors investigated the extent to which clinical diagnoses of externalizing disorders explain higher rates of antipsychotic use by foster care youths. Medicaid claims data from 44 states for 2009 for youths in foster care (N=301,894) and those not in foster care (N=5,092,574) were analyzed, excluding those with schizophrenia, bipolar disorder, autism, and major depressive disorder. Logistic regressions assessed the relationship between foster care, externalizing disorders, and antipsychotic use. Foster care youths had higher rates of externalizing disorders than the comparison group (attention-deficit hyperactivity disorder, 17.3% versus 6.5%; disruptive behavior disorder, 7.2% versus 2.5%; conduct disorder, 2.3% versus .5%) and greater antipsychotic use (7.4% versus 1.4%). Foster care remained a significant predictor of antipsychotic use after control for demographic and diagnostic covariates, including externalizing disorders (adjusted odds ratio=2.59, 95% confidence interval=2.54-2.63). High rates of externalizing disorder diagnoses only partially explained elevated levels of antipsychotic use in this vulnerable population.

Inverse association between toothbrushing and upper aerodigestive tract cancer risk in a Japanese population.

PubMed

Sato, Fumihito; Oze, Isao; Kawakita, Daisuke; Yamamoto, Noriyuki; Ito, Hidemi; Hosono, Satoyo; Suzuki, Takeshi; Kawase, Takakazu; Furue, Hiroki; Watanabe, Miki; Hatooka, Shunzo; Yatabe, Yasushi; Hasegawa, Yasuhisa; Shinoda, Masayuki; Ueda, Minoru; Tajima, Kazuo; Tanaka, Hideo; Matsuo, Keitaro

2011-11-01

Oral hygiene is attracting increasing attention as a potential risk factor for cancers. To investigate the association between toothbrushing frequency and upper aerodigestive tract (UADT) cancer, the authors conducted a large-scale case-control study. A total of 856 UADT cancer case participants and 2696 age- and sex-matched control subjects without cancer were included. Edentulous or participants with unknown frequency of toothbrushing or number of remaining teeth were excluded. Associations were assessed by odds ratios and 95% confidence intervals in logistic regression models with adjustment for potential confounders. Compared with toothbrushing once per day, the adjusted odds ratio for brushing twice or more was 0.82 (95% confidence interval: 0.68, 0.99) whereas that for not brushing was 1.79 (0.79, 4.05). This association was observed especially in subjects who had a history of heavy smoking or drinking. The authors suggest that toothbrushing could have a protective effect for UADT cancer. Copyright © 2010 Wiley Periodicals, Inc.

Postsurgical complications in patients with renal tumours with venous thrombosis treated with surgery.

PubMed

Caño-Velasco, J; Herranz-Amo, F; Barbas-Bernardos, G; Mayor-de Castro, J; Aragón-Chamizo, J; Arnal-Chacón, G; Lledó García, E; Hernández-Fernández, C

2018-04-06

Surgery on renal tumours with venous thrombosis suffers a high rate of complications and non-negligible perioperative mortality. Our objective was to analyse the postoperative complications, their relationship with the level of the thrombus and its potential predisposing factors. A retrospective analysis was conducted of 101 patients with renal tumours with venous thrombosis operated on between 1988 and 2017. Two patients were excluded because of intraoperative pulmonary thromboembolism and exitus (2%). The postsurgical complications were classified according to Clavien-Dindo. To compare the qualitative variables, we employed the chi-squared test. We performed a multivariate analysis using binary logistic regression to identify the independent predictors. Some type of postsurgical complication occurred in 34 (34.3%) patients, 11 (11.1%) of which were severe (Clavien III-V). There were significant differences in the total complications (P=.003) and severe complications (Clavien≥III; P=.03) depending on the level of the tumour thrombus. Copyright © 2018 AEU. Publicado por Elsevier España, S.L.U. All rights reserved.

Count me in: response to sexual orientation measures among older adults.

PubMed

Fredriksen-Goldsen, Karen I; Kim, Hyun-Jun

2015-07-01

Health disparities exist among sexual minority older adults. Yet, health and aging surveys rarely include sexual orientation measures and when they do, they often exclude older adults from being asked about sexual orientation. This is the first population-based study to assess item nonresponse to sexual orientation measures by age and change over time. We compare response rates and examine time trends in response patterns using adjusted logistic regressions. Among adults aged 65 and older, the nonresponse rate on sexual orientation is lower than income. While older adults show higher nonresponse rates on sexual orientation than younger adults, the nonresponse rates have significantly decreased over time. By 2010, only 1.23% of older adults responded don't know/not sure, with 1.55% refusing to answer sexual orientation questions. Decisions to not ask sexual orientation among older adults must be reconsidered, given documented health disparities and rapidly changing social trends in the understanding of diverse sexualities. © The Author(s) 2014.

On Models for Binomial Data with Random Numbers of Trials

PubMed Central

Comulada, W. Scott; Weiss, Robert E.

2010-01-01

Summary A binomial outcome is a count s of the number of successes out of the total number of independent trials n = s + f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability π of success that cannot be directly incorporated by the logistic regression model. Observations where n = 0 are excluded from the binomial analysis yet may be important to understanding how π is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study. PMID:17688514

Black tobacco, wine and mate in oropharyngeal cancer. A case-control study from Uruguay.

PubMed

De Stefani, E; Correa, P; Oreggia, F; Deneo-Pellegrini, H; Fernandez, G; Zavala, D; Carzoglio, J; Leiva, J; Fontham, E; Rivero, S

1988-01-01

A case-control study of oral and pharyngeal cancer involving interviews with 108 cases and 286 controls was carried out in the University Hospital of Montevideo, Uruguay. The study was restricted to males and cases afflicted with lip, salivary gland and nasopharyngeal cancer were excluded. Point estimates of RR associated with smoking variables, alcohol variables, nutritional items and ingestion of hot infusions of the herb Ilex paraguariensis ('Mate') were obtained by logistic regression analysis. Dark tobacco smokers showed a RR 3.4 times higher than light tobacco users and heavy drinkers of wine displayed an OR of 17.2. Mate exposure showed a significant dose-response, after adjustment for age, tobacco and alcohol intake, with a fivefold increase in risk for heavy consumers. Joint exposure to black tobacco and wine displayed very high risks and no significant interactions were observed. The results suggest that the high rates of oropharyngeal cancer could be explained by the multiplicative effect of black tobacco smoking, wine drinking and mate ingestion.

A Pharmacist Telephone Intervention to Identify Adherence Barriers and Improve Adherence Among Nonadherent Patients with Comorbid Hypertension and Diabetes in a Medicare Advantage Plan.

PubMed

Abughosh, Susan M; Wang, Xin; Serna, Omar; Henges, Chris; Masilamani, Santhi; Essien, Ekere James; Chung, Nancy; Fleming, Marc

2016-01-01

Patients with comorbid hypertension (HTN) and diabetes mellitus (DM) are at a high risk of developing macrovascular and microvascular complications of DM. Controlling high blood pressure can greatly reduce these complications. Angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin II receptor blockers (ARBs) are recommended for patients with both DM and HTN by the American Diabetes Association guidelines, and their benefit and efficacy in reducing macrovascular and microvascular complications of DM have been well documented. Poor adherence, however, remains a significant barrier to achieving full effectiveness and optimal outcomes. To examine the effect of a brief pharmacist telephone intervention in identifying adherence barriers and improving adherence to ACEI/ARB medications among nonadherent patients with comorbid HTN and DM who are enrolled in a Medicare Advantage plan. Cigna-HealthSpring's medical claims data was used to identify patients with HTN and DM diagnoses by using ICD-9-CM codes 401 and 250, and at least 2 fills for ACEIs or ARBs between January 2013 and October 2013. Patients who failed to refill their medication for more than 1 day and had a proportion of days covered (PDC) < 0.8 were considered nonadherent and were contacted by a pharmacist by phone to identify adherence barriers. Two outcome variables were evaluated: The first was adherence to ACEIs/ARBs, defined as PDC during the 6 months following the phone call intervention. The second outcome variable was a categorical outcome of discontinuation versus continuation. Discontinuation was defined as not using ACEIs/ARBs during the 6-month post-intervention period. Patients who disenrolled from the plan in 2014 or were switched to another medication commonly used for treating DM and HTN were excluded from further analysis. Descriptive statistics were conducted to assess the frequency distribution of sample demographic characteristics at baseline. Multiple linear regression was conducted to assess the intervention effect on adherence during the 6 months post-intervention using the first outcome of post-intervention PDC, adjusting for baseline PDC and other covariates. Logistic regression was performed to assess the association between medication discontinuation and other baseline characteristics using the second outcome of discontinuation. Other control variables in the models included demographics (age, sex, language), physician specialty (primary care vs. specialist), health plan (low-income subsidy vs. other), Centers for Medicare & Medicaid risk score, Charlson Comorbidity Index, and number of distinct medications. In total, 186 hypertensive diabetic patients, nonadherent to ACEIs/ARBs (PDC < 0.8), were included in the study. Of the 186 patients, 87 received the pharmacist phone call intervention. Among these patients, forgetfulness (25.29%) and doctor issues, such as having difficulty scheduling appointments (16.79%), were the most commonly reported barriers. After excluding those who switched from ACEIs/ARBs to another medication, 157 patients were included in the logistic regression model. Of those, 131 had continued using ACEIs/ARBs and were included in the linear regression model. The mean (±SD) post-intervention PDC for the intervention group was 0.58 (±0.26) and for the control group 0.29 (±0.17). Intervention was a significant predictor of better adherence in the linear regression model after adjusting all the other baseline covariates (β = 0.3182, 95% CI = 0.19-0.38, P < 0.001). Other covariates were not significantly associated with better adherence. In the logistic regression model (discontinuation: 26 [yes]/131 [no]) for predicting medication discontinuation, patients who received intervention were more likely to continue using ACEIs/ARBs (OR = 3.56, 95% CI = 1.06-11.86), and those with a higher comorbidity index were less likely to continue using them (OR = 0.72, 95% CI = 0.53-0.99). The brief pharmacist telephone intervention resulted in significantly better PDCs during the 6 months following the intervention as well as lower discontinuation rates among a group of nonadherent patients with comorbid HTN and DM. The overall PDC rates in both the intervention and control groups were still lower than the recommended 80%. Improving adherence to clinically meaningful values may require more than a brief pharmacist phone call. Incorporating motivational interviewing techniques with follow-up calls to address adherence barriers may be more influential in forming sustainable behavioral change and enhancing medication adherence.

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.

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 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…

Logistic regression accuracy across different spatial and temporal scales for a wide-ranging species, the marbled murrelet

Treesearch

Carolyn B. Meyer; Sherri L. Miller; C. John Ralph

2004-01-01

The scale at which habitat variables are measured affects the accuracy of resource selection functions in predicting animal use of sites. We used logistic regression models for a wide-ranging species, the marbled murrelet, (Brachyramphus marmoratus) in a large region in California to address how much changing the spatial or temporal scale of...

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…

Serum Ferritin, Insulin Resistance, and Metabolic Syndrome: Clinical and Laboratory Associations in 769 Non-Hispanic Whites Without Diabetes Mellitus in the HEIRS Study

PubMed Central

Barton, J. Clayborn; Barton, James C.

2015-01-01

Abstract Background: In some reports, serum ferritin (SF) has been associated with insulin resistance and metabolic syndrome. Methods: We studied non-Hispanic whites without diabetes mellitus in a postscreening examination. Participants included cases [HFE C282Y homozygosity; and transferrin saturation (TS) >50% and SF >300 μg/L (males) and TS >45% and SF >200 μg/dL (females), regardless of HFE genotype] and controls [HFE wild-type (wt/wt) and TS/SF 25th–75th percentiles]. We excluded participants with overnight fasts <8 hr, cirrhosis, hepatitis B or C, pregnancy, or missing data. Observations were age, sex, C282Y homozygosity, body mass index (BMI), systolic and diastolic blood pressures (SBP, DBP), lymphocytes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), C-reactive protein (CRP), TS, SF, and glucose/insulin. Insulin resistance was defined as homeostasis model assessment of insulin resistance (HOMA-IR) 4th quartile (≥2.70). Results: A total of 407 women and 362 men (mean age 54 years) included 188 C282Y homozygotes and 371 wt/wt. Significant trends across HOMA-IR quartiles included age, male sex, BMI, SBP, DBP, lymphocytes, ALT, CRP >0.5 mg/dL (positive), and TS (negative). Multiple regression on HOMA-IR revealed significant associations with male sex, BMI, SBP, lymphocytes, ALT, CRP>0.5 mg/dL (positive), and DBP and SF (negative). Logistic regression on HOMA-IR 4th quartile revealed significant positive associations with age, male sex, BMI, and lymphocytes. Metabolic syndrome occurred in 53 participants (6.9%). Logistic regression on metabolic syndrome revealed these odds ratios: HOMA-IR 4th quartile [9.1 (4.8, 17.3)] and CRP >0.5 mg/dL [2.9 (1.6, 5.4)]. Conclusions: Age, male sex, BMI, and lymphocytes were positively associated with HOMA-IR after correction for other factors. HOMA-IR 4th quartile and CRP >0.5 mg/dL predicted metabolic syndrome. PMID:25423072

Serum ferritin, insulin resistance, and metabolic syndrome: clinical and laboratory associations in 769 non-hispanic whites without diabetes mellitus in the HEIRS study.

PubMed

Acton, Ronald T; Barton, J Clayborn; Barton, James C

2015-03-01

In some reports, serum ferritin (SF) has been associated with insulin resistance and metabolic syndrome. We studied non-Hispanic whites without diabetes mellitus in a postscreening examination. Participants included cases [HFE C282Y homozygosity; and transferrin saturation (TS) >50% and SF >300 μg/L (males) and TS >45% and SF >200 μg/dL (females), regardless of HFE genotype] and controls [HFE wild-type (wt/wt) and TS/SF 25th-75th percentiles]. We excluded participants with overnight fasts <8 hr, cirrhosis, hepatitis B or C, pregnancy, or missing data. Observations were age, sex, C282Y homozygosity, body mass index (BMI), systolic and diastolic blood pressures (SBP, DBP), lymphocytes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), C-reactive protein (CRP), TS, SF, and glucose/insulin. Insulin resistance was defined as homeostasis model assessment of insulin resistance (HOMA-IR) 4th quartile (≥2.70). A total of 407 women and 362 men (mean age 54 years) included 188 C282Y homozygotes and 371 wt/wt. Significant trends across HOMA-IR quartiles included age, male sex, BMI, SBP, DBP, lymphocytes, ALT, CRP >0.5 mg/dL (positive), and TS (negative). Multiple regression on HOMA-IR revealed significant associations with male sex, BMI, SBP, lymphocytes, ALT, CRP>0.5 mg/dL (positive), and DBP and SF (negative). Logistic regression on HOMA-IR 4th quartile revealed significant positive associations with age, male sex, BMI, and lymphocytes. Metabolic syndrome occurred in 53 participants (6.9%). Logistic regression on metabolic syndrome revealed these odds ratios: HOMA-IR 4th quartile [9.1 (4.8, 17.3)] and CRP >0.5 mg/dL [2.9 (1.6, 5.4)]. Age, male sex, BMI, and lymphocytes were positively associated with HOMA-IR after correction for other factors. HOMA-IR 4th quartile and CRP >0.5 mg/dL predicted metabolic syndrome.

The Outcomes of Targeted Temperature Management After Cardiac Arrest at Emergency Department: A Real-World Experience in a Developing Country.

PubMed

Srivilaithon, Winchana; Muengtaweepongsa, Sombat

2017-03-01

Targeted temperature management (TTM) is indicated for comatose survivors of cardiac arrest to improve outcomes. However, the benefit of TTM was verified by rigid controlled clinical trials. This study aimed at evaluating its effects in real-world practices. A prospective observational study was done at the emergency department of tertiary care, Thammasat Hospital, from March 2012 until October 2015. We included all who did not obey verbal commands after being resuscitated from cardiac arrest regardless of initial cardiac rhythm. We excluded patients with traumatic arrest, uncontrolled bleeding, younger than 15 years old, and of poor neurological status (Glasgow coma scale below 14) before cardiac arrest. Primary and secondary outcomes were survival to hospital discharge and favorable neurological outcome (Cerebral Performance Categories 1 or 2 within 30 days). We used the logistic regression model to estimate the propensity score (PS) that will be used as a weight in the analysis. To analyze outcomes, the PS was introduced as a factor in the final logistic regression model in conjunction with other factors. A total of 192 cases, 61 and 131 patients, were enrolled in TTM and non-TTM groups, respectively. Characteristics believed to be related to initiation of TTM: gender, age, cardiac etiology, out-of-hospital cardiac arrest, witness arrest, collapse time, initial rhythm, received defibrillation, and advanced airway insertion, were included in multivariable analysis and estimated PS. After adjusted regression analysis with PS, the TTM group had a better result in survival to hospital discharge (34.43% vs. 12.21%; adjusted incidence risk ratio (IRR), 2.95; 95% confidence interval (CI), 1.49-5.84; p = 0.002). For neurological outcome, the TTM group had a higher number of favorable neurological outcomes (24.59% vs. 6.87%; IRR, 3.96; 95% CI, 1.67-9.36; p = 0.002). In real-world practices without a strictly controlled environment, TTM can improve survival and favorable neurological outcome in postcardiac arrest patients regardless of initial rhythm.

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.

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

[Relationship between fruit and vegetable gardening and health-related factors: male community gardeners aged 50-74 years living in a suburban area of Japan].

PubMed

Machida, Daisuke; Yoshida, Tohru

2017-01-01

Objectives　The aims of the study were as follows: 1) to investigate the relationship between community fruit and vegetable (FV) gardening and perceived changes in health-related factors by utilizing community gardens and 2) to determine the relationship of community FV gardening and other types of gardening on health-related factors among men aged 50-74 years living in a suburban area of Japan.Methods　In this cross-sectional study, we targeted men aged 50-74 years living in a city in Gunma Prefecture. A survey solicited demographic characteristics, FV gardening information, and health-related factors [BMI, self-rated health status, FV intake, physical activity (PA), and perceived neighborhood social cohesion (PNSC)]. The participants were divided into three groups: community gardeners, other types of gardeners, and non-gardeners. Items related to community gardening and perceived changes in health-related factors were presented only to community gardeners. The relationship between community gardening and perceived changes in health-related factors were analyzed by computing correlation coefficients. The relationships between FV gardening and specific health-related factors were analyzed by logistic regression modeling.Results　Significant positive correlations were observed between community FV gardening (the frequency of community gardening, the product of community gardening time and frequency of community gardening) and perceived changes in health-related factors (frequency of FV intake, amount of FV intake, and PA). The logistic regression models showed that 1) the number of participants with ≥23 METs h/week of PA was significantly greater among community gardeners than among non-gardeners; 2) the number of participants whose frequency of total vegetable intake, total vegetable intake (excluding juice), and total FV intake (excluding juice) was ≥5 times/day was significantly greater among other types of gardeners than non-gardeners; 3) participants with scores ≥ the median of PNSC were significantly greater among other types of gardeners than non-gardeners; and 4) participants who spent ≥4 hours/day sitting were significantly fewer among other types of gardeners than non-gardeners.Conclusion　Higher frequency of community gardening appears to induce greater perceived positive changes on FV intake and PA. It was indicated that FV gardening in community gardens contributes to increased PA, whereas other types of FV gardening contribute to increased FV intake frequency and decreased sitting time. In the future, higher-quality studies-for example, intervention studies using more rigorous measurements-will be necessary.

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.

Long-Term Outcomes after Abdominal Wall Reconstruction with Acellular Dermal Matrix.

PubMed

Garvey, Patrick B; Giordano, Salvatore A; Baumann, Donald P; Liu, Jun; Butler, Charles E

2017-03-01

Long-term outcomes data for hernia recurrence rates after abdominal wall reconstruction (AWR) with acellular dermal matrix (ADM) are lacking. The aim of this study was to assess the long-term durability of AWR using ADM. We studied patients who underwent AWR with ADM at a single center in 2005 to 2015 with a minimum follow-up of 36 months. Hernia recurrence was the primary end point and surgical site occurrence (SSO) was a secondary end point. The recurrence-free survival curves were estimated by Kaplan-Meier product limit method. Univariate and multivariable Cox proportional hazards regression models and logistic regression models were used to evaluate the associations of risk factors at surgery with subsequent risks for hernia recurrence and SSO, respectively. A total of 512 patients underwent AWR with ADM. After excluding those with follow-up less than 36 months, 191 patients were included, with a median follow-up of 52.9 months (range 36 to 104 months). Twenty-six of 191 patients had a hernia recurrence documented in the study. The cumulative recurrence rates were 11.5% at 3 years and 14.6% by 5 years. Factors significantly predictive of hernia recurrence developing included bridged repair, wound skin dehiscence, use of human cadaveric ADM, and coronary disease; component separation was protective. In a subset analysis excluding bridged repairs and human cadaveric ADM patients, cumulative hernia recurrence rates were 6.4% by 3 years and 8.3% by 5 years. The crude rate of SSO was 25.1% (48 of 191). Factors significantly predictive of the incidence of SSO included at least 1 comorbidity, BMI ≥30 kg/m 2 , and defect width >15 cm. Use of ADM for AWR was associated with 11.5% and 14.6% hernia recurrence rates at 3- and 5-years follow-up, respectively. Avoiding bridged repairs and human cadaveric ADM can improve long-term AWR outcomes using ADM. Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Clinical decision support tool for Co-management signalling.

PubMed

Horta, Alexandra Bayão; Salgado, Cátia; Fernandes, Marta; Vieira, Susana; Sousa, João M; Papoila, Ana Luísa; Xavier, Miguel

2018-05-01

Co-management between internists and surgeons of selected patients is becoming one of the pillars of modern clinical management in large hospitals. Defining the patients to be co-managed is essential. The aim of this study is to create a decision tool using real-world patient data collected in the preoperative period, to support the decision on which patients should have the co-management service offered. Data was collected from the electronic clinical health records of patients who had an International Classification of Diseases, 9th edition (ICD-9) code of colorectal surgery during the period between January 2012 and October 2014 in a 200 bed private teaching hospital in Lisbon. ICD-9 codes of colorectal surgery [48.5 and 48.6 (anterior rectal resection and abdominoperineal resection), 45.7 (partial colectomy), 45.8 (Total Colectomy), and 45.9 (Bowel Anastomosis)] were used. Only patients above 18 years old were considered. Patients with more than one procedure were excluded from the study. From these data the authors investigated the construction of predictive models using logistic regression and Takagi-Sugeno fuzzy modelling. Data contains information obtained from the clinical records of a cohort of 344 adult patients. Data from 398 emergent and elective surgeries were collected, from which 54 were excluded because they were second procedures for the same patients. Four preoperative variables were identified as being the most predictive of co-management, in multivariable regression analysis. The final model performed well after being internally validated (0.81 AUC, 77% accuracy, 74% sensitivity, 78% specificity, 93% negative predictive value). The results indicate that the decision process can be more objective and potentially automated. The authors developed a prediction model based on preoperative characteristics, in order to support the decision for the co-management of surgical patients in the postoperative ward setting. The model is a simple bedside decision tool that uses only four numerical variables. Copyright © 2018 Elsevier B.V. All rights reserved.

Severe Headache or Migraine History Is Inversely Correlated With Dietary Sodium Intake: NHANES 1999–2004

PubMed Central

Pogoda, Janice M.; Gross, Noah B.; Arakaki, Xianghong; Fonteh, Alfred N.; Cowan, Robert P.

