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

NASA Astrophysics Data System (ADS)

Ariffin, Syaiba Balqish; Midi, Habshah

2014-06-01

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

Evaluating guideline adherence regarding empirical vancomycin use in patients with neutropenic fever.

PubMed

Chastain, Daniel B; Wheeler, Sarah; Franco-Paredes, Carlos; Olubajo, Babatunde; Hawkins, W Anthony

2018-04-01

The purpose of this study was to evaluate the use of empirical vancomycin for patients with neutropenic fever (NF) with regard to adherence to treatment guidelines. Adult patients with a diagnosis of neutropenia, who met the definition of NF as per treatment guidelines, were identified. Use of vancomycin was evaluated as part of empirical therapy and again after 72h. Outcomes were assessed using descriptive statistics, the Chi-square or Fisher's exact test, and univariate exact logistic regression analyses. Sixty-four patients were included. Overall, inappropriate empirical vancomycin use was observed in more than 30% of patients. Of 35 patients with indications for empirical vancomycin, only 68% received it. At 72h, appropriate vancomycin continuation, de-escalation, or discontinuation occurred in 21 of 33 patients. On univariate regression, hematological malignancy was associated with appropriate empirical vancomycin prescribing, whether initiating or withholding (odds ratio 4.0, 95% confidence interval 1.31-12.1). No variable was independently associated with inappropriate continuation at 72h. There is poor guideline adherence to vancomycin prescribing as empirical therapy and at 72-h reassessment in patients with NF. Further efforts are needed to foster a more rational use of vancomycin in patients with NF. Copyright © 2018. Published by Elsevier Ltd.

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…

The special case of the 2 × 2 table: asymptotic unconditional McNemar test can be used to estimate sample size even for analysis based on GEE.

PubMed

Borkhoff, Cornelia M; Johnston, Patrick R; Stephens, Derek; Atenafu, Eshetu

2015-07-01

Aligning the method used to estimate sample size with the planned analytic method ensures the sample size needed to achieve the planned power. When using generalized estimating equations (GEE) to analyze a paired binary primary outcome with no covariates, many use an exact McNemar test to calculate sample size. We reviewed the approaches to sample size estimation for paired binary data and compared the sample size estimates on the same numerical examples. We used the hypothesized sample proportions for the 2 × 2 table to calculate the correlation between the marginal proportions to estimate sample size based on GEE. We solved the inside proportions based on the correlation and the marginal proportions to estimate sample size based on exact McNemar, asymptotic unconditional McNemar, and asymptotic conditional McNemar. The asymptotic unconditional McNemar test is a good approximation of GEE method by Pan. The exact McNemar is too conservative and yields unnecessarily large sample size estimates than all other methods. In the special case of a 2 × 2 table, even when a GEE approach to binary logistic regression is the planned analytic method, the asymptotic unconditional McNemar test can be used to estimate sample size. We do not recommend using an exact McNemar test. Copyright © 2015 Elsevier Inc. All rights reserved.

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…

Variation in the prevalence of chronic bronchitis among smokers: a cross-sectional study.

PubMed

Mahesh, P A; Jayaraj, B S; Chaya, S K; Lokesh, K S; McKay, A J; Prabhakar, A K; Pape, U J

2014-07-01

Given the wide variations in prevalence of chronic obstructive pulmonary disease observed between populations with similar levels of exposure to tobacco smoke, we aimed to investigate the possibility of variations in prevalence of chronic bronchitis (CB) between two geographically distinct smoking populations in rural Karnataka, India. The Burden of Obstructive Lung Disease (BOLD) questionnaire was administered to all men aged >30 years in a cross-sectional survey. The χ(2) and Fisher's exact tests were used to compare CB prevalence in the two populations. Logistic regression was used to