2016-01-01

Objective We investigated whether dietary sodium intake from respondents of a national cross‐sectional nutritional study differed by history of migraine or severe headaches. Background Several lines of evidence support a disruption of sodium homeostasis in migraine. Design Our analysis population was 8819 adults in the 1999–2004 National Health and Nutrition Examination Survey (NHANES) with reliable data on diet and headache history. We classified respondents who reported a history of migraine or severe headaches as having probable history of migraine. To reduce the diagnostic conflict from medication overuse headache, we excluded respondents who reported taking analgesic medications. Dietary sodium intake was measured using validated estimates of self‐reported total grams of daily sodium consumption and was analyzed as the residual value from the linear regression of total grams of sodium on total calories. Multivariable logistic regression that accounted for the stratified, multistage probability cluster sampling design of NHANES was used to analyze the relationship between migraine and dietary sodium. Results Odds of probable migraine history decreased with increasing dietary sodium intake (odds ratio = 0.93, 95% confidence interval = 0.87, 1.00, P = .0455). This relationship was maintained after adjusting for age, sex, and body mass index (BMI) with slightly reduced significance (P = .0505). In women, this inverse relationship was limited to those with lower BMI (P = .007), while in men the relationship did not differ by BMI. We likely excluded some migraineurs by omitting frequent analgesic users; however, a sensitivity analysis suggested little effect from this exclusion. Conclusions This study is the first evidence of an inverse relationship between migraine and dietary sodium intake. These results are consistent with altered sodium homeostasis in migraine and our hypothesis that dietary sodium may affect brain extracellular fluid sodium concentrations and neuronal excitability. PMID:27016121

Severe Headache or Migraine History is Inversely Correlated With Dietary Sodium Intake: NHANES 1999-2004.

PubMed

Pogoda, Janice M; Gross, Noah B; Arakaki, Xianghong; Fonteh, Alfred N; Cowan, Robert P; Harrington, Michael G

2016-04-01

We investigated whether dietary sodium intake from respondents of a national cross-sectional nutritional study differed by history of migraine or severe headaches. Several lines of evidence support a disruption of sodium homeostasis in migraine. Our analysis population was 8819 adults in the 1999-2004 National Health and Nutrition Examination Survey (NHANES) with reliable data on diet and headache history. We classified respondents who reported a history of migraine or severe headaches as having probable history of migraine. To reduce the diagnostic conflict from medication overuse headache, we excluded respondents who reported taking analgesic medications. Dietary sodium intake was measured using validated estimates of self-reported total grams of daily sodium consumption and was analyzed as the residual value from the linear regression of total grams of sodium on total calories. Multivariable logistic regression that accounted for the stratified, multistage probability cluster sampling design of NHANES was used to analyze the relationship between migraine and dietary sodium. Odds of probable migraine history decreased with increasing dietary sodium intake (odds ratio = 0.93, 95% confidence interval = 0.87, 1.00, P = .0455). This relationship was maintained after adjusting for age, sex, and body mass index (BMI) with slightly reduced significance (P = .0505). In women, this inverse relationship was limited to those with lower BMI (P = .007), while in men the relationship did not differ by BMI. We likely excluded some migraineurs by omitting frequent analgesic users; however, a sensitivity analysis suggested little effect from this exclusion. This study is the first evidence of an inverse relationship between migraine and dietary sodium intake. These results are consistent with altered sodium homeostasis in migraine and our hypothesis that dietary sodium may affect brain extracellular fluid sodium concentrations and neuronal excitability. © 2016 The Authors Headache published by Wiley Periodicals, Inc. on behalf of American Headache Society.

Relation of Post-Coronary Artery Bypass Graft Creatine Kinase-MB Elevations and New Q Waves With Long-Term Cardiovascular Death in Patients With Diabetes Mellitus and Multivessel Coronary Artery Disease.

PubMed

Domanski, Michael; Farkouh, Michael E; Zak, Victor; French, John; Alexander, John H; Bochenek, Andrzej; Hamon, Martial; Mahaffey, Kenneth; Puskas, John; Smith, Peter; Shrader, Peter; Fuster, Valentin

2016-12-01

Associations of early creatine phosphokinase-MB (CK-MB) elevation and new Q waves and their association with cardiovascular death (CVD) after coronary artery bypass grafting (CABG) have been reported, but this association has not been studied in a large population of patients with diabetes mellitus. In this study, we examine the association of periprocedural CK-MB elevations and new Q waves with CVD in the Future Revascularization Evaluation in Patients with Diabetes Mellitus: Optimal Management of Multivessel Disease trial. Cox proportional hazards regression was used to assess the relation of CK-MB elevations and new Q waves in the first 24 hours after procedure and their relation to CVD; logistic regression was used to assess odds ratios of these variables. Hazard ratios, 95% confidence intervals, and p values associated with Wald chi-square test are reported. CK-MB elevation in first 24 hours after procedure was independently associated with CVD. CVD hazard increased by 6% (p <0.001) with each multiple of CK-MB above the upper reference limit (URL); odds of new post-CABG Q waves increased by a factor of 1.08 (p <0.001); at 7× CK-MB URL, HR was >2. CK-MB URL multiples of 7, 12, and 15 were associated with new Q-wave odds ratios of 9, 16, and 27 times, respectively (p ≤0.001, C-statistic >0.70). New Q waves were independently associated with survival in the multivariate model only when CK-MB was excluded (p = 0.01). In conclusion, independent associations included (1) CVD and early post-CABG CK-MB elevation; (2) new Q waves with early post-CABG CK-MB elevation; (3) CVD with new Q waves only when CK-MB elevation is excluded from analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

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

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.

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.

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.

Excluded from social security: rejections of disability pension applications in Norway 1998-2004.

PubMed

Galaasen, Anders Mølster; Bruusgaard, Dag; Claussen, Bjørgulf

2012-03-01

Admission to disability pension (DP) in Norway, like most other countries, requires a medical condition as the main cause of income reduction. Still, a widespread assumption is that much of the recruitment to the programme is rather due to non-medical, mainly labour market factors. In this article, we study the grey zones between acceptance and rejection of DP applications, in light of the concept of marginalisation. From the total Norwegian population, aged 18-66 in 1998, we included all first-time applications for DP between 1998 and 2004. Logistic regressions of both application and application outcome were then performed, controlling for a range of socioeconomic variables and medical diagnosis. Medical diagnosis had the strongest impact on application outcome, together with the applicant's age. High rejection risk was found among applicants with complex musculoskeletal diagnoses, and also for complex psychiatric diagnoses as compared to well-defined ones. Persons having previously received social assistance more often applied for a DP and more often were rejected. The same is true, though on a lesser scale, for people with a weak affiliation to the labour market. The DP programme in Norway is to a large degree medically oriented, not only judicially but also in practice. Nevertheless, non-medical factors have a bearing on both application rates and application outcome. The control system seems to work in a way that excludes the most marginalised applicants, thus possibly contributing to further marginalisation of already disadvantaged groups.

One-minute heart rate recovery after cycloergometer exercise testing as a predictor of mortality in a large cohort of exercise test candidates: substantial differences with the treadmill-derived parameter.

PubMed

Gaibazzi, Nicola; Petrucci, Nicola; Ziacchi, Vigilio

2004-03-01

Previous work showed a strong inverse association between 1-min heart rate recovery (HRR) after exercising on a treadmill and all-cause mortality. The aim of this study was to determine whether the results could be replicated in a wide population of real-world exercise ECG candidates in our center, using a standard bicycle exercise test. Between 1991 and 1997, 1420 consecutive patients underwent ECG exercise testing performed according to our standard cycloergometer protocol. Three pre-specified cut-point values of 1-min HRR, derived from previous studies in the medical literature, were tested to see whether they could identify a higher-risk group for all-cause mortality; furthermore, we tested the possible association between 1-min HRR as a continuous variable and mortality using logistic regression. Both methods showed a lack of a statistically significant association between 1-min HRR and all-cause mortality. A weak trend toward an inverse association, although not statistically significant, could not be excluded. We could not validate the clear-cut results from some previous studies performed using the treadmill exercise test. The results in our study may only "not exclude" a mild inverse association between 1-min HRR measured after cycloergometer exercise testing and all-cause mortality. The 1-min HRR measured after cycloergometer exercise testing was not clinically useful as a prognostic marker.

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.

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.

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.

A comparison of three methods of assessing differential item functioning (DIF) in the Hospital Anxiety Depression Scale: ordinal logistic regression, Rasch analysis and the Mantel chi-square procedure.

PubMed

Cameron, Isobel M; Scott, Neil W; Adler, Mats; Reid, Ian C

2014-12-01

It is important for clinical practice and research that measurement scales of well-being and quality of life exhibit only minimal differential item functioning (DIF). DIF occurs where different groups of people endorse items in a scale to different extents after being matched by the intended scale attribute. We investigate the equivalence or otherwise of common methods of assessing DIF. Three methods of measuring age- and sex-related DIF (ordinal logistic regression, Rasch analysis and Mantel χ(2) procedure) were applied to Hospital Anxiety Depression Scale (HADS) data pertaining to a sample of 1,068 patients consulting primary care practitioners. Three items were flagged by all three approaches as having either age- or sex-related DIF with a consistent direction of effect; a further three items identified did not meet stricter criteria for important DIF using at least one method. When applying strict criteria for significant DIF, ordinal logistic regression was slightly less sensitive. Ordinal logistic regression, Rasch analysis and contingency table methods yielded consistent results when identifying DIF in the HADS depression and HADS anxiety scales. Regardless of methods applied, investigators should use a combination of statistical significance, magnitude of the DIF effect and investigator judgement when interpreting the results.

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.

Three combinations of manual therapy techniques within naprapathy in the treatment of neck and/or back pain: a randomized controlled trial.

PubMed

Paanalahti, Kari; Holm, Lena W; Nordin, Margareta; Höijer, Jonas; Lyander, Jessica; Asker, Martin; Skillgate, Eva

2016-04-23

Manual therapy as spinal manipulation, spinal mobilization, stretching and massage are common treatment methods for neck and back pain. The objective was to compare the treatment effect on pain intensity, pain related disability and perceived recovery from a) naprapathic manual therapy (spinal manipulation, spinal mobilization, stretching and massage) to b) naprapathic manual therapy without spinal manipulation and to c) naprapathic manual therapy without stretching for male and female patients seeking care for back and/or neck pain. Participants were recruited among patients, ages 18-65, seeking care at the educational clinic of Naprapathögskolan - the Scandinavian College of Naprapathic Manual Medicine in Stockholm. The patients (n = 1057) were randomized to one of three treatment arms a) manual therapy (i.e. spinal manipulation, spinal mobilization, stretching and massage), b) manual therapy excluding spinal manipulation and c) manual therapy excluding stretching. The primary outcomes were minimal clinically important improvement in pain intensity and pain related disability. Treatments were provided by naprapath students in the seventh semester of eight total semesters. Generalized estimating equations and logistic regression were used to examine the association between the treatments and the outcomes. At 12 weeks follow-up, 64% had a minimal clinically important improvement in pain intensity and 42% in pain related disability. The corresponding chances to be improved at the 52 weeks follow-up were 58% and 40% respectively. No systematic differences in effect when excluding spinal manipulation and stretching respectively from the treatment were found over 1 year follow-up, concerning minimal clinically important improvement in pain intensity (p = 0.41) and pain related disability (p = 0.85) and perceived recovery (p = 0.98). Neither were there disparities in effect when male and female patients were analyzed separately. The effect of manual therapy for male and female patients seeking care for neck and/or back pain at an educational clinic is similar regardless if spinal manipulation or if stretching is excluded from the treatment option. Current Controlled Trials ISRCTN92249294.

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.

Multidisciplinary approach for the management of complex hepatic injuries AAST-OIS grades IV-V: a prospective study.

PubMed

Asensio, J A; Petrone, P; García-Núñez, L; Kimbrell, B; Kuncir, E

2007-01-01

Complex hepatic injuries grades IV-V are highly lethal. The objective of this study is to assess the multidisciplinary approach for their management and to evaluate if survival could be improved with this approach. Prospective 54-month study of all patients sustaining hepatic injuries grades IV-V managed operatively at a Level I Trauma Center. survival. univariate and stepwise logistic regression. Seventy-five patients sustained penetrating (47/63%) and blunt (28/37%) injuries. Seven (9%) patients underwent emergency department thoracotomy with a mortality of 100%. Out of the 75 patients, 52 (69%) sustained grade IV, and 23 (31%) grade V. The estimated blood loss was 3,539+/-3,040 ml. The overall survival was 69%, adjusted survival excluding patients requiring emergency department thoracotomy was 76%. Survival stratified to injury grade: grade IV 42/52-81%, grade V 10/23-43%. Mortality grade IV versus V injuries (p < 0.002; RR 2.94; 95% CI 1.52-5.70). Risk factors for mortality: packed red blood cells transfused in operating room (p=0.024), estimated blood loss (p < 0.001), dysryhthmia (p < 0.0001), acidosis (p = 0.051), hypothermia (p = 0.04). The benefit of angiography and angioembolization indicated: 12% mortality (2/17) among those that received it versus a 36% mortality (21/58) among those that did not (p = 0.074; RR 0.32; 95% CI 0.08-1.25). Stepwise logistic regression identified as significant independent predictors of outcome: estimated blood loss (p= 0.0017; RR 1.24; 95% CI 1.08-1.41) and number of packed red blood cells transfused in the operating room (p = 0.0358; RR 1.16; 95% CI 1.01-1.34). The multidisciplinary approach to the management of these severe grades of injuries appears to improve survival in these highly lethal injuries. A prospective multi-institutional study is needed to validate this approach.

[Cardiovascular risk in Spanish smokers compared to non-smokers: RETRATOS study].

PubMed

Fernández de Bobadilla, Jaime; Sanz de Burgoa, Verónica; Garrido Morales, Patricio; López de Sá, Esteban

2011-11-01

To evaluate the level of cardiovascular risk in smokers seenin Primary Care clinics. Epidemiologic, cross-sectional and multicentre study. Primary Care. Every investigator included 4 consecutive patients (3 smokers, 1 non-smoker) aged 35-50 years, who came to the clinic for any reason. A total of 2,184 patients were included; 2,124 (1,597 smokers; 527 non-smokers) were evaluated and 60 patients were excluded because they did not meet with selection criteria. The 10-year risk of suffering from a fatal cardiovascular disease (CVDR) was calculated according to the SCORE (Systematic Coronary Risk Evaluation) model. The 10-year lethal CVR according SCORE model, was classified as: very high (> 15%), high (10-14%), slightly high (5-9%), average (3-4%), low (2%), very low (1%) and negligible (< 1%). A logistical regression model was used to estimate the relationship between smoking and prior cardiovascular events. 10-year fatal CVDR according to the SCORE model was significantly higher in smokers (40±5.3) vs. non-smokers (1.9±2.5) (P<.0001). low (< 3%) [78.0% non-smokers vs. 60.7% smokers (P<.0001)]; intermediate (3-5%) [11.1% non-smokers vs. 12.6% smokers (P<.001)]; high (> 5%) [10.9% non-smokers vs. 26.7% smokers (P<.001)]. The logistical regression model showed that non-smokers vs. smokers had less probability of suffering myocardial infarction (OR 0.3; 95% confidence interval (95% CI): 0.1-0.8; P<.0001), peripheral vascular disease (OR 0.6; 95% CI: 0.4-1.0; P=.0180) and chronic obstructive lung disease (OR 0.18; 95% CI: 0.1-0.2; P=.0507). Smoking is related to a high risk of fatal cardiovascular disease. Active promotion in Primary Care clinics of measures aimed at reducing the prevalence of the smoking habit would lead to a lowering of cardiovascular morbidity and mortality. Copyright © 2010 Elsevier España, S.L. All rights reserved.

The role of specific visual subfields in collisions with oncoming cars during simulated driving in patients with advanced glaucoma

PubMed Central

Kunimatsu-Sanuki, Shiho; Iwase, Aiko; Araie, Makoto; Aoki, Yuki; Hara, Takeshi; Fukuchi, Takeo; Udagawa, Sachiko; Ohkubo, Shinji; Sugiyama, Kazuhisa; Matsumoto, Chota; Nakazawa, Toru; Yamaguchi, Takuhiro; Ono, Hiroshi

2017-01-01

Background/aims To assess the role of specific visual subfields in collisions with oncoming cars during simulated driving in patients with advanced glaucoma. Methods Normal subjects and patients with glaucoma with mean deviation <–12 dB in both eyes (Humphrey Field Analyzer 24-2 SITA-S program) used a driving simulator (DS; Honda Motor, Tokyo). Two scenarios in which oncoming cars turned right crossing the driver's path were chosen. We compared the binocular integrated visual field (IVF) in the patients who were involved in collisions and those who were not. We performed a multivariate logistic regression analysis; the dependent parameter was collision involvement, and the independent parameters were age, visual acuity and mean sensitivity of the IVF subfields. Results The study included 43 normal subjects and 100 patients with advanced glaucoma. And, 5 of the 100 patients with advanced glaucoma experienced simulator sickness during the main test and were thus excluded. In total, 95 patients with advanced glaucoma and 43 normal subjects completed the main test of DS. Advanced glaucoma patients had significantly more collisions than normal patients in one or both DS scenarios (p<0.001). The patients with advanced glaucoma who were involved in collisions were older (p=0.050) and had worse visual acuity in the better eye (p<0.001) and had lower mean IVF sensitivity in the inferior hemifield, both 0°–12° and 13°–24° in comparison with who were not involved in collisions (p=0.012 and p=0.034). A logistic regression analysis revealed that collision involvement was significantly associated with decreased inferior IVF mean sensitivity from 13° to 24° (p=0.041), in addition to older age and lower visual acuity (p=0.018 and p<0.001). Conclusions Our data suggest that the inferior hemifield was associated with the incidence of motor vehicle collisions with oncoming cars in patients with advanced glaucoma. PMID:28400370

Postoperative Delirium in Severely Burned Patients Undergoing Early Escharotomy: Incidence, Risk Factors, and Outcomes.

PubMed

Guo, Zhenggang; Liu, Jiabin; Li, Jia; Wang, Xiaoyan; Guo, Hui; Ma, Panpan; Su, Xiaojun; Li, Ping

The aim of this study is to investigate the incidence, related risk factors, and outcomes of postoperative delirium (POD) in severely burned patients undergoing early escharotomy. This study included 385 severely burned patients (injured <1 week; TBSA, 31-50% or 11-20%; American Society of Anesthesiologists physical status, II-IV) aged 18 to 65 years, who underwent early escharotomy between October 2014 and December 2015, and were selected by cluster sampling. The authors excluded patients with preoperative delirium or diagnosed dementia, depression, or cognitive dysfunction. Preoperative, perioperative, intraoperative, and postoperative information, such as demographic characteristics, vital signs, and health history were collected. The Confusion Assessment Method was used once daily for 5 days after surgery to identify POD. Stepwise binary logistic regression analysis was used to identify the risk factors for POD, t-tests, and χ tests were performed to compare the outcomes of patients with and without the condition. Fifty-six (14.55%) of the patients in the sample were diagnosed with POD. Stepwise binary logistic regression showed that the significant risk factors for POD in severely burned patients undergoing early escharotomy were advanced age (>50 years old), a history of alcohol consumption (>3/week), high American Society of Anesthesiologists classification (III or IV), time between injury and surgery (>2 days), number of previous escharotomies (>2), combined intravenous and inhalation anesthesia, no bispectral index applied, long duration surgery (>180 min), and intraoperative hypotension (mean arterial pressure < 55 mm Hg). On the basis of the different odds ratios, the authors established a weighted model. When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). When the score of a patient's weighted odds ratios is more than 6, the incidence of POD increased significantly (P < .05). Further, POD was associated with more postoperative complications, including hepatic and renal function impairment and hypernatremia, as well as prolonged hospitalization, increased medical costs, and higher mortality.

Weaker masturbatory erection may be a sign of early cardiovascular risk associated with erectile dysfunction in young men without sexual intercourse.

PubMed

Huang, Yan-Ping; Chen, Bin; Yao, Feng-Juan; Chen, Sheng-Fu; Ouyang, Bin; Deng, Chun-Hua; Huang, Yi-Ran

2014-06-01

Although increasing evidences emphasize the importance of early cardiovascular evaluation in men with erectile dysfunction (ED) of unexplained aetiology, impaired masturbation-induced erections in young men are usually overlooked and habitually presumed to be psychological origin. To evaluate the young men presenting weaker masturbatory erection with no sexual intercourse (WME-NS) and verify if this cohort have early cardiovascular risks associated with ED. Male subjects aged 18-40 years with WME-NS were screened by analyzing detailed sexual intercourse and masturbatory history. The age-matched ED and non-ED population were identified by using International Index of Erectile Function-5 (IIEF-5). All subjects with acute and/or chronic diseases (including diagnosed hypertension and diabetes) and long-term pharmacotherapy were excluded. Nocturnal penile tumescence and rigidity (NPTR), systemic vascular parameters and biochemical indicators related to metabolism were assessed. Comparison analysis and logistic regression analysis were conducted among WME-NS, ED and non-ED population. In total, 78 WME-NS cases (mean 28.99 ± 5.92 years), 179 ED cases (mean 30.69 ± 5.21 years) and 43 non-ED cases (mean 28.65 ± 4.30 years) were screened for analysis. Compared with non-ED group, WME-NS group had higher prevalence of early ED risk factors including endothelial dysfunction, insulin resistance, high level of glycosylated serum protein and abnormal NPTR. Multivariable-adjusted logistic regression analysis showed endothelia dysfunction (odds ratio: 8.83 vs. 17.11, both P < 0.001) was the independent risk factor for both WME-NS and ED. Weaker masturbatory erection may be a sign of early cardiovascular risk associated with ED in young men without sexual intercourse. More studies are warranted to elucidate the clinical benefits by targeting these formulated strategies. © 2014 International Society for Sexual Medicine.

Does the length of uvula affect the palatal implant outcome in the management of habitual snoring?

PubMed

Akpinar, Meltem Esen; Yigit, Ozgur; Kocak, Ismail; Altundag, Aytug

2011-05-01

To evaluate the impact of the uvular length on the efficacy of palatal implants in primary snoring. Prospective case series, tertiary hospital, snoring and respiratory sleep disorders center. Forty subjects with inserted palatal implants and diagnoses of primary snoring were included. All met the inclusion criteria of age >18 years, body mass index <30, apnea-hypopnea index <5, tonsil grade <3, soft-palate length >25 mm, and Friedman tongue position <3 following clinical, endoscopic, and polysomnographic evaluation. Epworth sleepiness scale (ESS) and the snoring-intensity visual analogue scale (VAS) were recorded before and 9 months after the implant. Four subjects with extruded implants were excluded; the remaining 36 subjects were divided into two groups, Group I and Group II, with uvular lengths of ≤15 mm and >15 mm, respectively. The study assessed and compared subjective outcome measures including the partner's satisfaction (PS), partner's reported improvement (PRI), 50% VAS and ESS reduction, and subjective success (SS) defined as 50% VAS reduction. The Student t test, χ(2) test, and logistic regression models were used for statistical evaluation. SS (50% VAS reduction), PS, PRI, and 50% ESS reduction were significantly higher in Group I (P < .001, P = .0257, P = .027, P < .001). The overall SS, PRI, PS, and 50% ESS reduction were 33%, 78%, 50%, and 50%, respectively. The uvular length was found to be the determinant factor of SS (P = .005; odds ratio = 0.75), PRI (P = .039; odds ratio = 0.83), and 50% ESS reduction (P = .038; odds ratio: 0.84) following implant insertion through stepwise logistic regression analysis. Excess uvular length (>15 mm) is an important anatomic feature decreasing the efficacy of palatal implants in snoring, and additional measures, such as uvulectomy, should be considered simultaneously for better outcomes (level 4). Copyright © 2011 The American Laryngological, Rhinological, and Otological Society, Inc.

Ventilator-associated pneumonia, like real estate: location really matters.

PubMed

Eckert, Matthew J; Davis, Kimberly A; Reed, R Lawrence; Esposito, Thomas J; Santaniello, John M; Poulakidas, Stathis; Gamelli, Richard L; Luchette, Fred A

2006-01-01

Previous work has demonstrated an increased risk of ventilator-associated pneumonia (VAP) in trauma patients after prehospital (field) intubation as compared with emergency department (ED) intubations. However, this population was not compared with patients intubated as inpatients, making data interpretation difficult. We sought to further examine predictors for the development of VAP after trauma. A 10-year retrospective review of all patients mechanically ventilated greater than 24 hours after injury was performed. In all, 1,628 patients were identified, of which 1,213 (75%) were intubated as inpatients and 415 were emergently intubated (353 ED, 62 field). Overall, those intubated emergently were younger (p = 0.03) and less injured as seen by higher Glasgow Coma Scale scores (p = 0.0002), lower Injury Severity Scores (p = 0.01) and higher Revised Trauma Scores (p < 0.0001). Despite a lower injury severity, those patients emergently intubated were more likely to develop pneumonia as 22% of ED intubations and 15% of field intubations developed pneumonia, as compared with the inpatient rate of 6.5%. Pneumonia after field intubation was more likely to be community-acquired (p < 0.0001) with a significantly lower percentage of infecting enteric gram-negative rods (p < 0.0001) as compared with the inpatient and ED groups. Forward logistic regression analysis (with VAP = 1) identified inpatient intubation as protective against VAP (odds ratio 0.28, 95% CI = 0.2-0.4). Backwards logistic regression analysis further identified both field airway (odds ratio 2.29, 95% CI = 1.1-4.9) and ED airway (odds ratio 3.61, 95% CI = 2.5-5.2) as predictive of VAP. Compared with a population of trauma patients as inpatients, and excluding those patients mechanically ventilated less than 24 hours, patients intubated in the ED or field have a higher incidence of pneumonia, despite equivalent or lower injury severity.

YouTube videos as a source of medical information during the Ebola hemorrhagic fever epidemic.

PubMed

Nagpal, Sajan Jiv Singh; Karimianpour, Ahmadreza; Mukhija, Dhruvika; Mohan, Diwakar; Brateanu, Andrei

2015-01-01

The content and quality of medical information available on video sharing websites such as YouTube is not known. We analyzed the source and quality of medical information about Ebola hemorrhagic fever (EHF) disseminated on YouTube and the video characteristics that influence viewer behavior. An inquiry for the search term 'Ebola' was made on YouTube. The first 100 results were arranged in decreasing order of "relevance" using the default YouTube algorithm. Videos 1-50 and 51-100 were allocated to a high relevance (HR), and a low relevance (LR) video group, respectively. Multivariable logistic regression models were used to assess the predictors of a video being included in the HR vs. LR groups. Fourteen videos were excluded because they were parodies, songs or stand-up comedies (n = 11), not in English (n = 2) or a remaining part of a previous video (n = 1). Two scales, the video information and quality and index and the medical information and content index (MICI) assessed the overall quality, and the medical content of the videos, respectively. There were no videos from hospitals or academic medical centers. Videos in the HR group had a higher median number of views (186,705 vs. 43,796, p < 0.001), more 'likes' (1119 vs. 224, p < 0.001), channel subscriptions (208 vs. 32, p < 0.001), and 'shares' (519 vs. 98, p < 0.001). Multivariable logistic regression showed that only the 'clinical symptoms' component of the MICI scale was associated with a higher likelihood of a video being included in the HR vs. LR group.(OR 1.86, 95 % CI 1.06-3.28, p = 0.03). YouTube videos presenting clinical symptoms of infectious diseases during epidemics are more likely to be included in the HR group and influence viewers behavior.

[Lower lymphocyte response in severe cases of acute bronchiolitis due to respiratory syncytial virus].

PubMed

Ramos-Fernández, José Miguel; Moreno-Pérez, David; Antúnez-Fernández, Cristina; Milano-Manso, Guillermo; Cordón-Martínez, Ana María; Urda-Cardona, Antonio

2018-06-01

Acute bronchiolitis (AB) of the infant has a serious outcome in 6-16% of the hospital admitted cases. Its pathogenesis and evolution is related to the response of the T lymphocytes. The objective of the present study is to determine if the lower systemic lymphocytic response is related to a worse outcome of AB in hospitalised infants. Retrospective observational-analytical study of cases-controls nested in a cohort of patients admitted due to RSV-AB between the period from October 2010 to March 2015. Those with a full blood count in the first 48hours of respiratory distress were included. Infants with underlying disease, bacterial superinfection, and premature infants <32 weeks of gestation were excluded. The main dichotomous variable was PICU admission. Other variables were: gender, age, post-menstrual age, gestational and post-natal tobacco exposure, admission month, type of lactation, and days of onset of respiratory distress. Lymphocyte counts were categorised by quartiles. Bivariate analysis was performed with the main variable and then by logistic regression to analyse confounding factors. The study included 252 infants, of whom 6.6% (17) required PICU admission. The difference in mean±SD of lymphocytes for patients admitted to and not admitted to PICU was 4,044±1755 and 5,035±1786, respectively (Student-t test, P<.05). An association was found between PICU admission and lymphocyte count <3700/ml (Chi-squared, P=.019; OR: 3.2) and it was found to be maintained in the logistic regression, regardless of age and all other studied factors (Wald 4.191 P=.041, OR: 3.8). A relationship was found between lymphocytosis <3700/ml in the first days of respiratory distress and a worse outcome in previously healthy infants <12 months and gestational age greater than 32 weeks with RSV-AB. Copyright © 2017 Asociación Española de Pediatría. Publicado por Elsevier España, S.L.U. All rights reserved.

Low oxygen saturation is associated with pre-hospital mortality among non-traumatic patients using emergency medical services: A national database of Thailand.

PubMed

Sittichanbuncha, Yuwares; Savatmongkorngul, Sorrawit; Jawroongrit, Puchong; Sawanyawisuth, Kittisak

2015-09-01

Pre-hospital emergency medical services are an important network for Emergency Medicine. It has been shown to reduce morbidity and mortality of patients by medical procedures. The Thai government established pre-hospital emergency medical services in 2008 to improve emergency medical care. Since then, there are limited data at the national level on mortality rates with pre-hospital care and the risk factors associated with mortality in non-traumatic patients. To study the pre-hospital mortality rate and factors associated with mortality in non-traumatic patients using the emergency medical service in Thailand. This study retrieved medical data from the National Institute for Emergency Medicine, NIEMS. The inclusion criteria were adult patients above the age of 15 who received medical services by the emergency medical services in Thailand (except Bangkok) from April 1st, 2011 to March 31st, 2012. Patients were excluded if there was no treatment during pre-hospital period, if they were trauma patients, or if their medical data was incomplete. Patients were categorized as either in the survival or non-survival group. Factors associated with mortality were examined by multivariate logistic regression analysis. During the study period, there were 127,602 non-traumatic patients who used pre-hospital emergency medical services in Thailand. Of those, 98,587 patients met the study criteria. For the statistical analyses, there were 66,760 patients who had complete clinical investigations. The mortality rate in this group was 1.89%. Only oxygen saturation was associated with mortality by multivariate logistic regression analysis. The adjusted OR was 0.922 (95% CI 0.8550.994). Low oxygen saturation is significantly associated with pre-hospital mortality in a national database of non-traumatic patients using emergency medical services in Thailand. During pre-hospital care, oxygen level should be monitored and promptly treated. Pulse oximetry devices should be available in all pre-hospital services.

Delivery room continuous positive airway pressure and early pneumothorax in term newborn infants.

PubMed

Clevenger, L; Britton, J R

2017-01-01

To assess the association between delivery room (DR) continuous positive airway pressure (CPAP) and pneumothorax (PT) in term newborns. Two studies performed in community hospitals used data extracted from computerized records of term newborns. Infants receiving positive pressure ventilation in the DR were excluded. Tabulated data included receipt of DR CPAP, PT on the day of birth, and gestational age (GA). In a case-control study from 2001-2013, infants with PT were compared to controls without PT but with respiratory distress or hypoxia persisting from birth for receipt of DR CPAP. In a cohort study from 2014-2016, infants receiving and not receiving DR CPAP were compared for the incidence of PT. In the case-control study, data were obtained for 169 cases and 850 controls. Compared to controls, PT infants were more likely to have received DR CPAP (16.8% vs. 40.2%, respectively, P < 0.001). Logistic regression revealed DR CPAP (Adjusted Odds Ratio [AOR] = 3.30, 95% confidence interval [CI] = 2.31, 4.72, P < 0.001) and GA (AOR = 1.21, 95% CI = 1.05, 1.39, P = 0.009) to be independent predictors of early PT.In the cohort study, PT was observed in 0.1% of 9255 control infants not receiving DR CPAP and 4.8% of 228 infants receiving DR CPAP (P < 0.001). In logistic regression analyses, DR CPAP significantly predicted PT (OR = 59.59, 95% CI = 23.34, 147.12, P < 0.001) and remained a significant predictor of PT after controlling for gestational age. Respiratory conditions treated with CPAP in delivery rooms are associated with increased risk of PT. A cause-and-effect relationship between CPAP and PT cannot be claimed in this study. Further research is needed to better understand this relationship.

Atorvastatin Use Associated With Acute Pancreatitis

PubMed Central

Lai, Shih-Wei; Lin, Cheng-Li; Liao, Kuan-Fu

2016-01-01

Abstract Few data are present in the literature on the relationship between atorvastatin use and acute pancreatitis. The aim of this study was to explore this issue in Taiwan. Using representative claims data established from the Taiwan National Health Insurance Program, this case–control study consisted of 5810 cases aged 20 to 84 years with a first-time diagnosis of acute pancreatitis during the period 1998 to 2011and 5733 randomly selected controls without acute pancreatitis. Both cases and controls were matched by sex, age, comorbidities, and index year of diagnosing acute pancreatitis. Subjects who at least received 1 prescription for other statins or nonstatin lipid-lowering drugs were excluded from the study. If subjects never had 1 prescription for atorvastatin, they were defined as never use of atorvastatin. Current use of atorvastatin was defined as subjects whose last remaining 1 tablet of atorvastatin was noted ≤7 days before the date of diagnosing acute pancreatitis. Late use of atorvastatin was defined as subjects whose last remaining 1 tablet of atorvastatin was noted >7 days before the date of diagnosing acute pancreatitis. The odds ratio with 95% confidence interval of acute pancreatitis associated with atorvastatin use was calculated by using the logistic regression analysis. The logistic regression analysis revealed that the odds ratio of acute pancreatitis was 1.67 for subjects with current use of atorvastatin (95% confidence interval 1.18, 2.38), when compared with subjects with never use of atorvastatin. The odds ratio decreased to 1.15 for those with late use of atorvastatin (95% confidence interval 0.87, 1.52), but without statistical significance. Current use of atorvastatin is associated with the diagnosis of acute pancreatitis. Clinically, clinicians should consider the possibility of atorvastatin-associated acute pancreatitis when patients present with a diagnosis of acute pancreatitis without a definite etiology but are taking atorvastatin. PMID:26886597

Prevalence and factors associated with hyperuricaemia in newly diagnosed and untreated hypertensives in a sub-Saharan African setting.

PubMed

Kamdem, Félicité; Doualla, Marie-Solange; Kemta Lekpa, Fernando; Temfack, Elvis; Ngo Nouga, Yvette; Sontsa Donfack, Olivier; Dzudie, Anastase; Kingue, Samuel

2016-10-01

Few studies have evaluated the link between hyperuricaemia and cardiovascular disease in sub-Saharan Africa. To assess the prevalence of and factors associated with hyperuricaemia among newly diagnosed treatment-naïve hypertensive patients in sub-Saharan Africa. We performed a community-based cross-sectional study from January to December 2012 in Douala, Cameroon (Central Africa). We enrolled newly diagnosed treatment-naïve hypertensive patients, and excluded those with gout or a history of gout. Serum uric acid concentrations were measured by enzymatic colourimetric methods, and hyperuricaemia was defined as a serum uric acid concentration>70IU/mL. Fasting blood sugar concentrations, serum creatinine concentrations and lipid profiles were also measured. Logistic regression was used to study factors associated with hyperuricaemia. We included 839 newly diagnosed treatment-naïve hypertensive patients (427 women and 412 men; mean age 51±11 years; mean serum uric acid concentration 60.5±16.5IU/L). The prevalence of hyperuricaemia was 31.8% (95% confidence interval [CI] 28.7-34.9) and did not differ by sex (132 women vs. 135 men; P=0.56). Multivariable logistic regression identified age>55 years (adjusted odds ratio [AOR] 1.65, 95% CI 1.12-2.29), family history of hypertension (AOR 1.65, 95% CI 1.01-2.67), waist circumference>102cm in men or>88cm in women (AOR 1.60, 95% CI 1.12-2.29), low-density lipoprotein cholesterol>1g/L (AOR 1.33, 95% CI 0.97-1.82) and triglycerides>1.5g/L (AOR 1.63, 95% CI 1.01-2.65) as independently associated with hyperuricaemia. Hyperuricaemia is common among newly diagnosed treatment-naïve hypertensive patients in sub-Saharan Africa and is associated with some components of the metabolic syndrome. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

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.

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.

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.

Validity of Combining History Elements and Physical Examination Tests to Diagnose Patellofemoral Pain.

PubMed

Décary, Simon; Frémont, Pierre; Pelletier, Bruno; Fallaha, Michel; Belzile, Sylvain; Martel-Pelletier, Johanne; Pelletier, Jean-Pierre; Feldman, Debbie; Sylvestre, Marie-Pierre; Vendittoli, Pascal-André; Desmeules, François

2018-04-01

To assess the validity of diagnostic clusters combining history elements and physical examination tests to diagnose or exclude patellofemoral pain (PFP). Prospective diagnostic study. Orthopedic outpatient clinics, family medicine clinics, and community-dwelling. Consecutive patients (N=279) consulting one of the participating orthopedic surgeons (n=3) or sport medicine physicians (n=2) for any knee complaint. Not applicable. History elements and physical examination tests were obtained by a trained physiotherapist blinded to the reference standard: a composite diagnosis including both physical examination tests and imaging results interpretation performed by an expert physician. Penalized logistic regression (least absolute shrinkage and selection operator) was used to identify history elements and physical examination tests associated with the diagnosis of PFP, and recursive partitioning was used to develop diagnostic clusters. Diagnostic accuracy measures including sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios with associated 95% confidence intervals (CIs) were calculated. Two hundred seventy-nine participants were evaluated, and 75 had a diagnosis of PFP (26.9%). Different combinations of history elements and physical examination tests including the age of participants, knee pain location, difficulty descending stairs, patellar facet palpation, and passive knee extension range of motion were associated with a diagnosis of PFP and used in clusters to accurately discriminate between individuals with PFP and individuals without PFP. Two diagnostic clusters developed to confirm the presence of PFP yielded a positive likelihood ratio of 8.7 (95% CI, 5.2-14.6) and 3 clusters to exclude PFP yielded a negative likelihood ratio of .12 (95% CI, .06-.27). Diagnostic clusters combining common history elements and physical examination tests that can accurately diagnose or exclude PFP compared to various knee disorders were developed. External validation is required before clinical use. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Impact of renal dysfunction on outcomes of coronary artery bypass surgery: results from the Society of Thoracic Surgeons National Adult Cardiac Database.

PubMed

Cooper, William A; O'Brien, Sean M; Thourani, Vinod H; Guyton, Robert A; Bridges, Charles R; Szczech, Lynda A; Petersen, Rebecca; Peterson, Eric D

2006-02-28

Although patients with end-stage renal disease are known to be at high risk for mortality after coronary artery bypass graft (CABG) surgery, the impact of lesser degrees of renal impairment has not been well studied. The purpose of this study was to compare outcomes in patients undergoing CABG with a range from normal renal function to dependence on dialysis. We reviewed 483,914 patients receiving isolated CABG from July 2000 to December 2003, using the Society of Thoracic Surgeons National Adult Cardiac Database. Glomerular filtration rate (GFR) was estimated for patients with the use of the Modification of Diet in Renal Disease study formula. Multivariable logistic regression was used to determine the association of GFR with operative mortality and morbidities (stroke, reoperation, deep sternal infection, ventilation >48 hours, postoperative stay >2 weeks) after adjustment for 27 other known clinical risk factors. Preoperative renal dysfunction (RD) was common among CABG patients, with 51% having mild RD (GFR 60 to 90 mL/min per 1.73 m2, excludes dialysis), 24% moderate RD (GFR 30 to 59 mL/min per 1.73 m2, excludes dialysis), 2% severe RD (GFR <30 mL/min per 1.73 m2, excludes dialysis), and 1.5% requiring dialysis. Operative mortality rose inversely with declining renal function, from 1.3% for those with normal renal function to 9.3% for patients with severe RD not on dialysis and 9.0% for those who were dialysis dependent. After adjustment for other covariates, preoperative GFR was one of the most powerful predictors of operative mortality and morbidities. Preoperative RD is common in the CABG population and carries important prognostic importance. Assessment of preoperative renal function should be incorporated into clinical risk assessment and prediction models.

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.

Prevalence of incidental pancreatic cyst on upper endoscopic ultrasound

PubMed Central

Martínez, Belén; Martínez, Juan F.; Aparicio, José R.

2018-01-01

Background: This study aimed to determine the prevalence of incidental pancreatic cysts in patients undergoing upper endoscopic ultrasound without a known pancreatic abnormality. Methods: This prospective study was conducted in two hospitals in Spain and enrolled consecutive patients referred for upper endoscopic ultrasound for a condition unrelated to the pancreas. Patients with a previous pancreatic anomaly, history of acute or chronic pancreatitis, evidence of acute pancreatitis, previous upper gastrointestinal surgery, or chronic abdominal pain suggestive of pancreatic origin were excluded. Univariate logistic regression was performed to evaluate individual covariates and the incidental pancreatic cyst risk. Results: A total of 298 patients were included, of whom 64 had pancreatic cysts (21.5%; 16.9-26.6%). The mean size of the cysts was 6.3±3.7 (range 3-25) mm. Six cysts (2%) were >10 mm and 16 (5.4%) were compatible with branch duct intraductal papillary mucinous neoplasm. The pancreatic cyst prevalence was similar in the two hospitals and increased significantly with age. Conclusion: The prevalence of incidental pancreatic cysts during endoscopic ultrasound was very high in our study population. PMID:29333072

Elevated Levels of Adhesion Proteins Are Associated With Low Ankle-Brachial Index.

PubMed

Berardi, Cecilia; Wassel, Christine L; Decker, Paul A; Larson, Nicholas B; Kirsch, Phillip S; Andrade, Mariza de; Tsai, Michael Y; Pankow, James S; Sale, Michele M; Sicotte, Hugues; Tang, Weihong; Hanson, Naomi Q; McDermott, Mary M; Criqui, Michael H; Allison, Michael A; Bielinski, Suzette J

2017-04-01

Inflammation plays a pivotal role in peripheral artery disease (PAD). Cellular adhesion proteins mediate the interaction of leukocytes with endothelial cells during inflammation. To determine the association of cellular adhesion molecules with ankle-brachial index (ABI) and ABI category (≤1.0 vs >1.0) in a diverse population, 15 adhesion proteins were measured in the Multi-Ethnic Study of Atherosclerosis (MESA). To assess multivariable associations of each protein with ABI and ABI category, linear and logistic regression was used, respectively. Among 2364 participants, 23 presented with poorly compressible arteries (ABI > 1.4) and were excluded and 261 had ABI ≤ 1.0. Adjusting for traditional risk factors, elevated levels of soluble P-selectin, hepatocyte growth factor, and secretory leukocyte protease inhibitor were associated with lower ABI ( P = .0004, .001, and .002, respectively). Per each standard deviation of protein, we found 26%, 20%, and 19% greater odds of lower ABI category ( P = .001, .01, and .02, respectively). Further investigation into the adhesion pathway may shed new light on biological mechanisms implicated in PAD.

[Potential selection bias in telephone surveys: landline and mobile phones].

PubMed

Garcia-Continente, Xavier; Pérez-Giménez, Anna; López, María José; Nebot, Manel

2014-01-01

The increasing use of mobile phones in the last decade has decreased landline telephone coverage in Spanish households. This study aimed to analyze sociodemographic characteristics and health indicators by type of telephone service (mobile phone vs. landline or landline and mobile phone). Two telephone surveys were conducted in Spanish samples (February 2010 and February 2011). Multivariate logistic regression analyses were performed to analyze differences in the main sociodemographic characteristics and health indicators according to the type of telephone service available in Spanish households. We obtained 2027 valid responses (1627 landline telephones and 400 mobile phones). Persons contacted through a mobile phone were more likely to be a foreigner, to belong to the manual social class, to have a lower educational level, and to be a smoker than those contacted through a landline telephone. The profile of the population that has only a mobile phone differs from that with a landline telephone. Therefore, telephone surveys that exclude mobile phones could show a selection bias. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

Different cognitive profiles for single compared with recurrent fallers without dementia.

PubMed

Anstey, Kaarin J; Wood, Joanne; Kerr, Graham; Caldwell, Haley; Lord, Stephen R

2009-07-01

Relationships between self-reported retrospective falls and cognitive measures (executive function, reaction time [RT], processing speed, working memory, visual attention) were examined in a population based sample of older adults (n = 658). Two of the choice RT tests involved inhibiting responses to either targets of a specific color or location with hand and foot responses. Potentially confounding demographic variables, medical conditions, and postural sway were controlled for in logistic regression models, excluding participants with possible cognitive impairment. A factor analysis of cognitive measures extracted factors measuring RT, accuracy and inhibition, and visual search. Single fallers did not differ from nonfallers in terms of health, sway or cognitive function, except that they performed worse on accuracy and inhibition. In contrast, recurrent fallers performed worse than nonfallers on all measures. Results suggest that occasional falls in late life may be associated with subtle age-related changes in the prefrontal cortex leading to failures of executive control, whereas recurrent falling may result from more advanced brain ageing that is associated with generalized cognitive decline. 2009 American Psychological Association

A classification tree approach for improving the utilization of flow cytometry testing of blood specimens for B-cell non-Hodgkin lymphoproliferative disorders.

PubMed

Healey, Ryan; Naugler, Christopher; de Koning, Lawrence; Patel, Jay L

2015-01-01

We sought to improve the diagnostic efficiency of flow cytometry investigation on blood by developing data-driven ordering guidelines. Our goal was to improve flow cytometry utilization by decreasing negative testing, therefore reducing healthcare costs. We investigated several laboratory tests performed alongside flow cytometry to identify biomarkers useful in excluding non-leukemic bloods. Test results and patient demographic features were subjected to receiver-operator characteristic (ROC) curve, logistic regression and classification tree analyses to find significant predictors and develop decision rules. Our data show that, in the absence of a compelling clinical indication, flow cytometry testing is largely non-informative on bloods from patients less than 50 years of age having an absolute lymphocyte count (ALC) below 5.0 × 10(9)/L. For patients over age 50 having an ALC below this value, a ferritin value above 450 μg/L is counter-indicative of B-cell clonality. Using these guidelines, 26% of cases were correctly predicted as negative with greater than 97% accuracy.

Impact of screening for metabolic syndrome on the evaluation of obese living kidney donors.

PubMed

Marcusa, Daniel P; Schaubel, Douglas E; Woodside, Kenneth J; Sung, Randall S

2018-01-01

We report our experience with metabolic syndrome screening for obese living kidney donor candidates to mitigate the long-term risk of CKD. We retrospectively reviewed 814 obese (BMI≥30) and 993 nonobese living kidney donor evaluations over 12 years. Using logistic regression, we explored interactions between social/clinical variables and candidate acceptance before and after policy implementation. Obese donor candidate acceptance decreased after metabolic syndrome screening began (56.3%, 46.3%, p < 0.01), while nonobese candidate acceptance remained similar (59.6%, 59.2%, p = 0.59). Adjusting for age, gender, race, BMI, and number of prior evaluations, acceptance of obese candidates decreased significantly more than nonobese (p = 0.025). In candidates without metabolic syndrome, there was no significant change in how age, sex, race, or BMI affected a donor candidate's probability of acceptance. Metabolic syndrome screening is a simple stratification tool for centers with liberal absolute BMI cut-offs to exclude potentially higher-risk obese candidates. Copyright © 2017 Elsevier Inc. All rights reserved.

[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.

Regression from Game-Oriented to Traditional School

ERIC Educational Resources Information Center

Westin, Thomas; Wiklund, Mats; Mozelius, Peter; Norberg, Lena

2015-01-01

Pupils in Sweden are socialized in commercial off-the-shelf games, and, therefore, game-oriented formal education can constitute a foundation for further socialization of pupils excluded in school. However, digital illiteracy and traditional views among school staff forced a regression from the game-oriented formal trial education in this study…

NTproBNP in insulin-resistance mediated conditions: overweight/obesity, metabolic syndrome and diabetes. The population-based Casale Monferrato Study.

PubMed

Baldassarre, Stefano; Fragapani, Salvatore; Panero, Antonio; Fedele, Debora; Pinach, Silvia; Lucchiari, Manuela; Vitale, Anna Rita; Mengozzi, Giulio; Gruden, Gabriella; Bruno, Graziella

2017-09-25

NTproBNP and BNP levels are reduced in obese subjects, but population-based data comparing the pattern of this relationship in the full spectrum of insulin-resistance mediated conditions, overweight/obesity, metabolic syndrome and diabetes, are limited. The study-base were 3244 individuals aged 45-74 years, none of whom had heart failure, 1880 without diabetes and 1364 with diabetes, identified as part of two surveys of the population-based Casale Monferrato Study. All measurements were centralized. We examined with multiple linear regression and cubic regression splines the relationship between NTproBNP and BMI, independently of known risk factors and confounders. A logistic regression analysis was also performed to assess the effect of overweight/obesity (BMI ≥ 25 kg/m 2 ), diabetes and metabolic syndrome on NTproBNP values. Out of the overall cohort of 3244 people, overweight/obesity was observed in 1118 (59.4%) non-diabetic and 917 (67.2%) diabetic subjects, respectively. In logistic regression, compared to normal weight individuals, those with a BMI ≥ 25 kg/m 2 had a OR of 0.70 (95% CI 0.56-0.87) of having high NTproBNP values, independently of diabetes. As interaction between diabetes and NTproBNP was evident (p < 0.001), stratified analyses were performed. Diabetes either alone or combined with overweight/obesity or metabolic syndrome enhanced fourfold and over the OR of having high NTproBNP levels, while the presence of metabolic syndrome alone had a more modest effect (OR 1.54, 1.18-2.01) even after having excluded individuals with CVD. In the non-diabetic cohort, obesity/overweight and HOMA-IR ≥ 2.0 decreased to a similar extent the ORs of high NTproBNP [0.76 (0.60-0.95) and 0.74 (0.59-0.93)], but the association between overweight/obesity and NTproBNP was no longer significant after the inclusion into the model of HOMA-IR, whereas CRP > 3 mg/dl conferred a fully adjusted OR of 0.65 (0.49-0.86). NT-proBNP levels are lower in overweight/obesity, even in those with diabetes. Both insulin-resistance and chronic low-grade inflammation are involved in this relationship. Further intervention studies are required to clarify the potential role of drugs affecting the natriuretic peptides system on body weight and risk of diabetes.

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.

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

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.

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.

41 CFR 101-30.300 - Scope of subpart.

Code of Federal Regulations, 2013 CFR

2013-07-01

....3-Cataloging Items of Supply § 101-30.300 Scope of subpart. This subpart prescribes the types of items to be cataloged, the types of items to be excluded from the Federal Catalog System, the responsibilities for catalog data preparation and transmission to the Defense Logistics Services Center (DLSC), and...

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

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.

Prevalence of frailty in middle-aged and older community-dwelling Europeans living in 10 countries.

PubMed

Santos-Eggimann, Brigitte; Cuénoud, Patrick; Spagnoli, Jacques; Junod, Julien

2009-06-01

Frailty is an indicator of health status in old age. Its frequency has been described mainly for North America; comparable data from other countries are lacking. Here we report on the prevalence of frailty in 10 European countries included in a population-based survey. Cross-sectional analysis of 18,227 randomly selected community-dwelling individuals 50 years of age and older, enrolled in the Survey of Health, Aging and Retirement in Europe (SHARE) in 2004. Complete data for assessing a frailty phenotype (exhaustion, shrinking, weakness, slowness, and low physical activity) were available for 16,584 participants. Prevalences of frailty and prefrailty were estimated for individuals 50-64 years and 65 years of age and older from each country. The latter group was analyzed further after excluding disabled individuals. We estimated country effects in this subset using multivariate logistic regression models, controlling first for age, gender, and then demographics and education. The proportion of frailty (three to five criteria) or prefrailty (one to two criteria) was higher in southern than in northern Europe. International differences in the prevalences of frailty and prefrailty for 65 years and older group persisted after excluding the disabled. Demographic characteristics did not account for international differences; however, education was associated with frailty. Controlling for education, age and gender diminished the effects of residing in Italy and Spain. A higher prevalence of frailty in southern countries is consistent with previous findings of a north-south gradient for other health indicators in SHARE. Our data suggest that socioeconomic factors like education contribute to these differences in frailty and prefrailty.

A population based study on the night-time effect in trauma care.

PubMed

Di Bartolomeo, Stefano; Marino, Massimiliano; Ventura, Chiara; Trombetti, Susanna; De Palma, Rossana

2014-10-01

The so-called off hour effect-that is, increased mortality for patients admitted outside normal working hours-has never been demonstrated in trauma care. However, most of the studies excluded transferred cases. Because these patients are a special challenge for trauma systems, we hypothesised that their processes of care could be more sensitive to the off hour effect. The study design was retrospective, cohort and population based. We compared the mortality of all patients by daytime and night-time admittance to hospitals in an Italian region, with 4.5 million inhabitants, following a major injury in 2011. Logistic regression was used, adjusted for demographics and severity of injury (TMPM-ICD9), and stratified by transfer status. 1940 major trauma cases were included; 105 were acutely transferred. Night-time admission had a significant pejorative effect on mortality in the adjusted analysis (OR=1.49; 95% CI 1.05 to 2.11). This effect was most evident in transferred cases (OR=3.71; 95% CI 1.11 to 12.43). The night-time effect in trauma care was demonstrated for the first time and was maximal in transferred cases. This may explain why it was not found in previous studies where these patients were mostly excluded. Also, the use of population based data-whereby patients not accessing trauma centre care and presumably receiving poorer care were included-may have contributed to the findings. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Determinants of Medical System Delay in the Diagnosis of Colorectal Cancer within the Veteran Affairs Health System

PubMed Central

Fisher, Deborah A.; Zullig, Leah L.; Grambow, Steven C.; Abbott, David H.; Sandler, Robert S.; Fletcher, Robert H.; El-Serag, Hashem B.; Provenzale, Dawn

2010-01-01

Background & Aims The goals of this study were to evaluate determinants of the time in the medical system until a colorectal cancer diagnosis and to explore characteristics associated with stage at diagnosis. Methods We examined medical records and survey data for 468 patients with colorectal cancer at 15 Veterans Affairs medical centers. Patients were classified as screen-detected, bleeding-detected, or other (resulting from the evaluation of another medical concern). Patients who presented emergently with obstruction or perforation were excluded. We used Cox proportional hazards models to determine predictors of time in the medical system until diagnosis. Logistic regression models were used to determine predictors of stage at diagnosis. Results We excluded 21 subjects who presented emergently leaving 447 subjects; the mean age was 67 years and 98% were male, 66% Caucasian, and 43% stage I or II. Diagnosis was by screening for 39%, bleeding symptoms for 27% and other for 34%. The median times to diagnosis were 73–91 days and not significantly different by diagnostic category. In the multivariable model for time-to-diagnosis, older age, having comorbidities, and Atlantic region were associated with a longer time to diagnosis. In the multivariable model for stage-at-diagnosis only diagnostic category was associated with stage; screen-detected category was associated with decreased risk of late stage cancer. Conclusions Our results point to several factors associated with a longer time from the initial clinical event until diagnosis. This increased time in the health care system did not clearly translate into more advanced disease at diagnosis. PMID:20238248

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…

Factors influencing the postoperative use of analgesics in dogs and cats by Canadian veterinarians.

PubMed

Dohoo, S E; Dohoo, I R

1996-09-01

Four hundred and seventeen Canadian veterinarians were surveyed to determine their postoperative use of analgesics in dogs and cats following 6 categories of surgeries, and their opinion toward pain perception and perceived complications associated with the postoperative use of potent opioid analgesics. Three hundred and seventeen (76%) returned the questionnaire. An analgesic user was defined as a veterinarian who administers analgesics to at least 50% of dogs or 50% of cats following abdominal surgery, excluding ovariohysterectomy. The veterinarians responding exhibited a bimodal distribution of analgesic use, with 49.5% being defined as analgesic users. These veterinarians tended to use analgesics in 100% of animals following abdominal surgery. Veterinarians defined as analgesic nonusers rarely used postoperative analgesics following any abdominal surgery. Pain perception was defined as the average of pain rankings (on a scale of 1 to 10) following abdominal surgery, or the value for dogs or cats if the veterinarian worked with only 1 of the 2 species. Maximum concern about the risks associated with the postoperative use of potent opioid agonists was defined as the highest ranking assigned to any of the 7 risks evaluated in either dogs or cats. Logistic regression analysis identified the pain perception score and the maximum concern regarding the use of potent opioid agonists in the postoperative period as the 2 factors that distinguished analgesic users from analgesic nonusers. This model correctly classified 68% of veterinarians as analgesic users or nonusers. Linear regression analysis identified gender and the presence of an animal health technologist in the practice as the 2 factors that influenced pain perception by veterinarians. Linear regression analysis identified working with an animal health technologist, graduation within the past 10 years, and attendance at continuing education as factors that influenced maximum concern about the postoperative use of opioid agonists.

Hazard Regression Models of Early Mortality in Trauma Centers

PubMed Central

Clark, David E; Qian, Jing; Winchell, Robert J; Betensky, Rebecca A

2013-01-01

Background Factors affecting early hospital deaths after trauma may be different from factors affecting later hospital deaths, and the distribution of short and long prehospital times may vary among hospitals. Hazard regression (HR) models may therefore be more useful than logistic regression (LR) models for analysis of trauma mortality, especially when treatment effects at different time points are of interest. Study Design We obtained data for trauma center patients from the 2008–9 National Trauma Data Bank (NTDB). Cases were included if they had complete data for prehospital times, hospital times, survival outcome, age, vital signs, and severity scores. Cases were excluded if pulseless on admission, transferred in or out, or ISS<9. Using covariates proposed for the Trauma Quality Improvement Program and an indicator for each hospital, we compared LR models predicting survival at 8 hours after injury to HR models with survival censored at 8 hours. HR models were then modified to allow time-varying hospital effects. Results 85,327 patients in 161 hospitals met inclusion criteria. Crude hazards peaked initially, then steadily declined. When hazard ratios were assumed constant in HR models, they were similar to odds ratios in LR models associating increased mortality with increased age, firearm mechanism, increased severity, more deranged physiology, and estimated hospital-specific effects. However, when hospital effects were allowed to vary by time, HR models demonstrated that hospital outliers were not the same at different times after injury. Conclusions HR models with time-varying hazard ratios reveal inconsistencies in treatment effects, data quality, and/or timing of early death among trauma centers. HR models are generally more flexible than LR models, can be adapted for censored data, and potentially offer a better tool for analysis of factors affecting early death after injury. PMID:23036828

Association of cardiovascular system medications with cognitive function and dementia in older adults living in nursing homes in Australia.

PubMed

Liu, Enwu; Dyer, Suzanne M; O'Donnell, Lisa Kouladjian; Milte, Rachel; Bradley, Clare; Harrison, Stephanie L; Gnanamanickam, Emmanuel; Whitehead, Craig; Crotty, Maria

2017-06-01

To examine associations between cardiovascular system medication use with cognition function and diagnosis of dementia in older adults living in nursing homes in Australia. As part of a cross-sectional study of 17 Australian nursing homes examining quality of life and resource use, we examined the association between cognitive impairment and cardiovascular medication use (identified using the Anatomical Therapeutic Classification System) using general linear regression and logistic regression models. People who were receiving end of life care were excluded. Participants included 541 residents with a mean age of 85.5 years (± 8.5), a mean Psychogeriatric Assessment Scale-Cognitive Impairment (PAS-Cog) score of 13.3 (± 7.7), a prevalence of cardiovascular diseases of 44% and of hypertension of 47%. Sixty-four percent of participants had been diagnosed with dementia and 72% had received cardiovascular system medications within the previous 12 months. Regression models demonstrated the use of cardiovascular medications was associated with lower (better) PAS-Cog scores [Coefficient (β) = -3.7; 95% CI: -5.2 to -2.2; P < 0.0001] and a lower probability of a dementia diagnosis (OR = 0.44; 95% CI: 0.26 to 0.75, P = 0.0022). Analysis by subgroups of medications showed cardiac therapy medications (C01), beta blocking agents (C07), and renin-angiotensin system agents (C09) were associated with lower PAS-Cog scores (better cognition) and lower dementia diagnosis probability. This analysis has demonstrated an association between greater cardiovascular system medication use and better cognitive status among older adults living in nursing homes. In this population, there may be differential access to health care and treatment of cardiovascular risk factors. This association warrants further investigation in large cohort studies.

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.

Analysis of an Environmental Exposure Health Questionnaire in a Metropolitan Minority Population Utilizing Logistic Regression and Support Vector Machines

PubMed Central

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D.; Hood, Darryl B.; Skelton, Tyler

2014-01-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire. PMID:23395953

An ultra low power feature extraction and classification system for wearable seizure detection.

PubMed

Page, Adam; Pramod Tim Oates, Siddharth; Mohsenin, Tinoosh

2015-01-01

In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. The input to each machine learning classifier is a 198 feature vector containing 9 features for each of the 22 EEG channels obtained over 1-second windows. All classifiers were able to obtain F1 scores over 80% and onset sensitivity of 100% when tested on 10 patients. Among five different classifiers that were explored, logistic regression (LR) proved to have minimum hardware complexity while providing average F-1 score of 91%. Both ASIC and FPGA implementations of logistic regression are presented and show the smallest area, power consumption, and the lowest latency when compared to the previous work.

The arcsine is asinine: the analysis of proportions in ecology.

PubMed

Warton, David I; Hui, Francis K C

2011-01-01

The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

PubMed

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

Prescription-drug-related risk in driving: comparing conventional and lasso shrinkage logistic regressions.

PubMed

Avalos, Marta; Adroher, Nuria Duran; Lagarde, Emmanuel; Thiessard, Frantz; Grandvalet, Yves; Contrand, Benjamin; Orriols, Ludivine

2012-09-01

Large data sets with many variables provide particular challenges when constructing analytic models. Lasso-related methods provide a useful tool, although one that remains unfamiliar to most epidemiologists. We illustrate the application of lasso methods in an analysis of the impact of prescribed drugs on the risk of a road traffic crash, using a large French nationwide database (PLoS Med 2010;7:e1000366). In the original case-control study, the authors analyzed each exposure separately. We use the lasso method, which can simultaneously perform estimation and variable selection in a single model. We compare point estimates and confidence intervals using (1) a separate logistic regression model for each drug with a Bonferroni correction and (2) lasso shrinkage logistic regression analysis. Shrinkage regression had little effect on (bias corrected) point estimates, but led to less conservative results, noticeably for drugs with moderate levels of exposure. Carbamates, carboxamide derivative and fatty acid derivative antiepileptics, drugs used in opioid dependence, and mineral supplements of potassium showed stronger associations. Lasso is a relevant method in the analysis of databases with large number of exposures and can be recommended as an alternative to conventional strategies.

Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai

NASA Astrophysics Data System (ADS)

Shafizadeh-Moghadam, Hossein; Helbich, Marco

2015-03-01

The rapid growth of megacities requires special attention among urban planners worldwide, and particularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this will bring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use change models - and in particular non-spatial logistic regression models (LR) and auto-logistic regression models (ALR) - for the Mumbai region over the period 1973-2010, in order to determine the drivers behind spatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variations in driving-forces across space. The study comes to two main conclusions. First, both global models suggest similar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimated coefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporal and spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth processes, and so can assist context-related planning and policymaking activities when seeking to secure a sustainable urban future.

Prediction of cold and heat patterns using anthropometric measures based on machine learning.

PubMed

Lee, Bum Ju; Lee, Jae Chul; Nam, Jiho; Kim, Jong Yeol

2018-01-01

To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns. Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures. In women, the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men, the best indicator was BMI. In experiments using a combination of measures, the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression, and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression. Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.

Application of classification tree and logistic regression for the management and health intervention plans in a community-based study.

PubMed

Teng, Ju-Hsi; Lin, Kuan-Chia; Ho, Bin-Shenq

2007-10-01

A community-based aboriginal study was conducted and analysed to explore the application of classification tree and logistic regression. A total of 1066 aboriginal residents in Yilan County were screened during 2003-2004. The independent variables include demographic characteristics, physical examinations, geographic location, health behaviours, dietary habits and family hereditary diseases history. Risk factors of cardiovascular diseases were selected as the dependent variables in further analysis. The completion rate for heath interview is 88.9%. The classification tree results find that if body mass index is higher than 25.72 kg m(-2) and the age is above 51 years, the predicted probability for number of cardiovascular risk factors > or =3 is 73.6% and the population is 322. If body mass index is higher than 26.35 kg m(-2) and geographical latitude of the village is lower than 24 degrees 22.8', the predicted probability for number of cardiovascular risk factors > or =4 is 60.8% and the population is 74. As the logistic regression results indicate that body mass index, drinking habit and menopause are the top three significant independent variables. The classification tree model specifically shows the discrimination paths and interactions between the risk groups. The logistic regression model presents and analyses the statistical independent factors of cardiovascular risks. Applying both models to specific situations will provide a different angle for the design and management of future health intervention plans after community-based study.

Risk factors for pedicled flap necrosis in hand soft tissue reconstruction: a multivariate logistic regression analysis.

PubMed

Gong, Xu; Cui, Jianli; Jiang, Ziping; Lu, Laijin; Li, Xiucun

2018-03-01

Few clinical retrospective studies have reported the risk factors of pedicled flap necrosis in hand soft tissue reconstruction. The aim of this study was to identify non-technical risk factors associated with pedicled flap perioperative necrosis in hand soft tissue reconstruction via a multivariate logistic regression analysis. For patients with hand soft tissue reconstruction, we carefully reviewed hospital records and identified 163 patients who met the inclusion criteria. The characteristics of these patients, flap transfer procedures and postoperative complications were recorded. Eleven predictors were identified. The correlations between pedicled flap necrosis and risk factors were analysed using a logistic regression model. Of 163 skin flaps, 125 flaps survived completely without any complications. The pedicled flap necrosis rate in hands was 11.04%, which included partial flap necrosis (7.36%) and total flap necrosis (3.68%). Soft tissue defects in fingers were noted in 68.10% of all cases. The logistic regression analysis indicated that the soft tissue defect site (P = 0.046, odds ratio (OR) = 0.079, confidence interval (CI) (0.006, 0.959)), flap size (P = 0.020, OR = 1.024, CI (1.004, 1.045)) and postoperative wound infection (P < 0.001, OR = 17.407, CI (3.821, 79.303)) were statistically significant risk factors for pedicled flap necrosis of the hand. Soft tissue defect site, flap size and postoperative wound infection were risk factors associated with pedicled flap necrosis in hand soft tissue defect reconstruction. © 2017 Royal Australasian College of Surgeons.

A regularization corrected score method for nonlinear regression models with covariate error.

PubMed

Zucker, David M; Gorfine, Malka; Li, Yi; Tadesse, Mahlet G; Spiegelman, Donna

2013-03-01

Many regression analyses involve explanatory variables that are measured with error, and failing to account for this error is well known to lead to biased point and interval estimates of the regression coefficients. We present here a new general method for adjusting for covariate error. Our method consists of an approximate version of the Stefanski-Nakamura corrected score approach, using the method of regularization to obtain an approximate solution of the relevant integral equation. We develop the theory in the setting of classical likelihood models; this setting covers, for example, linear regression, nonlinear regression, logistic regression, and Poisson regression. The method is extremely general in terms of the types of measurement error models covered, and is a functional method in the sense of not involving assumptions on the distribution of the true covariate. We discuss the theoretical properties of the method and present simulation results in the logistic regression setting (univariate and multivariate). For illustration, we apply the method to data from the Harvard Nurses' Health Study concerning the relationship between physical activity and breast cancer mortality in the period following a diagnosis of breast cancer. Copyright © 2013, The International Biometric Society.

Logistic Mixed Models to Investigate Implicit and Explicit Belief Tracking

PubMed Central

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. PMID:27853440

Carotid artery intima-media complex thickening in patients with relatively long-surviving type 1 diabetes mellitus.

PubMed

Distiller, Larry A; Joffe, Barry I; Melville, Vanessa; Welman, Tania; Distiller, Greg B

2006-01-01

The factors responsible for premature coronary atherosclerosis in patients with type 1 diabetes are ill defined. We therefore assessed carotid intima-media complex thickness (IMT) in relatively long-surviving patients with type 1 diabetes as a marker of atherosclerosis and correlated this with traditional risk factors. Cross-sectional study of 148 patients with relatively long-surviving (>18 years) type 1 diabetes (76 men and 72 women) attending the Centre for Diabetes and Endocrinology, Johannesburg. The mean common carotid artery IMT and presence or absence of plaque was evaluated by high-resolution B-mode ultrasound. Their median age was 48 years and duration of diabetes 26 years (range 18-59 years). Traditional risk factors (age, duration of diabetes, glycemic control, hypertension, smoking and lipoprotein concentrations) were recorded. Three response variables were defined and modeled. Standard multiple regression was used for a continuous IMT variable, logistic regression for the presence/absence of plaque and ordinal logistic regression to model three categories of "risk." The median common carotid IMT was 0.62 mm (range 0.44-1.23 mm) with plaque detected in 28 cases. The multiple regression model found significant associations between IMT and current age (P=.001), duration of diabetes (P=.033), BMI (P=.008) and diagnosed hypertension (P=.046) with HDL showing a protective effect (P=.022). Current age (P=.001) and diagnosed hypertension (P=.004), smoking (P=.008) and retinopathy (P=.033) were significant in the logistic regression model. Current age was also significant in the ordinal logistic regression model (P<.001), as was total cholesterol/HDL ratio (P<.001) and mean HbA(1c) concentration (P=.073). The major factors influencing common carotid IMT in patients with relatively long-surviving type 1 diabetes are age, duration of diabetes, existing hypertension and HDL (protective) with a relatively minor role ascribed to relatively long-standing glycemic control.

What is the best strategy for investigating abnormal liver function tests in primary care? Implications from a prospective study.

PubMed

Lilford, Richard J; Bentham, Louise M; Armstrong, Matthew J; Neuberger, James; Girling, Alan J

2013-06-20

Evaluation of predictive value of liver function tests (LFTs) for the detection of liver-related disease in primary care. A prospective observational study. 11 UK primary care practices. Patients (n=1290) with an abnormal eight-panel LFT (but no previously diagnosed liver disease). Patients were investigated by recording clinical features, and repeating LFTs, specific tests for individual liver diseases, and abdominal ultrasound scan. Patients were characterised as having: hepatocellular disease; biliary disease; tumours of the hepato-biliary system and none of the above. The relationship between LFT results and disease categories was evaluated by stepwise regression and logistic discrimination, with adjustment for demographic and clinical factors. True and False Positives generated by all possible LFT combinations were compared with a view towards optimising the choice of analytes in the routine LFT panel. Regression methods showed that alanine aminotransferase (ALT) was associated with hepatocellular disease (32 patients), while alkaline phosphatase (ALP) was associated with biliary disease (12 patients) and tumours of the hepatobiliary system (9 patients). A restricted panel of ALT and ALP was an efficient choice of analytes, comparing favourably with the complete panel of eight analytes, provided that 48 False Positives can be tolerated to obtain one additional True Positive. Repeating a complete panel in response to an abnormal reading is not the optimal strategy. The LFT panel can be restricted to ALT and ALP when the purpose of testing is to exclude liver disease in primary care.

Declining trends in alcohol consumption among Swedish youth-does the theory of collectivity of drinking cultures apply?

PubMed

Raninen, Jonas; Livingston, Michael; Leifman, Håkan

2014-11-01

To analyse trends in alcohol consumption among young people in Sweden between 2004 and 2012, to test whether the theory of collectivity of drinking cultures is valid for a population of young people and to investigate the impact of an increasing proportion of abstainers on the overall per capita trends. Data were drawn from an annual survey of a nationally representative sample of students in year 11 (17-18 years old). The data covered 9 years and the total sample comprised 36,141 students. Changes in the overall per capita consumption were tested using linear regression on log-transformed data, and changes in abstention rates were tested using logistic regression. The analyses were then continued by calculating average consumption in deciles. Alcohol consumption among year 11 students declined significantly among both boys and girls between 2004 and 2012. These changes were reflected at all levels of consumption, and the same results were found when abstainers were excluded from the analyses. The increasing proportion of abstainers had a minimal effect on the overall decline in consumption; rather, this was driven by a decline in consumption among the heaviest drinkers. The theory of collectivity of drinking cultures seems valid for understanding changes in alcohol consumption among Swedish year 11 students. No support was found for a polarization of alcohol consumption in this nationally representative sample. © The Author 2014. Medical Council on Alcohol and Oxford University Press. All rights reserved.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

Throwing out the baby with the bathwater?: Comparing 2 approaches to implausible values of change in body size.

PubMed

Gray, Christine L; Robinson, Whitney R

2014-07-01

In childhood obesity research, the appearance of height loss, or "shrinkage," indicates measurement error. It is unclear whether a common response--excluding "shrinkers" from analysis--reduces bias. Using data from the National Longitudinal Study of Adolescent Health, we sampled 816 female adolescents (≥17 years) who had attained adult height by 1996 and for whom adult height was consistently measured in 2001 and 2008 ("gold-standard" height). We estimated adolescent obesity prevalence and the association of maternal education with adolescent obesity under 3 conditions: excluding shrinkers (for whom gold-standard height was less than recorded height in 1996), retaining shrinkers, and retaining shrinkers but substituting their gold-standard height. When we estimated obesity prevalence, excluding shrinkers decreased precision without improving validity. When we regressed obesity on maternal education, excluding shrinkers produced less valid and less precise estimates. In some circumstances, ignoring shrinkage is a better strategy than excluding shrinkers.

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

PubMed

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

2016-01-01

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

Multinomial logistic regression in workers' health

NASA Astrophysics Data System (ADS)

Grilo, Luís M.; Grilo, Helena L.; Gonçalves, Sónia P.; Junça, Ana

2017-11-01

In European countries, namely in Portugal, it is common to hear some people mentioning that they are exposed to excessive and continuous psychosocial stressors at work. This is increasing in diverse activity sectors, such as, the Services sector. A representative sample was collected from a Portuguese Services' organization, by applying a survey (internationally validated), which variables were measured in five ordered categories in Likert-type scale. A multinomial logistic regression model is used to estimate the probability of each category of the dependent variable general health perception where, among other independent variables, burnout appear as statistically significant.

Blastocoele expansion degree predicts live birth after single blastocyst transfer for fresh and vitrified/warmed single blastocyst transfer cycles.

PubMed

Du, Qing-Yun; Wang, En-Yin; Huang, Yan; Guo, Xiao-Yi; Xiong, Yu-Jing; Yu, Yi-Ping; Yao, Gui-Dong; Shi, Sen-Lin; Sun, Ying-Pu

2016-04-01

To evaluate the independent effects of the degree of blastocoele expansion and re-expansion and the inner cell mass (ICM) and trophectoderm (TE) grades on predicting live birth after fresh and vitrified/warmed single blastocyst transfer. Retrospective study. Reproductive medical center. Women undergoing 844 fresh and 370 vitrified/warmed single blastocyst transfer cycles. None. Live-birth rate correlated with blastocyst morphology parameters by logistic regression analysis and Spearman correlations analysis. The degree of blastocoele expansion and re-expansion was the only blastocyst morphology parameter that exhibited a significant ability to predict live birth in both fresh and vitrified/warmed single blastocyst transfer cycles respectively by multivariate logistic regression and Spearman correlations analysis. Although the ICM grade was significantly related to live birth in fresh cycles according to the univariate model, its effect was not maintained in the multivariate logistic analysis. In vitrified/warmed cycles, neither ICM nor TE grade was correlated with live birth by logistic regression analysis. This study is the first to confirm that the degree of blastocoele expansion and re-expansion is a better predictor of live birth after both fresh and vitrified/warmed single blastocyst transfer cycles than ICM or TE grade. Copyright © 2016. Published by Elsevier Inc.

Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees.

PubMed

Chung, Yi-Shih

2013-12-01

Factor complexity is a characteristic of traffic crashes. This paper proposes a novel method, namely boosted regression trees (BRT), to investigate the complex and nonlinear relationships in high-variance traffic crash data. The Taiwanese 2004-2005 single-vehicle motorcycle crash data are used to demonstrate the utility of BRT. Traditional logistic regression and classification and regression tree (CART) models are also used to compare their estimation results and external validities. Both the in-sample cross-validation and out-of-sample validation results show that an increase in tree complexity provides improved, although declining, classification performance, indicating a limited factor complexity of single-vehicle motorcycle crashes. The effects of crucial variables including geographical, time, and sociodemographic factors explain some fatal crashes. Relatively unique fatal crashes are better approximated by interactive terms, especially combinations of behavioral factors. BRT models generally provide improved transferability than conventional logistic regression and CART models. This study also discusses the implications of the results for devising safety policies. Copyright © 2012 Elsevier Ltd. All rights reserved.

Estimating time-varying exposure-outcome associations using case-control data: logistic and case-cohort analyses.

PubMed

Keogh, Ruth H; Mangtani, Punam; Rodrigues, Laura; Nguipdop Djomo, Patrick

2016-01-05

Traditional analyses of standard case-control studies using logistic regression do not allow estimation of time-varying associations between exposures and the outcome. We present two approaches which allow this. The motivation is a study of vaccine efficacy as a function of time since vaccination. Our first approach is to estimate time-varying exposure-outcome associations by fitting a series of logistic regressions within successive time periods, reusing controls across periods. Our second approach treats the case-control sample as a case-cohort study, with the controls forming the subcohort. In the case-cohort analysis, controls contribute information at all times they are at risk. Extensions allow left truncation, frequency matching and, using the case-cohort analysis, time-varying exposures. Simulations are used to investigate the methods. The simulation results show that both methods give correct estimates of time-varying effects of exposures using standard case-control data. Using the logistic approach there are efficiency gains by reusing controls over time and care should be taken over the definition of controls within time periods. However, using the case-cohort analysis there is no ambiguity over the definition of controls. The performance of the two analyses is very similar when controls are used most efficiently under the logistic approach. Using our methods, case-control studies can be used to estimate time-varying exposure-outcome associations where they may not previously have been considered. The case-cohort analysis has several advantages, including that it allows estimation of time-varying associations as a continuous function of time, while the logistic regression approach is restricted to assuming a step function form for the time-varying association.

10 CFR 436.31 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... systems, building load simulation models, statistical regression analysis, or some combination of these..., excluding any cogeneration process for other than a federally owned building or buildings or other federally...

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

PubMed

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Regression analysis for solving diagnosis problem of children's health

NASA Astrophysics Data System (ADS)

Cherkashina, Yu A.; Gerget, O. M.

2016-04-01

The paper includes results of scientific researches. These researches are devoted to the application of statistical techniques, namely, regression analysis, to assess the health status of children in the neonatal period based on medical data (hemostatic parameters, parameters of blood tests, the gestational age, vascular-endothelial growth factor) measured at 3-5 days of children's life. In this paper a detailed description of the studied medical data is given. A binary logistic regression procedure is discussed in the paper. Basic results of the research are presented. A classification table of predicted values and factual observed values is shown, the overall percentage of correct recognition is determined. Regression equation coefficients are calculated, the general regression equation is written based on them. Based on the results of logistic regression, ROC analysis was performed, sensitivity and specificity of the model are calculated and ROC curves are constructed. These mathematical techniques allow carrying out diagnostics of health of children providing a high quality of recognition. The results make a significant contribution to the development of evidence-based medicine and have a high practical importance in the professional activity of the author.

[Calculating Pearson residual in logistic regressions: a comparison between SPSS and SAS].

PubMed

Xu, Hao; Zhang, Tao; Li, Xiao-song; Liu, Yuan-yuan

2015-01-01

To compare the results of Pearson residual calculations in logistic regression models using SPSS and SAS. We reviewed Pearson residual calculation methods, and used two sets of data to test logistic models constructed by SPSS and STATA. One model contained a small number of covariates compared to the number of observed. The other contained a similar number of covariates as the number of observed. The two software packages produced similar Pearson residual estimates when the models contained a similar number of covariates as the number of observed, but the results differed when the number of observed was much greater than the number of covariates. The two software packages produce different results of Pearson residuals, especially when the models contain a small number of covariates. Further studies are warranted.

Personality predicts time to remission and clinical status in hypochondriasis during a 6-year follow-up.

PubMed

Greeven, Anja; van Balkom, Anton J L M; Spinhoven, Philip

2014-05-01

We aimed to investigate whether personality characteristics predict time to remission and psychiatric status. The follow-up was at most 6 years and was performed within the scope of a randomized controlled trial that investigated the efficacy of cognitive behavioral therapy, paroxetine, and placebo in hypochondriasis. The Life Chart Interview was administered to investigate for each year if remission had occurred. Personality was assessed at pretest by the Abbreviated Dutch Temperament and Character Inventory. Cox's regression models for recurrent events were compared with logistic regression models. Sixteen (36.4%) of 44 patients achieved remission during the follow-up period. Cox's regression yielded approximately the same results as the logistic regression. Being less harm avoidant and more cooperative were associated with a shorter time to remission and a remitted state after the follow-up period. Personality variables seem to be relevant for describing patients with a more chronic course of hypochondriacal complaints.

A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements

PubMed Central

Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%. PMID:25302338

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.

A hybrid approach of stepwise regression, logistic regression, support vector machine, and decision tree for forecasting fraudulent financial statements.

PubMed

Chen, Suduan; Goo, Yeong-Jia James; Shen, Zone-De

2014-01-01

As the fraudulent financial statement of an enterprise is increasingly serious with each passing day, establishing a valid forecasting fraudulent financial statement model of an enterprise has become an important question for academic research and financial practice. After screening the important variables using the stepwise regression, the study also matches the logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. The study adopts financial and nonfinancial variables to assist in establishment of the forecasting fraudulent financial statement model. Research objects are the companies to which the fraudulent and nonfraudulent financial statement happened between years 1998 to 2012. The findings are that financial and nonfinancial information are effectively used to distinguish the fraudulent financial statement, and decision tree C5.0 has the best classification effect 85.71%.

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy

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

Dean, Jamie A., E-mail: jamie.dean@icr.ac.uk; Wong, Kee H.; Gay, Hiram

Purpose: Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue–sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. Methods and Materials: FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogrammore » data. The reduced dose data were input into functional logistic regression models (functional partial least squares–logistic regression [FPLS-LR] and functional principal component–logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate–response associations, assessed using bootstrapping. Results: The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/−0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/−0.96, 0.79/−0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. Conclusions: FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling.« less

Functional Data Analysis Applied to Modeling of Severe Acute Mucositis and Dysphagia Resulting From Head and Neck Radiation Therapy.

PubMed

Dean, Jamie A; Wong, Kee H; Gay, Hiram; Welsh, Liam C; Jones, Ann-Britt; Schick, Ulrike; Oh, Jung Hun; Apte, Aditya; Newbold, Kate L; Bhide, Shreerang A; Harrington, Kevin J; Deasy, Joseph O; Nutting, Christopher M; Gulliford, Sarah L

2016-11-15

Current normal tissue complication probability modeling using logistic regression suffers from bias and high uncertainty in the presence of highly correlated radiation therapy (RT) dose data. This hinders robust estimates of dose-response associations and, hence, optimal normal tissue-sparing strategies from being elucidated. Using functional data analysis (FDA) to reduce the dimensionality of the dose data could overcome this limitation. FDA was applied to modeling of severe acute mucositis and dysphagia resulting from head and neck RT. Functional partial least squares regression (FPLS) and functional principal component analysis were used for dimensionality reduction of the dose-volume histogram data. The reduced dose data were input into functional logistic regression models (functional partial least squares-logistic regression [FPLS-LR] and functional principal component-logistic regression [FPC-LR]) along with clinical data. This approach was compared with penalized logistic regression (PLR) in terms of predictive performance and the significance of treatment covariate-response associations, assessed using bootstrapping. The area under the receiver operating characteristic curve for the PLR, FPC-LR, and FPLS-LR models was 0.65, 0.69, and 0.67, respectively, for mucositis (internal validation) and 0.81, 0.83, and 0.83, respectively, for dysphagia (external validation). The calibration slopes/intercepts for the PLR, FPC-LR, and FPLS-LR models were 1.6/-0.67, 0.45/0.47, and 0.40/0.49, respectively, for mucositis (internal validation) and 2.5/-0.96, 0.79/-0.04, and 0.79/0.00, respectively, for dysphagia (external validation). The bootstrapped odds ratios indicated significant associations between RT dose and severe toxicity in the mucositis and dysphagia FDA models. Cisplatin was significantly associated with severe dysphagia in the FDA models. None of the covariates was significantly associated with severe toxicity in the PLR models. Dose levels greater than approximately 1.0 Gy/fraction were most strongly associated with severe acute mucositis and dysphagia in the FDA models. FPLS and functional principal component analysis marginally improved predictive performance compared with PLR and provided robust dose-response associations. FDA is recommended for use in normal tissue complication probability modeling. Copyright © 2016 The Author(s). Published by Elsevier Inc. All rights reserved.

Methodologic considerations in the design and analysis of nested case-control studies: association between cytokines and postoperative delirium.

PubMed

Ngo, Long H; Inouye, Sharon K; Jones, Richard N; Travison, Thomas G; Libermann, Towia A; Dillon, Simon T; Kuchel, George A; Vasunilashorn, Sarinnapha M; Alsop, David C; Marcantonio, Edward R

2017-06-06

The nested case-control study (NCC) design within a prospective cohort study is used when outcome data are available for all subjects, but the exposure of interest has not been collected, and is difficult or prohibitively expensive to obtain for all subjects. A NCC analysis with good matching procedures yields estimates that are as efficient and unbiased as estimates from the full cohort study. We present methodological considerations in a matched NCC design and analysis, which include the choice of match algorithms, analysis methods to evaluate the association of exposures of interest with outcomes, and consideration of overmatching. Matched, NCC design within a longitudinal observational prospective cohort study in the setting of two academic hospitals. Study participants are patients aged over 70 years who underwent scheduled major non-cardiac surgery. The primary outcome was postoperative delirium from in-hospital interviews and medical record review. The main exposure was IL-6 concentration (pg/ml) from blood sampled at three time points before delirium occurred. We used nonparametric signed ranked test to test for the median of the paired differences. We used conditional logistic regression to model the risk of IL-6 on delirium incidence. Simulation was used to generate a sample of cohort data on which unconditional multivariable logistic regression was used, and the results were compared to those of the conditional logistic regression. Partial R-square was used to assess the level of overmatching. We found that the optimal match algorithm yielded more matched pairs than the greedy algorithm. The choice of analytic strategy-whether to consider measured cytokine levels as the predictor or outcome-- yielded inferences that have different clinical interpretations but similar levels of statistical significance. Estimation results from NCC design using conditional logistic regression, and from simulated cohort design using unconditional logistic regression, were similar. We found minimal evidence for overmatching. Using a matched NCC approach introduces methodological challenges into the study design and data analysis. Nonetheless, with careful selection of the match algorithm, match factors, and analysis methods, this design is cost effective and, for our study, yields estimates that are similar to those from a prospective cohort study design.

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

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

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System weremore » used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic carcinogenesis (C) were studied by toxicogenomics. • Important genes for H and C were selected by logistic ridge regression analysis. • Amino acid biosynthesis and oxidative responses may be involved in C. • Predictive models for H and C provided 94.8% and 82.7% accuracy, respectively. • The identified genes could be useful for assessment of liver hypertrophy.« less

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks.

PubMed

Zhang, Xingyu; Kim, Joyce; Patzer, Rachel E; Pitts, Stephen R; Patzer, Aaron; Schrager, Justin D

2017-10-26

To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient's reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model. Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN. The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient's reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

PubMed

Cevenini, Gabriele; Barbini, Emanuela; Scolletta, Sabino; Biagioli, Bonizella; Giomarelli, Pierpaolo; Barbini, Paolo

2007-11-22

Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example. Eight models were developed: Bayes linear and quadratic models, k-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively. Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results. Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.

Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey)

NASA Astrophysics Data System (ADS)

Ozdemir, Adnan

2011-07-01

SummaryThe purpose of this study is to produce a groundwater spring potential map of the Sultan Mountains in central Turkey, based on a logistic regression method within a Geographic Information System (GIS) environment. Using field surveys, the locations of the springs (440 springs) were determined in the study area. In this study, 17 spring-related factors were used in the analysis: geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transport capacity index, distance to drainage, distance to fault, drainage density, and fault density map. The coefficients of the predictor variables were estimated using binary logistic regression analysis and were used to calculate the groundwater spring potential for the entire study area. The accuracy of the final spring potential map was evaluated based on the observed springs. The accuracy of the model was evaluated by calculating the relative operating characteristics. The area value of the relative operating characteristic curve model was found to be 0.82. These results indicate that the model is a good estimator of the spring potential in the study area. The spring potential map shows that the areas of very low, low, moderate and high groundwater spring potential classes are 105.586 km 2 (28.99%), 74.271 km 2 (19.906%), 101.203 km 2 (27.14%), and 90.05 km 2 (24.671%), respectively. The interpretations of the potential map showed that stream power index, relative permeability of lithologies, geology, elevation, aspect, wetness index, plan curvature, and drainage density play major roles in spring occurrence and distribution in the Sultan Mountains. The logistic regression approach has not yet been used to delineate groundwater potential zones. In this study, the logistic regression method was used to locate potential zones for groundwater springs in the Sultan Mountains. The evolved model was found to be in strong agreement with the available groundwater spring test data. Hence, this method can be used routinely in groundwater exploration under favourable conditions.

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

Tang, Weidong; Ruan, Feng; Chen, Qi; Chen, Suping; Shao, Xuebo; Gao, Jianbo; Zhang, Mao

2016-07-01

Acute organophosphorus pesticide poisoning (AOPP) is becoming a significant problem and a potential cause of human mortality because of the abuse of organophosphate compounds. This study aims to determine the independent prognostic factors of AOPP by using multivariate logistic regression analysis. The clinical data for 71 subjects with AOPP admitted to our hospital were retrospectively analyzed. This information included the Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, admission blood cholinesterase levels, 6-h post-admission blood cholinesterase levels, cholinesterase activity, blood pH, and other factors. Univariate analysis and multivariate logistic regression analyses were conducted to identify all prognostic factors and independent prognostic factors, respectively. A receiver operating characteristic curve was plotted to analyze the testing power of independent prognostic factors. Twelve of 71 subjects died. Admission blood lactate levels, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, blood pH, and APACHE II scores were identified as prognostic factors for AOPP according to the univariate analysis, whereas only 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, and blood pH were independent prognostic factors identified by multivariate logistic regression analysis. The receiver operating characteristic analysis suggested that post-admission 6-h lactate clearance rates were of moderate diagnostic value. High 6-h post-admission blood lactate levels, low blood pH, and low post-admission 6-h lactate clearance rates were independent prognostic factors identified by multivariate logistic regression analysis. Copyright © 2016 by Daedalus Enterprises.

A local equation for differential diagnosis of β-thalassemia trait and iron deficiency anemia by logistic regression analysis in Southeast Iran.

PubMed

Sargolzaie, Narjes; Miri-Moghaddam, Ebrahim

2014-01-01

The most common differential diagnosis of β-thalassemia (β-thal) trait is iron deficiency anemia. Several red blood cell equations were introduced during different studies for differential diagnosis between β-thal trait and iron deficiency anemia. Due to genetic variations in different regions, these equations cannot be useful in all population. The aim of this study was to determine a native equation with high accuracy for differential diagnosis of β-thal trait and iron deficiency anemia for the Sistan and Baluchestan population by logistic regression analysis. We selected 77 iron deficiency anemia and 100 β-thal trait cases. We used binary logistic regression analysis and determined best equations for probability prediction of β-thal trait against iron deficiency anemia in our population. We compared diagnostic values and receiver operative characteristic (ROC) curve related to this equation and another 10 published equations in discriminating β-thal trait and iron deficiency anemia. The binary logistic regression analysis determined the best equation for best probability prediction of β-thal trait against iron deficiency anemia with area under curve (AUC) 0.998. Based on ROC curves and AUC, Green & King, England & Frazer, and then Sirdah indices, respectively, had the most accuracy after our equation. We suggest that to get the best equation and cut-off in each region, one needs to evaluate specific information of each region, specifically in areas where populations are homogeneous, to provide a specific formula for differentiating between β-thal trait and iron deficiency anemia.

Selenium in irrigated agricultural areas of the western United States

USGS Publications Warehouse

Nolan, B.T.; Clark, M.L.

1997-01-01

A logistic regression model was developed to predict the likelihood that Se exceeds the USEPA chronic criterion for aquatic life (5 ??g/L) in irrigated agricultural areas of the western USA. Preliminary analysis of explanatory variables used in the model indicated that surface-water Se concentration increased with increasing dissolved solids (DS) concentration and with the presence of Upper Cretaceous, mainly marine sediment. The presence or absence of Cretaceous sediment was the major variable affecting Se concentration in surface-water samples from the National Irrigation Water Quality Program. Median Se concentration was 14 ??g/L in samples from areas underlain by Cretaceous sediments and < 1 ??g/L in samples from areas underlain by non-Cretaceous sediments. Wilcoxon rank sum tests indicated that elevated Se concentrations in samples from areas with Cretaceous sediments, irrigated areas, and from closed lakes and ponds were statistically significant. Spearman correlations indicated that Se was positively correlated with a binary geology variable (0.64) and DS (0.45). Logistic regression models indicated that the concentration of Se in surface water was almost certain to exceed the Environmental Protection Agency aquatic-life chronic criterion of 5 ??g/L when DS was greater than 3000 mg/L in areas with Cretaceous sediments. The 'best' logistic regression model correctly predicted Se exceedances and nonexceedances 84.4% of the time, and model sensitivity was 80.7%. A regional map of Cretaceous sediment showed the location of potential problem areas. The map and logistic regression model are tools that can be used to determine the potential for Se contamination of irrigated agricultural areas in the western USA.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Non-ignorable missingness in logistic regression.

PubMed

Wang, Joanna J J; Bartlett, Mark; Ryan, Louise

2017-08-30

Nonresponses and missing data are common in observational studies. Ignoring or inadequately handling missing data may lead to biased parameter estimation, incorrect standard errors and, as a consequence, incorrect statistical inference and conclusions. We present a strategy for modelling non-ignorable missingness where the probability of nonresponse depends on the outcome. Using a simple case of logistic regression, we quantify the bias in regression estimates and show the observed likelihood is non-identifiable under non-ignorable missing data mechanism. We then adopt a selection model factorisation of the joint distribution as the basis for a sensitivity analysis to study changes in estimated parameters and the robustness of study conclusions against different assumptions. A Bayesian framework for model estimation is used as it provides a flexible approach for incorporating different missing data assumptions and conducting sensitivity analysis. Using simulated data, we explore the performance of the Bayesian selection model in correcting for bias in a logistic regression. We then implement our strategy using survey data from the 45 and Up Study to investigate factors associated with worsening health from the baseline to follow-up survey. Our findings have practical implications for the use of the 45 and Up Study data to answer important research questions relating to health and quality-of-life. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Prediction model for the return to work of workers with injuries in Hong Kong.

PubMed

Xu, Yanwen; Chan, Chetwyn C H; Lo, Karen Hui Yu-Ling; Tang, Dan

2008-01-01

This study attempts to formulate a prediction model of return to work for a group of workers who have been suffering from chronic pain and physical injury while also being out of work in Hong Kong. The study used Case-based Reasoning (CBR) method, and compared the result with the statistical method of logistic regression model. The database of the algorithm of CBR was composed of 67 cases who were also used in the logistic regression model. The testing cases were 32 participants who had a similar background and characteristics to those in the database. The methods of setting constraints and Euclidean distance metric were used in CBR to search the closest cases to the trial case based on the matrix. The usefulness of the algorithm was tested on 32 new participants, and the accuracy of predicting return to work outcomes was 62.5%, which was no better than the 71.2% accuracy derived from the logistic regression model. The results of the study would enable us to have a better understanding of the CBR applied in the field of occupational rehabilitation by comparing with the conventional regression analysis. The findings would also shed light on the development of relevant interventions for the return-to-work process of these workers.

Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

PubMed

Liu, Yang; Traskin, Mikhail; Lorch, Scott A; George, Edward I; Small, Dylan

2015-03-01

A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.

Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion

NASA Astrophysics Data System (ADS)

Jokar Arsanjani, Jamal; Helbich, Marco; Kainz, Wolfgang; Darvishi Boloorani, Ali

2013-04-01

This research analyses the suburban expansion in the metropolitan area of Tehran, Iran. A hybrid model consisting of logistic regression model, Markov chain (MC), and cellular automata (CA) was designed to improve the performance of the standard logistic regression model. Environmental and socio-economic variables dealing with urban sprawl were operationalised to create a probability surface of spatiotemporal states of built-up land use for the years 2006, 2016, and 2026. For validation, the model was evaluated by means of relative operating characteristic values for different sets of variables. The approach was calibrated for 2006 by cross comparing of actual and simulated land use maps. The achieved outcomes represent a match of 89% between simulated and actual maps of 2006, which was satisfactory to approve the calibration process. Thereafter, the calibrated hybrid approach was implemented for forthcoming years. Finally, future land use maps for 2016 and 2026 were predicted by means of this hybrid approach. The simulated maps illustrate a new wave of suburban development in the vicinity of Tehran at the western border of the metropolis during the next decades.

A statistical method for predicting seizure onset zones from human single-neuron recordings

NASA Astrophysics Data System (ADS)

Valdez, André B.; Hickman, Erin N.; Treiman, David M.; Smith, Kris A.; Steinmetz, Peter N.

2013-02-01

Objective. Clinicians often use depth-electrode recordings to localize human epileptogenic foci. To advance the diagnostic value of these recordings, we applied logistic regression models to single-neuron recordings from depth-electrode microwires to predict seizure onset zones (SOZs). Approach. We collected data from 17 epilepsy patients at the Barrow Neurological Institute and developed logistic regression models to calculate the odds of observing SOZs in the hippocampus, amygdala and ventromedial prefrontal cortex, based on statistics such as the burst interspike interval (ISI). Main results. Analysis of these models showed that, for a single-unit increase in burst ISI ratio, the left hippocampus was approximately 12 times more likely to contain a SOZ; and the right amygdala, 14.5 times more likely. Our models were most accurate for the hippocampus bilaterally (at 85% average sensitivity), and performance was comparable with current diagnostics such as electroencephalography. Significance. Logistic regression models can be combined with single-neuron recording to predict likely SOZs in epilepsy patients being evaluated for resective surgery, providing an automated source of clinically useful information.

Association between cardiovascular risk factors and carotid intima-media thickness in prepubertal Brazilian children.

PubMed

Gazolla, Fernanda Mussi; Neves Bordallo, Maria Alice; Madeira, Isabel Rey; de Miranda Carvalho, Cecilia Noronha; Vieira Monteiro, Alexandra Maria; Pinheiro Rodrigues, Nádia Cristina; Borges, Marcos Antonio; Collett-Solberg, Paulo Ferrez; Muniz, Bruna Moreira; de Oliveira, Cecilia Lacroix; Pinheiro, Suellen Martins; de Queiroz Ribeiro, Rebeca Mathias

2015-05-01

Early exposure to cardiovascular risk factors creates a chronic inflammatory state that could damage the endothelium followed by thickening of the carotid intima-media. To investigate the association of cardiovascular risk factors and thickening of the carotid intima. Media in prepubertal children. In this cross-sectional study, carotid intima-media thickness (cIMT) and cardiovascular risk factors were assessed in 129 prepubertal children aged from 5 to 10 year. Association was assessed by simple and multivariate logistic regression analyses. In simple logistic regression analyses, body mass index (BMI) z-score, waist circumference, and systolic blood pressure (SBP) were positively associated with increased left, right, and average cIMT, whereas diastolic blood pressure was positively associated only with increased left and average cIMT (p<0.05). In multivariate logistic regression analyses increased left cIMT was positively associated to BMI z-score and SBP, and increased average cIMT was only positively associated to SBP (p<0.05). BMI z-score and SBP were the strongest risk factors for increased cIMT.

New machine-learning algorithms for prediction of Parkinson's disease

NASA Astrophysics Data System (ADS)

Mandal, Indrajit; Sairam, N.

2014-03-01

This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

Landslide Hazard Mapping in Rwanda Using Logistic Regression

NASA Astrophysics Data System (ADS)

Piller, A.; Anderson, E.; Ballard, H.

2015-12-01

Landslides in the United States cause more than $1 billion in damages and 50 deaths per year (USGS 2014). Globally, figures are much more grave, yet monitoring, mapping and forecasting of these hazards are less than adequate. Seventy-five percent of the population of Rwanda earns a living from farming, mostly subsistence. Loss of farmland, housing, or life, to landslides is a very real hazard. Landslides in Rwanda have an impact at the economic, social, and environmental level. In a developing nation that faces challenges in tracking, cataloging, and predicting the numerous landslides that occur each year, satellite imagery and spatial analysis allow for remote study. We have focused on the development of a landslide inventory and a statistical methodology for assessing landslide hazards. Using logistic regression on approximately 30 test variables (i.e. slope, soil type, land cover, etc.) and a sample of over 200 landslides, we determine which variables are statistically most relevant to landslide occurrence in Rwanda. A preliminary predictive hazard map for Rwanda has been produced, using the variables selected from the logistic regression analysis.

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

GIS-based rare events logistic regression for mineral prospectivity mapping

NASA Astrophysics Data System (ADS)

Xiong, Yihui; Zuo, Renguang

2018-02-01

Mineralization is a special type of singularity event, and can be considered as a rare event, because within a specific study area the number of prospective locations (1s) are considerably fewer than the number of non-prospective locations (0s). In this study, GIS-based rare events logistic regression (RELR) was used to map the mineral prospectivity in the southwestern Fujian Province, China. An odds ratio was used to measure the relative importance of the evidence variables with respect to mineralization. The results suggest that formations, granites, and skarn alterations, followed by faults and aeromagnetic anomaly are the most important indicators for the formation of Fe-related mineralization in the study area. The prediction rate and the area under the curve (AUC) values show that areas with higher probability have a strong spatial relationship with the known mineral deposits. Comparing the results with original logistic regression (OLR) demonstrates that the GIS-based RELR performs better than OLR. The prospectivity map obtained in this study benefits the search for skarn Fe-related mineralization in the study area.

[Analysis of rational clinical uses of traditional Chinese medicine injections and factors influencing adverse drug reactions].

PubMed

Sun, Shi-Guang; Li, Zi-Feng; Xie, Yan-Ming; Liu, Jian; Lu, Yan; Song, Yi-Fei; Han, Ying-Hua; Liu, Li-Da; Peng, Ting-Ting

2013-09-01

To rationalize the clinical use and safety are some of the key issues in the surveillance of traditional Chinese medicine injections (TCMIs). In this 2011 study, 240 medical records of patients who had been discharged following treatment with TCMIs between 1 and 12 month previously were randomly selected from hospital records. Consistency between clinical use and the description of TCMIs was evaluated. Research on drug use and adverse drug reactions/events using logistic regression analysis was carried out. There was poor consistency between clinical use and best practice advised in manuals on TCMIs. Over-dosage and overly concentrated administration of TCMIs occurred, with the outcome of modifying properties of the blood. Logistic regression analysis showed that, drug concentration was a valid predictor for both adverse drug reactions/events and benefits associated with TCMIs. Surveillance of rational clinical use and safety of TCMIs finds that clinical use should be consistent with technical drug manual specifications, and drug use should draw on multi-layered logistic regression analysis research to help avoid adverse drug reactions/events.

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

NASA Astrophysics Data System (ADS)

Oh, Hyun-Joo; Lee, Saro; Chotikasathien, Wisut; Kim, Chang Hwan; Kwon, Ju Hyoung

2009-04-01

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.

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

Testing Gene-Gene Interactions in the Case-Parents Design

PubMed Central

Yu, Zhaoxia

2011-01-01

The case-parents design has been widely used to detect genetic associations as it can prevent spurious association that could occur in population-based designs. When examining the effect of an individual genetic locus on a disease, logistic regressions developed by conditioning on parental genotypes provide complete protection from spurious association caused by population stratification. However, when testing gene-gene interactions, it is unknown whether conditional logistic regressions are still robust. Here we evaluate the robustness and efficiency of several gene-gene interaction tests that are derived from conditional logistic regressions. We found that in the presence of SNP genotype correlation due to population stratification or linkage disequilibrium, tests with incorrectly specified main-genetic-effect models can lead to inflated type I error rates. We also found that a test with fully flexible main genetic effects always maintains correct test size and its robustness can be achieved with negligible sacrifice of its power. When testing gene-gene interactions is the focus, the test allowing fully flexible main effects is recommended to be used. PMID:21778736

A logistic regression analysis of factors related to the treatment compliance of infertile patients with polycystic ovary syndrome.

PubMed

Li, Saijiao; He, Aiyan; Yang, Jing; Yin, TaiLang; Xu, Wangming

2011-01-01

To investigate factors that can affect compliance with treatment of polycystic ovary syndrome (PCOS) in infertile patients and to provide a basis for clinical treatment, specialist consultation and health education. Patient compliance was assessed via a questionnaire based on the Morisky-Green test and the treatment principles of PCOS. Then interviews were conducted with 99 infertile patients diagnosed with PCOS at Renmin Hospital of Wuhan University in China, from March to September 2009. Finally, these data were analyzed using logistic regression analysis. Logistic regression analysis revealed that a total of 23 (25.6%) of the participants showed good compliance. Factors that significantly (p < 0.05) affected compliance with treatment were the patient's body mass index, convenience of medical treatment and concerns about adverse drug reactions. Patients who are obese, experience inconvenient medical treatment or are concerned about adverse drug reactions are more likely to exhibit noncompliance. Treatment education and intervention aimed at these patients should be strengthened in the clinic to improve treatment compliance. Further research is needed to better elucidate the compliance behavior of patients with PCOS.

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

PubMed

Bersabé, Rosa; Rivas, Teresa

2010-05-01

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

Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

PubMed Central

Yan, Fang-Rong; Lin, Jin-Guan; Liu, Yu

2011-01-01

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis. PMID:21716672

John Snow, William Farr and the 1849 outbreak of cholera that affected London: a reworking of the data highlights the importance of the water supply.

PubMed

Bingham, P; Verlander, N Q; Cheal, M J

2004-09-01

This paper examines why Snow's contention that cholera was principally spread by water was not accepted in the 1850s by the medical elite. The consequence of rejection was that hundreds in the UK continued to die. Logistic regression was used to re-analyse data, first published in 1852 by William Farr, consisting of the 1849 mortality rate from cholera and eight potential explanatory variables for the 38 registration districts of London. Logistic regression does not support Farr's original conclusion that a district's elevation above high water was the most important explanatory variable. Elevation above high water, water supply and poor rate each have an independent significant effect on district cholera mortality rate, but in terms of size of effect, it can be argued that water supply most strongly 'invited' further consideration. The science of epidemiology, that Farr helped to found, has continued to advance. Had logistic regression been available to Farr, its application to his 1852 data set would have changed his conclusion.

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

Evaluating the Locational Attributes of Education Management Organizations (EMOs)

ERIC Educational Resources Information Center

Gulosino, Charisse; Miron, Gary

2017-01-01

This study uses logistic and multinomial logistic regression models to analyze neighborhood factors affecting EMO (Education Management Organization)-operated schools' locational attributes (using census tracts) in 41 states for the 2014-2015 school year. Our research combines market-based school reform, institutional theory, and resource…

A Numerical Study of New Logistic Map

NASA Astrophysics Data System (ADS)

Khmou, Youssef

In this paper, we propose a new logistic map based on the relation of the information entropy, we study the bifurcation diagram comparatively to the standard logistic map. In the first part, we compare the obtained diagram, by numerical simulations, with that of the standard logistic map. It is found that the structures of both diagrams are similar where the range of the growth parameter is restricted to the interval [0,e]. In the second part, we present an application of the proposed map in traffic flow using macroscopic model. It is found that the bifurcation diagram is an exact model of the Greenberg’s model of traffic flow where the growth parameter corresponds to the optimal velocity and the random sequence corresponds to the density. In the last part, we present a second possible application of the proposed map which consists of random number generation. The results of the analysis show that the excluded initial values of the sequences are (0,1).

Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study.

PubMed

Leffondré, Karen; Abrahamowicz, Michal; Siemiatycki, Jack

2003-12-30

Case-control studies are typically analysed using the conventional logistic model, which does not directly account for changes in the covariate values over time. Yet, many exposures may vary over time. The most natural alternative to handle such exposures would be to use the Cox model with time-dependent covariates. However, its application to case-control data opens the question of how to manipulate the risk sets. Through a simulation study, we investigate how the accuracy of the estimates of Cox's model depends on the operational definition of risk sets and/or on some aspects of the time-varying exposure. We also assess the estimates obtained from conventional logistic regression. The lifetime experience of a hypothetical population is first generated, and a matched case-control study is then simulated from this population. We control the frequency, the age at initiation, and the total duration of exposure, as well as the strengths of their effects. All models considered include a fixed-in-time covariate and one or two time-dependent covariate(s): the indicator of current exposure and/or the exposure duration. Simulation results show that none of the models always performs well. The discrepancies between the odds ratios yielded by logistic regression and the 'true' hazard ratio depend on both the type of the covariate and the strength of its effect. In addition, it seems that logistic regression has difficulty separating the effects of inter-correlated time-dependent covariates. By contrast, each of the two versions of Cox's model systematically induces either a serious under-estimation or a moderate over-estimation bias. The magnitude of the latter bias is proportional to the true effect, suggesting that an improved manipulation of the risk sets may eliminate, or at least reduce, the bias. Copyright 2003 JohnWiley & Sons, Ltd.

Reported gum disease as a cardiovascular risk factor in adults with intellectual disabilities.

PubMed

Hsieh, K; Murthy, S; Heller, T; Rimmer, J H; Yen, G

2018-03-01

Several risk factors for cardiovascular disease (CVD) have been identified among adults with intellectual disabilities (ID). Periodontitis has been reported to increase the risk of developing a CVD in the general population. Given that individuals with ID have been reported to have a higher prevalence of poor oral health than the general population, the purpose of this study was to determine whether adults with ID with informant reported gum disease present greater reported CVD than those who do not have reported gum disease and whether gum disease can be considered a risk factor for CVD. Using baseline data from the Longitudinal Health and Intellectual Disability Study from which informant survey data were collected, 128 participants with reported gum disease and 1252 subjects without reported gum disease were identified. A series of univariate logistic regressions was conducted to identify potential confounding factors for a multiple logistic regression. The series of univariate logistic regressions identified age, Down syndrome, hypercholesterolemia, hypertension, reported gum disease, daily consumption of fruits and vegetables and the addition of table salt as significant risk factors for reported CVD. When the significant factors from the univariate logistic regression were included in the multiple logistic analysis, reported gum disease remained as an independent risk factor for reported CVD after adjusting for the remaining risk factors. Compared with the adults with ID without reported gum disease, adults in the gum disease group demonstrated a significantly higher prevalence of reported CVD (19.5% vs. 9.7%; P = .001). After controlling for other risk factors, reported gum disease among adults with ID may be associated with a higher risk of CVD. However, further research that also includes clinical indices of periodontal disease and CVD for this population is needed to determine if there is a causal relationship between gum disease and CVD. © 2017 MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disabilities and John Wiley & Sons Ltd.

The role of specific visual subfields in collisions with oncoming cars during simulated driving in patients with advanced glaucoma.

PubMed

Kunimatsu-Sanuki, Shiho; Iwase, Aiko; Araie, Makoto; Aoki, Yuki; Hara, Takeshi; Fukuchi, Takeo; Udagawa, Sachiko; Ohkubo, Shinji; Sugiyama, Kazuhisa; Matsumoto, Chota; Nakazawa, Toru; Yamaguchi, Takuhiro; Ono, Hiroshi

2017-07-01

To assess the role of specific visual subfields in collisions with oncoming cars during simulated driving in patients with advanced glaucoma. Normal subjects and patients with glaucoma with mean deviation <-12 dB in both eyes (Humphrey Field Analyzer 24-2 SITA-S program) used a driving simulator (DS; Honda Motor, Tokyo). Two scenarios in which oncoming cars turned right crossing the driver's path were chosen. We compared the binocular integrated visual field (IVF) in the patients who were involved in collisions and those who were not. We performed a multivariate logistic regression analysis; the dependent parameter was collision involvement, and the independent parameters were age, visual acuity and mean sensitivity of the IVF subfields. The study included 43 normal subjects and 100 patients with advanced glaucoma. And, 5 of the 100 patients with advanced glaucoma experienced simulator sickness during the main test and were thus excluded. In total, 95 patients with advanced glaucoma and 43 normal subjects completed the main test of DS. Advanced glaucoma patients had significantly more collisions than normal patients in one or both DS scenarios (p<0.001). The patients with advanced glaucoma who were involved in collisions were older (p=0.050) and had worse visual acuity in the better eye (p<0.001) and had lower mean IVF sensitivity in the inferior hemifield, both 0°-12° and 13°-24° in comparison with who were not involved in collisions (p=0.012 and p=0.034). A logistic regression analysis revealed that collision involvement was significantly associated with decreased inferior IVF mean sensitivity from 13° to 24° (p=0.041), in addition to older age and lower visual acuity (p=0.018 and p<0.001). Our data suggest that the inferior hemifield was associated with the incidence of motor vehicle collisions with oncoming cars in patients with advanced glaucoma. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Are older adults living in more equal counties healthier than older adults living in more unequal counties? A propensity score matching approach.

PubMed

Choi, HwaJung; Burgard, Sarah; Elo, Irma T; Heisler, Michele

2015-09-01

We assessed the potential contextual effect of income inequality on health by: 1) comparing individuals with similar socioeconomic status (SES) but who reside in counties with different levels of income inequality; and 2) examining whether the potential effect of county-level income inequality on health varies across SES groups. We used the Health and Retirement Study, a nationally representative study of Americans over the age of 50. Using propensity score matching, we selected SES-comparable individuals living in high-income inequality counties and in low-income inequality counties. We examined differences in self-rated overall health outcomes and in other specific physical/mental health outcomes between the two groups using logistic regression (n = 34,994) and imposing different sample restrictions based on residential duration in the area. We then used logistic regression with interactions to assess whether, and if so how, health outcomes differed among participants of different SES groups defined by wealth, income, and education. In bivariate analyses of the unmatched full sample, adults living in high-income inequality counties have worse health outcomes for most health measures. After propensity score matching, adults in high-income inequality counties had worse self-rated health status (AOR = 1.12; 95% CI 1.04-1.19) and were more likely to report diagnosed psychiatric problems (AOR = 1.08; 95% CI 0.99-1.19) than their matched counterparts in low-income inequality counties. These associations were stronger with longer-term residents in the area. Adverse health outcomes associated with living in high-income inequality counties were significant particularly for individuals in the 30(th) or greater percentiles of income/wealth distribution and those without a college education. In summary, after using more precise matching methods to compare individuals with similar characteristics and addressing measurement error by excluding more recently arrived county residents, adults living in high-income inequality counties had worse reported overall physical and mental health than adults living in low-income inequality counties. Copyright © 2015 Elsevier Ltd. All rights reserved.

Role of Anemia in Home Oxygen Therapy in Chronic Obstructive Pulmonary Disease Patients.

PubMed

Copur, Ahmet Sinan; Fulambarker, Ashok; Molnar, Janos; Nadeem, Rashid; McCormack, Charles; Ganesh, Aarthi; Kheir, Fayez; Hamon, Sara

2015-01-01

Anemia is a known comorbidity found in chronic obstructive pulmonary disease (COPD) patients. Hypoxemia is common and basically due to ventilation/perfusion (V/Q) mismatch in COPD. Anemia, by decreasing arterial oxygen content, may be a contributing factor for decreased delivery of oxygen to tissues. The objective of this study is to determine if anemia is a factor in qualifying COPD patients for home oxygen therapy. The study was designed as a retrospective, cross-sectional, observational chart review. Patients who were referred for home oxygen therapy evaluation were selected from the computerized patient record system. Demographic data, oxygen saturation at rest and during exercise, pulmonary function test results, hemoglobin level, medications, reason for anemia, comorbid diseases, and smoking status were recorded. The χ tests, independent sample t tests, and logistic regression were used for statistical analysis. Only 356 of total 478 patient referrals had a diagnosis of COPD over a 2-year period. Although 39 of them were excluded, 317 patients were included in the study. The overall rate of anemia was 38% in all COPD patients. Anemia was found significantly more frequent in COPD patients on home oxygen therapy (46%) than those not on home oxygen therapy (18.5%) (P < 0.0001). Mean saturation of peripheral oxygen values were significantly lower in anemic COPD patients both at rest and during exercise (P < 0.0001). Also, in COPD patients, age, Global Initiative for Chronic Obstructive Lung Disease class, smoking status, hemoglobin level, hematocrit, percent of forced expiratory volume in first second, forced expiratory volume in first second/forced vital capacity, residual volume/total lung volume, percent of carbon monoxide diffusion capacity were significantly different between home oxygen therapy and those not on home oxygen therapy (P < 0.05). Multivariate logistic regression showed that anemia remained a strong predictor for long-term oxygen therapy use in COPD patients after adjusting for other significant parameters. Anemic COPD patients are more hypoxic especially during exercise than those who are not anemic. We conclude that anemia is a contributing factor in qualifying COPD patients for home oxygen therapy.

Inflammatory mediators in chronic heart failure in North India.

PubMed

Fedacko, Jan; Singh, Ram B; Gupta, Aditya; Hristova, Krasimira; Toda, Eri; Kumar, Adarsh; Saxena, Manoj; Baby, Anjum; Singh, Ranjana; Toru, Takahashi; Wilson, Douglas W

2014-08-01

Recent evidence shows that pro-inflammatory cytokines may be important in the assessment of severity and prognosis in congestive heart failure (CHF). In the present study, we examine the association of cytokines with causes, grade and prognosis of CHF patients. Of 127 patients with CHF, 11 were excluded and the remaining 116 patients with different aetiologies of CHF, and 250 age- and sex-matched control subjects, were evaluated in this case study. Severity of disease based on the New York Heart Association (NYHA) standards, fell within functional classes II to IV. The diagnosis of HF was based on clinical manifestations as well as on echocardiographic heart enlargement. Cytokines were measured by chemiluminescence. Causes of death were assessed based on death certificates. Multivariate logistic regression analysis was used to determine the risk factors of heart failure. Echocardiographic ejection fraction was 39.1 +/- 8.2% (mean +/- SD) in the study group indicating class II-IV heart failure. Laboratory data showed increase in biomarkers of oxidative stress, among HF patients compared to healthy subjects. Pro-inflammatory cytokines; IL-6 and TNF-alpha were significantly higher among HF patients compared to healthy subjects. TNF-alpha and IL-6, showed significant increase among patients with CHF due to ischaemic heart disease and cardiomyopathy compared to levels among CHF patients with valvular heart disease and hypertensive heart diseases. The levels of the cytokines were significantly higher among patients with class III and IV heart failure and those who died, compared to patients with class II heart failure. Multivariate logistic regression analysis revealed that CAD, cardiomyopathy, and IL-6 were strongly associated--and low ejection fraction and TNF-alpha--weakly associated with HF. Of 116 patients, 20 (17.2%) died during a follow-up of two years, and the deaths were mainly among NYHA class III and IV patients in whom the cause of CHF was CAD (10.9%) and cardiomyopathy (6.9%) which had greater levels of cytokines. The findings indicated that pro-inflammatory cytokines may be important indicators of causes, severity of CHF and prognosis among these patients.

Missing data treatments matter: an analysis of multiple imputation for anterior cervical discectomy and fusion procedures.

PubMed

Ondeck, Nathaniel T; Fu, Michael C; Skrip, Laura A; McLynn, Ryan P; Cui, Jonathan J; Basques, Bryce A; Albert, Todd J; Grauer, Jonathan N

2018-04-09

The presence of missing data is a limitation of large datasets, including the National Surgical Quality Improvement Program (NSQIP). In addressing this issue, most studies use complete case analysis, which excludes cases with missing data, thus potentially introducing selection bias. Multiple imputation, a statistically rigorous approach that approximates missing data and preserves sample size, may be an improvement over complete case analysis. The present study aims to evaluate the impact of using multiple imputation in comparison with complete case analysis for assessing the associations between preoperative laboratory values and adverse outcomes following anterior cervical discectomy and fusion (ACDF) procedures. This is a retrospective review of prospectively collected data. Patients undergoing one-level ACDF were identified in NSQIP 2012-2015. Perioperative adverse outcome variables assessed included the occurrence of any adverse event, severe adverse events, and hospital readmission. Missing preoperative albumin and hematocrit values were handled using complete case analysis and multiple imputation. These preoperative laboratory levels were then tested for associations with 30-day postoperative outcomes using logistic regression. A total of 11,999 patients were included. Of this cohort, 63.5% of patients had missing preoperative albumin and 9.9% had missing preoperative hematocrit. When using complete case analysis, only 4,311 patients were studied. The removed patients were significantly younger, healthier, of a common body mass index, and male. Logistic regression analysis failed to identify either preoperative hypoalbuminemia or preoperative anemia as significantly associated with adverse outcomes. When employing multiple imputation, all 11,999 patients were included. Preoperative hypoalbuminemia was significantly associated with the occurrence of any adverse event and severe adverse events. Preoperative anemia was significantly associated with the occurrence of any adverse event, severe adverse events, and hospital readmission. Multiple imputation is a rigorous statistical procedure that is being increasingly used to address missing values in large datasets. Using this technique for ACDF avoided the loss of cases that may have affected the representativeness and power of the study and led to different results than complete case analysis. Multiple imputation should be considered for future spine studies. Copyright © 2018 Elsevier Inc. All rights reserved.

Fetal head circumference and subpubic angle are independent risk factors for unplanned cesarean and operative delivery.

PubMed

Rizzo, Giuseppe; Aiello, Elisa; Bosi, Costanza; D'Antonio, Francesco; Arduini, Domenico

2017-08-01

The aim of this study was to ascertain whether combined ultrasound assessment of fetal head circumference (HC) and maternal subpubic angle (SPA) prior to the onset of labor may predict the likelihood of an unplanned operative delivery (UOD) in nulliparous women at term. Prospective cohort study of singleton pregnancies in cephalic presentation. Pregnancies experiencing UOD secondary to fetal distress were excluded. HC was assessed transabdominally and SPA values were obtained from a reconstructed coronal plane on three-dimensional (3D) ultrasound performed translabially at 36-38 weeks of gestation. Maternal characteristics, HC expressed as multiple of median, and SPA were compared according to the mode of delivery. Logistic regression and receiver operating characteristics curve analyses were used to analyze the data. 597 pregnancies were included in the study. Spontaneous vaginal delivery occurred in 70.2% of the cases and UOD was required in 29.8%. There was no difference in pregnancy characteristics and birthweight between women who had a spontaneous vaginal birth compared with UOD. The HC multiple of median was larger (1.00 ± 0.02 vs. 1.03 ± 0.02, p ≤ 0.0001), whereas SPA was narrower in the UOD group (124.02 ± 13.64 vs. 102.61 ± 16.13, p ≤ 0.0001). At logistic regression, SPA (OR 0.91, 95% CI 0.89-0.93), HC multiple of median (OR 1.13, 95% CI 1.09-1.17) and maternal height (OR 0.95, 95% CI 0.92-0.99) were independently associated with UOD. When combined, the diagnostic accuracy of a predictive model integrating HC, SPA and maternal height was highly predictive of UOD with an area under the curve of 0.904 (95% CI 0.88-0.93). Ultrasound assessment of fetal HC and maternal SPA after 36 weeks of gestation can identify a subset of women at higher risk of UOD during labor, for whom early planned delivery might be beneficial. © 2017 Nordic Federation of Societies of Obstetrics and Gynecology.

Physical fitness of 9 year olds in England: related factors.

PubMed

Kikuchi, S; Rona, R J; Chinn, S

1995-04-01

To examine the influence of social factors, passive smoking, and other parental health related factors, as well as anthropometric and other measurements on children's cardiorespiratory fitness. This was a cross sectional study. The analysis was based on 22 health areas in England. The subjects were 299 boys and 282 girls aged 8 to 9 years. Parents did not give positive consent for 15% of the eligible sample. A further 25% of the eligible sample did not participate because the cycle-ergometer broke down, study time was insufficient, or they were excluded from the analysis because they were from ethnic minority groups or had missing data on one continuous variable. Cardiorespiratory fitness was determined using the cycle-ergometer test. It was measured in terms of PWC85%-that is, power output per body weight (watt/kg) assessed at 85% of maximum heart rate. The association between children's fitness and biological and social factors was analysed in two stages. Firstly, multiple logistic analysis was used to examine the factors associated with the children's ability to complete the test for at least four minutes. Secondly, multiple linear regression analysis was used to examine the independent association of the factors with PWC85%. In the logistic analysis, shorter children, children with higher blood pressure, and boys with a larger sibship size had poorer fitness. In the multiple regression analysis, only height (p < 0.001) was positively associated, and the sum of skinfold thicknesses at four sites (p = 0.001) was negatively associated with fitness in both sexes. In girls, a positive association was found with pre-exercise peak expiratory flow rate (p < 0.05), and there were negative associations with systolic blood pressure (p < 0.05) and family history of heart attack (p < 0.05). In boys an association was found with skinfold distribution and fitness (p < 0.05), so that children with relatively less body fat were fitter. Social and health behaviour factors such as father's social class, father's employment status, or parents' smoking habits were unrelated to child's fitness. Height and obesity are strongly associated, and systolic blood pressure to a small extent, with children's fitness, but social factors are unrelated.

The importance of proximal fusion level selection for outcomes of multi-level lumbar posterolateral fusion.

PubMed

Nam, Woo Dong; Cho, Jae Hwan

2015-03-01

There are few studies about risk factors for poor outcomes from multi-level lumbar posterolateral fusion limited to three or four level lumbar posterolateral fusions. The purpose of this study was to analyze the outcomes of multi-level lumbar posterolateral fusion and to search for possible risk factors for poor surgical outcomes. We retrospectively analyzed 37 consecutive patients who underwent multi-level lumbar or lumbosacral posterolateral fusion with posterior instrumentation. The outcomes were deemed either 'good' or 'bad' based on clinical and radiological results. Many demographic and radiological factors were analyzed to examine potential risk factors for poor outcomes. Student t-test, Fisher exact test, and the chi-square test were used based on the nature of the variables. Multiple logistic regression analysis was used to exclude confounding factors. Twenty cases showed a good outcome (group A, 54.1%) and 17 cases showed a bad outcome (group B, 45.9%). The overall fusion rate was 70.3%. The revision procedures (group A: 1/20, 5.0%; group B: 4/17, 23.5%), proximal fusion to L2 (group A: 5/20, 25.0%; group B: 10/17, 58.8%), and severity of stenosis (group A: 12/19, 63.3%; group B: 3/11, 27.3%) were adopted as possible related factors to the outcome in univariate analysis. Multiple logistic regression analysis revealed that only the proximal fusion level (superior instrumented vertebra, SIV) was a significant risk factor. The cases in which SIV was L2 showed inferior outcomes than those in which SIV was L3. The odds ratio was 6.562 (95% confidence interval, 1.259 to 34.203). The overall outcome of multi-level lumbar or lumbosacral posterolateral fusion was not as high as we had hoped it would be. Whether the SIV was L2 or L3 was the only significant risk factor identified for poor outcomes in multi-level lumbar or lumbosacral posterolateral fusion in the current study. Thus, the authors recommend that proximal fusion levels be carefully determined when multi-level lumbar fusions are considered.

Lumbar spine spondylolysis in the adult population: using computed tomography to evaluate the possibility of adult onset lumbar spondylosis as a cause of back pain.

PubMed

Brooks, Benjamin K; Southam, Samuel L; Mlady, Gary W; Logan, Jeremy; Rosett, Matthew

2010-07-01

To determine if new onset of low back pain in adults could be secondary to lumbar spondylolysis by establishing the age-related prevalence in the general population by examining patients undergoing computed tomography (CT) for reasons unrelated to back pain. The records of 2,555 patients who had undergone abdominal and pelvic CT in 2008 were reviewed electronically. In order to determine a true representation of the general population, we reviewed all indications for CT, excluding patients with a primary complaint of low back pain as the primary indication for imaging. Equal numbers of patients were separated into age groups by decade to ensure an even distribution of ages for statistical analysis. Patients older than 70 years were grouped together to provide case numbers comparable to those of the other decades. Logistic regression analysis was performed to evaluate the significance of the results. Three board-certified radiologists, including two musculoskeletal fellows and a radiology resident, retrospectively evaluated CT scans for lumbar spondylolysis, including unilateral and bilateral defects. Of the 2,555 cases evaluated, there were 203 positive cases of defects of the lumbar pars interarticularis. This corresponded to an overall prevalence of 8.0%. Prevalence per decade was fairly evenly distributed and ranged from 7.0%( ages 30-39 years) to 9.2% (ages 70 years and above). Prevalence of ages 20-49 years was 7.9%, and that of ages 50 years and older was 8.0%. Male to female ratio was 1.5:1. Logistic regression showed no significant increase in spondylolysis based on age. No significant increase in the prevalence of lumbar spondylolysis was demonstrated in patients older than 20 years. This suggests that the development of symptomatic lumbar pars defects do not occur in this population and should not be considered as a rare but potentially treatable cause of new onset low back pain in adults. This study demonstrated an overall prevalence of pars defects of 8.0% in our population. As demonstrated in previous studies, the male to female ratio of 1.5:1 was a statistically significant difference.

The Importance of Proximal Fusion Level Selection for Outcomes of Multi-Level Lumbar Posterolateral Fusion

PubMed Central

Nam, Woo Dong

2015-01-01

Background There are few studies about risk factors for poor outcomes from multi-level lumbar posterolateral fusion limited to three or four level lumbar posterolateral fusions. The purpose of this study was to analyze the outcomes of multi-level lumbar posterolateral fusion and to search for possible risk factors for poor surgical outcomes. Methods We retrospectively analyzed 37 consecutive patients who underwent multi-level lumbar or lumbosacral posterolateral fusion with posterior instrumentation. The outcomes were deemed either 'good' or 'bad' based on clinical and radiological results. Many demographic and radiological factors were analyzed to examine potential risk factors for poor outcomes. Student t-test, Fisher exact test, and the chi-square test were used based on the nature of the variables. Multiple logistic regression analysis was used to exclude confounding factors. Results Twenty cases showed a good outcome (group A, 54.1%) and 17 cases showed a bad outcome (group B, 45.9%). The overall fusion rate was 70.3%. The revision procedures (group A: 1/20, 5.0%; group B: 4/17, 23.5%), proximal fusion to L2 (group A: 5/20, 25.0%; group B: 10/17, 58.8%), and severity of stenosis (group A: 12/19, 63.3%; group B: 3/11, 27.3%) were adopted as possible related factors to the outcome in univariate analysis. Multiple logistic regression analysis revealed that only the proximal fusion level (superior instrumented vertebra, SIV) was a significant risk factor. The cases in which SIV was L2 showed inferior outcomes than those in which SIV was L3. The odds ratio was 6.562 (95% confidence interval, 1.259 to 34.203). Conclusions The overall outcome of multi-level lumbar or lumbosacral posterolateral fusion was not as high as we had hoped it would be. Whether the SIV was L2 or L3 was the only significant risk factor identified for poor outcomes in multi-level lumbar or lumbosacral posterolateral fusion in the current study. Thus, the authors recommend that proximal fusion levels be carefully determined when multi-level lumbar fusions are considered. PMID:25729522

Risk of early surgery for Crohn's disease: implications for early treatment strategies.

PubMed

Sands, Bruce E; Arsenault, Joanne E; Rosen, Michael J; Alsahli, Mazen; Bailen, Laurence; Banks, Peter; Bensen, Steven; Bousvaros, Athos; Cave, David; Cooley, Jeffrey S; Cooper, Herbert L; Edwards, Susan T; Farrell, Richard J; Griffin, Michael J; Hay, David W; John, Alex; Lidofsky, Sheldon; Olans, Lori B; Peppercorn, Mark A; Rothstein, Richard I; Roy, Michael A; Saletta, Michael J; Shah, Samir A; Warner, Andrew S; Wolf, Jacqueline L; Vecchio, James; Winter, Harland S; Zawacki, John K

2003-12-01

In this study we aimed to define the rate of early surgery for Crohn's disease and to identify risk factors associated with early surgery as a basis for subsequent studies of early intervention in Crohn's disease. We assembled a retrospective cohort of patients with Crohn's disease diagnosed between 1991 and 1997 and followed for at least 3 yr, who were identified in 16 community and referral-based practices in New England. Chart review was performed for each patient. Details of baseline demographic and disease features were recorded. Surgical history including date of surgery, indication, and procedure were also noted. Risk factors for early surgery (defined as major surgery for Crohn's disease within 3 yr of diagnosis, exclusive of major surgery at time of diagnosis) were identified by univariate analysis. Multiple logistic regression was used to identify independent risk factors. Of 345 eligible patients, 69 (20.1%) required surgery within 3 yr of diagnosis, excluding the 14 patients (4.1%) who had major surgery at the time of diagnosis. Overall, the interval between diagnosis and surgery was short; one half of all patients who required surgery underwent operation within 6 months of diagnosis. Risk factors identified by univariate analysis as significantly associated with early surgery included the following: smoking; disease of small bowel without colonic involvement; nausea and vomiting or abdominal pain on presentation; neutrophil count; and steroid use in the first 6 months. Disease localized to the colon only, blood in the stool, use of 5-aminosalicylate, and lymphocyte count were inversely associated with risk of early surgery. Logistic regression confirmed independent associations with smoking as a positive risk factor and involvement of colon without small bowel as a negative risk factor for early surgery. The rate of surgery is high in the first 3 yr after diagnosis of Crohn's disease, particularly in the first 6 months. These results suggest that improved risk stratification and potent therapies with rapid onset of action are needed to modify the natural history of Crohn's disease.

Patient Characteristics and Temporal Trends in Police Transport of Blunt Trauma Patients: A Multicenter Retrospective Cohort Study.

PubMed

Kaufman, Elinore J; Jacoby, Sara F; Sharoky, Catherine E; Carr, Brendan G; Delgado, M Kit; Reilly, Patrick M; Holena, Daniel N

2017-01-01

Police transport (PT) of penetrating trauma patients has the potential to decrease prehospital times for patients with life-threatening hemorrhage and is part of official policy in Philadelphia, Pennsylvania. We hypothesized that rates of PT of bluntly injured patients have increased over the past decade. We used Pennsylvania Trauma Outcomes Study registry data from 2006-15 to identify bluntly injured adult patients transported to all 8 trauma centers in Philadelphia. PT was compared to ambulance transport, excluding transfers, burn patients, and private transport. We compared demographics, mechanism, and injury outcomes between PT and ambulance transport patients and used multivariable logistic regression to identify independent predictors of PT. We also identified physiological indicators and injury patterns that might have benefitted from prehospital intervention by EMS. Of 28 897 bluntly injured patients, 339 (1.2%) were transported by police and 28 558 (98.8%) by ambulance. Blunt trauma accounted for 11% of PT and penetrating trauma for 89%. PT patients were younger, more likely to be male, and more likely to be African American or Asian and were more often injured by assault or motor vehicle crash. There were no significant differences presenting physiology between PT and EMS patients. In multivariable logistic regression analysis, male sex (OR 1.89, 95%CI 1.40-2.55), African American race (OR 1.71 95%CI 1.34-2.18), and Asian race (OR 2.25, 95%CI 1.22-4.14) were independently associated with PT. Controlling for injury severity and physiology, there was no significant difference in mortality between PT and EMS. Overall, 64% of PT patients had a condition that might have benefited from prehospital intervention such as supplemental oxygen for brain injury or spine stabilization for vertebral fractures. PT affects a small minority of blunt trauma patients, and did not appear associated with higher mortality. However, PT patients included many who might have benefited from proven, prehospital intervention. Clinicians, EMS providers, and law enforcement should collaborate to optimize use of PT within the trauma system.

Axial length and proliferative diabetic retinopathy.

PubMed

Yang, Ko-Jen; Sun, Chi-Chin; Ku, Wan-Chen; Chuang, Lan-Hsin; Ng, Soh Ching; Chou, Kuei-Mei; Kuo, Sheng-Fong; Yeung, Ling

2012-04-01

To determine the correlation between axial length and diabetic retinopathy (DR) in patients with diabetes mellitus for 10 years or more. This study was a prospective, observational, cross-sectional study. Patients with diabetes for 10 years or more were included. We excluded eyes with any other significant ocular disease or any prior intraocular surgery, except uncomplicated cataract surgery. Only one eye of each patient was included as the study eye. The severity of DR was graded as no DR, non-proliferative DR (NPDR), or proliferative DR (PDR). Axial length was measured by A-scan ultrasound (10 MHz Transducer, AL-2000 Biometer/Pachymeter; Tomey, Phoenix, AZ). Univariate logistic regression models were used to evaluate the relationship between the dependent variables (any DR, PDR) and all potential risk factors. Axial length and other factors with p value <0.1 were included in multivariate logistic regression models. Backward selection based on the likelihood ratio statistic was used to select the final models. We included 166 eyes from 166 patients (93 female and 73 male; mean age, 68.8 years). The mean diabetes duration was 15.4 years. Fifty-four (32.5%) eyes had no DR, 72 (43.4%) eyes had NPDR, and 40 (24.1%) eyes had PDR. In univariate analysis, hypertension (p = 0.009), renal impairment (p = 0.079), and insulin use (p = 0.009) were associated with developing any DR. Hypertension (p = 0.042), renal impairment (p = 0.014), insulin use (p = 0.040), pseudophakia (p = 0.019), and axial length (p = 0.076) were associated with developing PDR. In multivariate analysis, hypertension (p = 0.005) and insulin use (p = 0.010) were associated with developing any DR. Hypertension (p = 0.020), renal impairment (p = 0.025), pseudophakia (p = 0.006), and axial length (p = 0.024) were associated with developing PDR. This observational study suggests an inverse relationship between axial length and the development of PDR in patients with diabetes for 10 years or more. No relationship was found between axial length and the development of any DR.

The old man and the C-spine fracture: Impact of halo vest stabilization in patients with blunt cervical spine fractures.

PubMed

Sharpe, John P; Magnotti, Louis J; Weinberg, Jordan A; Schroeppel, Thomas J; Fabian, Timothy C; Croce, Martin A

2016-01-01

Placement of a halo vest for cervical spine fractures is presumed to be less morbid than operative fixation. However, restrictions imposed by the halo vest can be detrimental, especially in older patients. The purpose of this study was to evaluate the impact of halo vest placement on outcomes by age in patients with cervical spine fractures without spinal cord injury. All patients with blunt cervical spine fractures managed over an 18-year period were identified. Those with spinal cord injury and severe traumatic brain injury were excluded. Patients were stratified by age, sex, halo vest, injury severity, and severity of shock. Outcomes included intensive care unit length of stay, ventilator days, ventilator-associated pneumonia, functional status, and mortality. Multivariable logistic regression was performed to determine whether halo vest was an independent predictor of mortality in older patients. A total of 3,457 patients were identified: 69% were male, with a mean Injury Severity Score (ISS) and Glasgow Coma Scale (GCS) score of 19 and 13, respectively. Overall mortality was 5.3%. One hundred seventy-nine patients were managed with a halo vest, 133 of those 54 years and older and 46 of those younger than 54 years. Both mortality (13% vs. 0%, p < 0.001) and intensive care unit length of stay (4 days vs. 2 days, p = 0.02) were significantly increased in older patients despite less severe injury (admission GCS score of 15 vs. 14 and ISS of 14 vs. 17, p = 0.03). Multivariable logistic regression identified halo vest as an independent predictor of mortality after adjusting for injury severity and severity of shock (odds ratio, 2.629; 95% confidence interval, 1.056-6.543) in older patients. The potential risk of operative stabilization must be weighed against that of halo vest placement for older patients with cervical spine fractures following blunt trauma. Patient age should be strongly considered before placement of a halo vest for cervical spine stabilization. Therapeutic study, level IV.

Independent association of elevated serum hepatocyte growth factor levels with development of insulin resistance in a 10-year prospective study.

PubMed

Tsukagawa, Eri; Adachi, Hisashi; Hirai, Yuji; Enomoto, Mika; Fukami, Ako; Ogata, Kinuka; Kasahara, Akiko; Yokoi, Kanako; Imaizumi, Tsutomu

2013-07-01

Hepatocyte growth factor (HGF) receptors form a hybrid complex with insulin receptors in the liver of mice, which lead to robust signalling to regulate glucose metabolism. Serum HGF levels are high in subjects with metabolic syndrome and/or obesity. Accordingly, we prospectively investigated the relationship between HGF and the development of insulin resistance (IR) in a general population without IR at baseline. A total of 1492 subjects received health examinations. After excluding subjects with diabetes and/or IR (n = 402) at baseline, the remaining subjects (n = 1090) were followed-up 10 years later. Complete data sets were available from 716 subjects for prospective analysis. Logistic regression was performed to determine factors associated with the development of IR after 10 years. In subjects without diabetes at baseline, serum HGF levels were higher (0·26 ± 0·10 ng/ml, n = 259) in subjects with IR than without it (0·22 ± 0·09 ng/ml, n = 1090). After deleting subjects who developed liver disease during follow-up, 188 were found to have developed IR at 10 years after the original screening. HGF (P < 0·05), age (P < 0·001), homoeostasis model assessment index (P < 0·001), HDL-c (P < 0·05; inversely) and hypertensive medication (P < 0·05) were significantly associated with the development of IR by multivariate stepwise logistic regression analysis. A significant (P < 0·05) relative risk [1·75 (95%CI: 1·01-3·12)] for the development of IR was observed in the highest (≥0·30 ng/ml) vs the lowest categories (<0·15 ng/ml) of HGF after adjustments for confounders. Our 10-year prospective study suggests that elevated serum HGF levels were significantly associated with the development of IR. © 2012 John Wiley & Sons Ltd.

Are older adults living in more equal counties healthier than older adults living in more unequal counties? A propensity score matching approach

PubMed Central

Choi, HwaJung; Burgard, Sarah; Elo, Irma T.; Heisler, Michele

2015-01-01

We assessed the potential contextual effect of income inequality on health by: 1) comparing individuals with similar socioeconomic status (SES) but who reside in counties with different levels of income inequality; and 2) examining whether the potential effect of county-level income inequality on health varies across SES groups. We used the Health and Retirement Study, a nationally representative study of Americans over the age of 50. Using propensity score matching, we selected SES-comparable individuals living in high-income inequality counties and in low-income inequality counties. We examined differences in self-rated overall health outcomes and in other specific physical/mental health outcomes between the two groups using logistic regression (n=34,994) and imposing different sample restrictions based on residential duration in the area. We then used logistic regression with interactions to assess whether, and if so how, health outcomes differed among participants of different SES groups defined by wealth, income, and education. In bivariate analyses of the unmatched full sample, adults living in high-income inequality counties have worse health outcomes for most health measures. After propensity score matching, adults in high-income inequality counties had worse self-rated health status (AOR=1.12; 95% CI 1.04–1.19) and were more likely to report diagnosed psychiatric problems (AOR=1.08; 95% CI 0.99–1.19) than their matched counterparts in low-income inequality counties. These associations were stronger with longer-term residents in the area. Adverse health outcomes associated with living in high-income inequality counties were significant particularly for individuals in the 30th or greater percentiles of income/wealth distribution and those without a college education. In summary, after using more precise matching methods to compare individuals with similar characteristics and addressing measurement error by excluding more recently arrived county residents, adults living in high-income inequality counties had worse reported overall physical and mental health than adults living in low-income inequality counties. PMID:26256736

Complementary nonparametric analysis of covariance for logistic regression in a randomized clinical trial setting.

PubMed

Tangen, C M; Koch, G G

1999-03-01

In the randomized clinical trial setting, controlling for covariates is expected to produce variance reduction for the treatment parameter estimate and to adjust for random imbalances of covariates between the treatment groups. However, for the logistic regression model, variance reduction is not obviously obtained. This can lead to concerns about the assumptions of the logistic model. We introduce a complementary nonparametric method for covariate adjustment. It provides results that are usually compatible with expectations for analysis of covariance. The only assumptions required are based on randomization and sampling arguments. The resulting treatment parameter is a (unconditional) population average log-odds ratio that has been adjusted for random imbalance of covariates. Data from a randomized clinical trial are used to compare results from the traditional maximum likelihood logistic method with those from the nonparametric logistic method. We examine treatment parameter estimates, corresponding standard errors, and significance levels in models with and without covariate adjustment. In addition, we discuss differences between unconditional population average treatment parameters and conditional subpopulation average treatment parameters. Additional features of the nonparametric method, including stratified (multicenter) and multivariate (multivisit) analyses, are illustrated. Extensions of this methodology to the proportional odds model are also made.

Improving power and robustness for detecting genetic association with extreme-value sampling design.

PubMed

Chen, Hua Yun; Li, Mingyao

2011-12-01

Extreme-value sampling design that samples subjects with extremely large or small quantitative trait values is commonly used in genetic association studies. Samples in such designs are often treated as "cases" and "controls" and analyzed using logistic regression. Such a case-control analysis ignores the potential dose-response relationship between the quantitative trait and the underlying trait locus and thus may lead to loss of power in detecting genetic association. An alternative approach to analyzing such data is to model the dose-response relationship by a linear regression model. However, parameter estimation from this model can be biased, which may lead to inflated type I errors. We propose a robust and efficient approach that takes into consideration of both the biased sampling design and the potential dose-response relationship. Extensive simulations demonstrate that the proposed method is more powerful than the traditional logistic regression analysis and is more robust than the linear regression analysis. We applied our method to the analysis of a candidate gene association study on high-density lipoprotein cholesterol (HDL-C) which includes study subjects with extremely high or low HDL-C levels. Using our method, we identified several SNPs showing a stronger evidence of association with HDL-C than the traditional case-control logistic regression analysis. Our results suggest that it is important to appropriately model the quantitative traits and to adjust for the biased sampling when dose-response relationship exists in extreme-value sampling designs. © 2011 Wiley Periodicals, Inc.

Prevalence of Frailty in Middle-Aged and Older Community-Dwelling Europeans Living in 10 Countries

PubMed Central

Cuénoud, Patrick; Spagnoli, Jacques; Junod, Julien

2009-01-01

Background Frailty is an indicator of health status in old age. Its frequency has been described mainly for North America; comparable data from other countries are lacking. Here we report on the prevalence of frailty in 10 European countries included in a population-based survey. Methods Cross-sectional analysis of 18,227 randomly selected community-dwelling individuals 50 years of age and older, enrolled in the Survey of Health, Aging and Retirement in Europe (SHARE) in 2004. Complete data for assessing a frailty phenotype (exhaustion, shrinking, weakness, slowness, and low physical activity) were available for 16,584 participants. Prevalences of frailty and prefrailty were estimated for individuals 50–64 years and 65 years of age and older from each country. The latter group was analyzed further after excluding disabled individuals. We estimated country effects in this subset using multivariate logistic regression models, controlling first for age, gender, and then demographics and education. Results The proportion of frailty (three to five criteria) or prefrailty (one to two criteria) was higher in southern than in northern Europe. International differences in the prevalences of frailty and prefrailty for 65 years and older group persisted after excluding the disabled. Demographic characteristics did not account for international differences; however, education was associated with frailty. Controlling for education, age and gender diminished the effects of residing in Italy and Spain. Conclusions A higher prevalence of frailty in southern countries is consistent with previous findings of a north–south gradient for other health indicators in SHARE. Our data suggest that socioeconomic factors like education contribute to these differences in frailty and prefrailty. PMID:19276189

The impact of partial resuscitation attempts on the reported outcomes of out-of-hospital cardiac arrest in Victoria, Australia: implications for Utstein-style outcome reports.

PubMed

Nehme, Z; Andrew, E; Bernard, S; Smith, K

2014-09-01

Success rates from cardiopulmonary resuscitation (CPR) are often quantified by Utstein-style outcome reports in populations who receive an attempted resuscitation. In some cases, evidence of futility is ascertained after a partial resuscitation attempt has been administered, and these cases reduce the overall effectiveness of CPR. We examine the impact of partial resuscitation attempts on the reported outcomes of out-of-hospital cardiac arrest (OHCA) in Victoria, Australia. Between 2002 and 2012, 34,849 adult OHCA cases of presumed cardiac aetiology were included from the Victorian Ambulance Cardiac Arrest Registry. Resuscitation attempts lasting ≤10min in cases which died on scene were defined as a partial resuscitation. We used logistic regression to identify factors associated with a partial resuscitation attempt in the emergency medical service (EMS) treated population. Survival outcomes with and without partial resuscitations were compared across included years. The proportion of partial resuscitations in the overall EMS treated population increased significantly from 8.6% in 2002 to 18.8% in 2012 (p for trend<0.001), and were largely supported by documented evidence of irreversible death. Partial resuscitations were independently associated with older age, female gender, initial non-shockable rhythm, prolonged downtime, and lower skill level of EMS personnel. Selectively excluding partial resuscitations increased event survival by 7.6% (95% CI 4.1-11.2%), and survival to hospital discharge increased by 3.1% (95% CI 0.5-5.7%) in 2012 (p<0.001 for both). In our EMS system, evidence of futility was often identified after the commencement of a partial resuscitation attempt. Excluding these events from OHCA outcome reports may better reflect the overall effectiveness of CPR. Crown Copyright © 2014. Published by Elsevier Ireland Ltd. All rights reserved.

Dietary habits during the 2 months following the Chernobyl accident and differentiated thyroid cancer risk in a population-based case-control study.

PubMed

Xhaard, Constance; Rubino, Carole; Souchard, Vincent; Maillard, Stéphane; Ren, Yan; Borson-Chazot, Françoise; Sassolas, Geneviève; Schvartz, Claire; Colonna, Marc; Lacour, Brigitte; Woronoff, Anne Sophie; Velten, Michel; Marrer, Emilie; Bailly, Laurent; Mariné Barjoan, Eugènia; Schlumberger, Martin; Drozdovitch, Vladimir; Bouville, Andre; Orgiazzi, Jacques; Adjadj, Elisabeth; de Vathaire, Florent

2018-02-01

The Chernobyl nuclear power plant accident occurred in Ukraine on April 26th 1986. In France, the radioactive fallout and thyroid radiation doses were much lower than in highly contaminated areas. However, a number of risk projections have suggested that a small excess in differentiated thyroid cancer (DTC) might occur in eastern France due to this low-level fallout. In order to investigate this potential impact, a case-control study on DTC risk factors was started in 2005, focusing on cases who were less than 15 years old at the time of the Chernobyl accident. Here, we aim to evaluate the relationship between some specific reports of potentially contaminated food between April and June 1986 - in particular fresh dairy products and leafy vegetables - and DTC risk. After excluding subjects who were not born before the Chernobyl accident, the study included 747 cases of DTC matched with 815 controls. Odds ratios were calculated using conditional logistic regression models and were reported for all participants, for women only, for papillary cancer only, and excluding microcarcinomas. The DTC risk was slightly higher for participants who had consumed locally produced leafy vegetables. However, this association was not stronger in the more contaminated areas than in the others. Conversely, the reported consumption of fresh dairy products was not statistically associated with DTC risk. Because the increase in DTC risk associated with a higher consumption of locally produced vegetables was not more important in the most contaminated areas, our study lacked power to provide evidence for a strong association between consumption of potentially contaminated food and DTC risk. Copyright © 2017 Elsevier Ltd. All rights reserved.

Interleukin-6 is an independent predictor of progressive atherosclerosis in the carotid artery: The Tromsø Study.

PubMed

Eltoft, Agnethe; Arntzen, Kjell Arne; Wilsgaard, Tom; Mathiesen, Ellisiv B; Johnsen, Stein Harald

2018-04-01

Novel biomarkers are linked to cardiovascular disease (CVD). The aim of the present study was to investigate the association between 28 blood biomarkers and the formation and progression of carotid plaque. In a nested case control study with 703 participants from the population based Tromsø Study, a large biomarker panel was measured in blood obtained at baseline. Carotid ultrasound was assessed both at baseline and at 6 years of follow-up. Four groups were defined: Group 1: no plaque at baseline or at follow-up (reference group); Group 2: novel plaque at follow-up; Group 3: stable plaque at follow-up; Group 4: progression of plaque at follow-up. By multinomial logistic regression analyses, we assessed the risk of being in the different plaque groups with regard to traditional cardiovascular risk factors and levels of biomarkers at baseline. Adjusted for traditional risk factors, interleukin-6 (IL-6) was an independent predictor of plaque progression (OR 1.44, 95% CI 1.12-1.85 per SD increase in IL-6 level). This result remained significant after inclusion of other novel biomarkers to the model, and when subjects with former CVD were excluded. Neopterin was protective of novel plaque formation (OR 0.73, 95% CI 0.57-0.93). Myeloperoxidase and Caspase-1 were independent predictors of plaque progression, but this effect disappeared when excluding subjects with former CVD. IL-6 is an independent predictor of plaque progression, suggesting that it may be a marker of progressive atherosclerosis in the general population and that its central role in CVD may be related to promotion of plaque growth. Copyright © 2018 Elsevier B.V. All rights reserved.

Common polygenic variation enhances risk prediction for Alzheimer’s disease

PubMed Central

Sims, Rebecca; Bannister, Christian; Harold, Denise; Vronskaya, Maria; Majounie, Elisa; Badarinarayan, Nandini; Morgan, Kevin; Passmore, Peter; Holmes, Clive; Powell, John; Brayne, Carol; Gill, Michael; Mead, Simon; Goate, Alison; Cruchaga, Carlos; Lambert, Jean-Charles; van Duijn, Cornelia; Maier, Wolfgang; Ramirez, Alfredo; Holmans, Peter; Jones, Lesley; Hardy, John; Seshadri, Sudha; Schellenberg, Gerard D.; Amouyel, Philippe

2015-01-01

The identification of subjects at high risk for Alzheimer’s disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer’s disease and the accuracy of Alzheimer’s disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer’s Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer’s disease (P = 4.9 × 10−26). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10−19). The best prediction accuracy AUC = 78.2% (95% confidence interval 77–80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer’s disease has a significant polygenic component, which has predictive utility for Alzheimer’s disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes. PMID:26490334

The association between vitamin E intake and hypertension: results from the re-analysis of the National Health and Nutrition Survey.

PubMed

Kuwabara, Akiko; Nakade, Makiko; Tamai, Hiroshi; Tsuboyama-Kasaoka, Nobuyo; Tanaka, Kiyoshi

2014-01-01

Recently, there has been an increasing concern about noncommunicable diseases (NCDs), in which oxidative damage plays a role. In this paper, we have re-analyzed the data from the National Health and Nutrition Survey (NHNS) 2007 to study the relationship between an NCD (e.g. hypertension) and the dietary intake of vitamin E, a potent anti-oxidative vitamin. The inclusion criteria were those aged 40 and over, excluding pregnant or lactating women, and data from 1,405 males and 2,102 females were analyzed. The mean ages were 63.5 and 62.4, respectively. Nutrients intake was evaluated from a semi-weighted, 1-d household dietary record. When the subjects were categorized into tertiles based on their vitamin E intake, higher vitamin E intake was associated with a lower percentage of subjects with hypertension (p for trend=0.01). Subjects with higher vitamin E intake had higher energy intake-adjusted intake of other nutrients which have been considered to be related to hypertension such as potassium, magnesium, and vitamin C. Logistic regression analysis was done with the low tertile of vitamin E intake as the reference. The medium and high tertiles of vitamin E intake were associated with a significantly lower odds ratio for hypertension, 0.73 (95% CI; 0.62-0.87) for the former and 0.81 (95% CI; 0.69-0.96) for the latter. Additional analyses, one adjusted for the indices associated with hypertension and one excluding the subjects with vitamin E supplementation, have yielded the similar results. In summary, re-analysis of data from NHNS has revealed that higher vitamin E intake was significantly associated with lower prevalence of hypertension.

Lethality by pneumonia and factors associated to death.

PubMed

Ferreira, Sidnei; Sant'anna, Clemax C; March, Maria de Fátima B P; Santos, Marilene Augusta R C; Cunha, Antonio Jose Ledo A

2014-01-01

To describe the case-fatality rate (CFR) and risk factors of death in children with community-acquired acute pneumonia (CAP) in a pediatric university hospital. A longitudinal study was developed with prospective data collected from 1996 to 2011. Patients aged 1 month to 12 years were included in the study. Those who left the hospital against medical orders and those transferred to ICU or other units were excluded. Demographic and clinical-etiological characteristics and the initial treatment were studied. Variables associated to death were determined by bivariate and multivariate analysis using logistic regression. A total of 871 patients were selected, of whom 11 were excluded; thus 860 children were included in the study. There were 26 deaths, with a CFR of 3%; in 58.7% of these, penicillin G was the initial treatment. Pneumococcus was the most common pathogen (50.4%). From 1996 to 2000, there were 24 deaths (93%), with a CFR of 5.8% (24/413). From 2001 to 2011, the age group of hospitalized patients was older (p = 0.03), and the number of deaths (p = 0.02) and the percentage of disease severity were lower (p = 0.06). Only disease severity remained associated to death in the multivariate analysis (OR = 3.2; 95%CI: 1.2-8.9; p = 0.02). When the 1996-2000 and 2001-2011 periods were compared, a significant reduction in CFR was observed in the latter, as well as a change in the clinical profile of the pediatric inpatients at the institute. These findings may be related to the improvement in the socio-economical status of the population. Penicillin use did not influence CFR. Copyright © 2013 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Association Between Troponin Levels and Embolic Stroke of Undetermined Source.

PubMed

Merkler, Alexander E; Gialdini, Gino; Murthy, Santosh B; Salehi Omran, Setareh; Moya, Antonio; Lerario, Michael P; Chong, Ji; Okin, Peter M; Weinsaft, Jonathan W; Safford, Monika M; Fink, Matthew E; Navi, Babak B; Iadecola, Costantino; Kamel, Hooman

2017-09-22

Our aim was to determine whether patients with embolic strokes of undetermined source (ESUS) have higher rates of elevated troponin than patients with noncardioembolic strokes. CAESAR (The Cornell Acute Stroke Academic Registry) prospectively enrolled all adults with acute stroke from 2011 to 2014. Two neurologists used standard definitions to retrospectively ascertain the etiology of stroke, with a third resolving disagreements. In this analysis we included patients with ESUS and, as controls, patients with small- and large-artery strokes; only patients with a troponin measured within 24 hours of stroke onset were included. A troponin elevation was defined as a value exceeding our laboratory's upper limit (0.04 ng/mL) without a clinically recognized acute ST-segment elevation myocardial infarction. Multiple logistic regression was used to evaluate the association between troponin elevation and ESUS after adjustment for demographics, stroke severity, insular infarction, and vascular risk factors. In a sensitivity analysis we excluded patients diagnosed with atrial fibrillation after discharge. Among 512 patients, 243 (47.5%) had ESUS, and 269 (52.5%) had small- or large-artery stroke. In multivariable analysis an elevated troponin was independently associated with ESUS (odds ratio 3.3; 95% confidence interval 1.2, 8.8). This result was unchanged after excluding patients diagnosed with atrial fibrillation after discharge (odds ratio 3.4; 95% confidence interval 1.3, 9.1), and the association remained significant when troponin was considered a continuous variable (odds ratio for log[troponin], 1.4; 95% confidence interval 1.1, 1.7). Elevations in cardiac troponin are more common in patients with ESUS than in those with noncardioembolic strokes. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Evaluation of methodology for the analysis of 'time-to-event' data in pharmacogenomic genome-wide association studies.

PubMed

Syed, Hamzah; Jorgensen, Andrea L; Morris, Andrew P

2016-06-01

To evaluate the power to detect associations between SNPs and time-to-event outcomes across a range of pharmacogenomic study designs while comparing alternative regression approaches. Simulations were conducted to compare Cox proportional hazards modeling accounting for censoring and logistic regression modeling of a dichotomized outcome at the end of the study. The Cox proportional hazards model was demonstrated to be more powerful than the logistic regression analysis. The difference in power between the approaches was highly dependent on the rate of censoring. Initial evaluation of single-nucleotide polymorphism association signals using computationally efficient software with dichotomized outcomes provides an effective screening tool for some design scenarios, and thus has important implications for the development of analytical protocols in pharmacogenomic studies.

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

Butler, W.J.; Kalasinski, L.A.

In this paper, a generalized logistic regression model for correlated observations is used to analyze epidemiologic data on the frequency of spontaneous abortion among a group of women office workers. The results are compared to those obtained from the use of the standard logistic regression model that assumes statistical independence among all the pregnancies contributed by one woman. In this example, the correlation among pregnancies from the same woman is fairly small and did not have a substantial impact on the magnitude of estimates of parameters of the model. This is due at least partly to the small average numbermore » of pregnancies contributed by each woman.« less

Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.

PubMed

Jovanovic, Milos; Radovanovic, Sandro; Vukicevic, Milan; Van Poucke, Sven; Delibasic, Boris

2016-09-01

Quantification and early identification of unplanned readmission risk have the potential to improve the quality of care during hospitalization and after discharge. However, high dimensionality, sparsity, and class imbalance of electronic health data and the complexity of risk quantification, challenge the development of accurate predictive models. Predictive models require a certain level of interpretability in order to be applicable in real settings and create actionable insights. This paper aims to develop accurate and interpretable predictive models for readmission in a general pediatric patient population, by integrating a data-driven model (sparse logistic regression) and domain knowledge based on the international classification of diseases 9th-revision clinical modification (ICD-9-CM) hierarchy of diseases. Additionally, we propose a way to quantify the interpretability of a model and inspect the stability of alternative solutions. The analysis was conducted on >66,000 pediatric hospital discharge records from California, State Inpatient Databases, Healthcare Cost and Utilization Project between 2009 and 2011. We incorporated domain knowledge based on the ICD-9-CM hierarchy in a data driven, Tree-Lasso regularized logistic regression model, providing the framework for model interpretation. This approach was compared with traditional Lasso logistic regression resulting in models that are easier to interpret by fewer high-level diagnoses, with comparable prediction accuracy. The results revealed that the use of a Tree-Lasso model was as competitive in terms of accuracy (measured by area under the receiver operating characteristic curve-AUC) as the traditional Lasso logistic regression, but integration with the ICD-9-CM hierarchy of diseases provided more interpretable models in terms of high-level diagnoses. Additionally, interpretations of models are in accordance with existing medical understanding of pediatric readmission. Best performing models have similar performances reaching AUC values 0.783 and 0.779 for traditional Lasso and Tree-Lasso, respectfully. However, information loss of Lasso models is 0.35 bits higher compared to Tree-Lasso model. We propose a method for building predictive models applicable for the detection of readmission risk based on Electronic Health records. Integration of domain knowledge (in the form of ICD-9-CM taxonomy) and a data-driven, sparse predictive algorithm (Tree-Lasso Logistic Regression) resulted in an increase of interpretability of the resulting model. The models are interpreted for the readmission prediction problem in general pediatric population in California, as well as several important subpopulations, and the interpretations of models comply with existing medical understanding of pediatric readmission. Finally, quantitative assessment of the interpretability of the models is given, that is beyond simple counts of selected low-level features. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating a Logistic Discrimination Functions When One of the Training Samples Is Subject to Misclassification: A Maximum Likelihood Approach.

PubMed

Nagelkerke, Nico; Fidler, Vaclav

2015-01-01

The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. diseased individuals are incorrectly classified/labeled as healthy controls. We show that this leads to zero-inflated binomial model with a defective logistic regression or discrimination function, whose parameters can be estimated using standard statistical methods such as maximum likelihood. These parameters can be used to estimate the probability of true group membership among those, possibly erroneously, classified as controls. Two examples are analyzed and discussed. A simulation study explores properties of the maximum likelihood parameter estimates and the estimates of the number of mislabeled observations.

Ethnicity and Psychiatric Disorders

PubMed Central

Hawes, Armani M.; Axinn, William G.; Ghimire, Dirgha J.

2016-01-01

Psychiatric disorders are one of the leading causes of disease-related disability in the world today. However, little is known about the ethnic variation of these disorders within populations. This is especially true in contexts outside of the United States and the European Diaspora. This study provides new evidence from South Asia on ethnic differences in Major Depressive Episode, Major Depressive Disorder, Panic Attack, Panic Disorder, Post-Traumatic Stress Disorder, and Intermittent Explosive Disorder. We use data from 400 adult interviews conducted in Nepal in a controlled comparison design as a case study. We use a series of multilevel logistic regression models to predict ethnic group differences in psychiatric disorders and episodes with measures from clinically validated World Mental Health survey instruments. Compared to the Brahmin/Chhetri group, we found historically excluded Dalits had statistically significantly higher odds of almost all psychiatric disorders and episodes. We also found that historically resilient Janajatis had statistically significantly lower odds of being diagnosed with PTSD than the majority Brahmin/Chhetri group. We also found no significant gender difference in MDD or MDE. Psychiatric disorders and episodes vary significantly by ethnicity within a rural Asian population, but gender differences are small. PMID:28824961

Unhealthy food in relation to posttraumatic stress symptoms among adolescents.

PubMed

Vilija, Malinauskiene; Romualdas, Malinauskas

2014-03-01

The linkage between mood states and unhealthy food consumption has been under investigation in the recent years. This study aimed to evaluate the associations between posttraumatic stress (PTS) symptoms after lifetime traumatic experiences and daily unhealthy food consumption among adolescents, taking into account the possible effects of physical inactivity, smoking, and a sense of coherence. A self-administered questionnaire measured symptoms of PTS, lifetime traumatic experiences, food frequency scale, sense of coherence scale in a representative sample of eighth grade pupils of the Kaunas, Lithuania, secondary schools (N=1747; 49.3% girls and 50.7% boys). In the logistic regression models, all lifetime traumatic events were associated with PTS symptoms, as well as were unhealthy foods, (including light alcoholic drinks, spirits, soft and energy drinks, flavored milk, coffee, fast food, chips and salty snacks, frozen processed foods; excluding sweet snacks, biscuits and pastries) and sense of coherence weakened the strength of the associations. However, physical inactivity and smoking showed no mediating effect for the majority of unhealthy foods. In conclusion, we found that intervention and preventive programs on PTS symptoms may be beneficial while dealing with behavioral problems (unhealthy diet, smoking, alcohol, physical inactivity) among adolescents. Copyright © 2013 Elsevier Ltd. All rights reserved.

Risk factors for postoperative complications following oral surgery.

PubMed

Shigeishi, Hideo; Ohta, Kouji; Takechi, Masaaki

2015-01-01

The objective of this study was to clarify significant risk factors for postoperative complications in the oral cavity in patients who underwent oral surgery, excluding those with oral cancer. This study reviewed the records of 324 patients who underwent mildly to moderately invasive oral surgery (e.g., impacted tooth extraction, cyst excision, fixation of mandibular and maxillary fractures, osteotomy, resection of a benign tumor, sinus lifting, bone grafting, removal of a sialolith, among others) under general anesthesia or intravenous sedation from 2012 to 2014 at the Department of Oral and Maxillofacial Reconstructive Surgery, Hiroshima University Hospital. Univariate analysis showed a statistical relationship between postoperative complications (i.e., surgical site infection, anastomotic leak) and diabetes (p=0.033), preoperative serum albumin level (p=0.009), and operation duration (p=0.0093). Furthermore, preoperative serum albumin level (<4.0 g/dL) and operation time (≥120 minutes) were found to be independent factors affecting postoperative complications in multiple logistic regression analysis results (odds ratio 3.82, p=0.0074; odds ratio 2.83, p=0.0086, respectively). Our results indicate that a low level of albumin in serum and prolonged operation duration are important risk factors for postoperative complications occurring in the oral cavity following oral surgery.

Factors associated with self-rated health among migrant workers: results from a population-based cross-sectional study in Almaty, Kazakhstan.

PubMed

Kumparatana, Pam; Cournos, Francine; Terlikbayeva, Assel; Rozental, Yelena; Gilbert, Louisa

2017-06-01

To determine factors associated with SRH among migrant workers in Almaty, Kazakhstan. In 2007, 805 vendors were screened. Approximately half were eligible (n =450), defined as at least 18 years old, a worker/owner in a randomly selected stall, having traveled 2 + hours outside of Almaty within the past year, and being an internal/external migrant. 28 non-migrants were excluded, leaving 422 participants. Logistic regression was used to examine the relationship between SRH, mental health, and psychosocial problems. Approximately 46% reported having poor or fair SRH. Clinical depression (OR 0.859, 95% CI 0.342-2.154), alcohol problems (OR 1.169, 95% CI 0.527-2.593), and legal status (OR 0.995, 95% CI 0.806-1.229) were not significantly associated with SRH, nor was exposure to interpersonal violence among women (OR 1.554, 95% CI 0.703-3.435). After adjusting for key variables, only ethnicity and social support were found to be significantly protective against poor or fair SRH. SRH was not a comprehensive health measure for these Central Asian migrant workers. More specific questions are needed to identify mental illness and interpersonal violence.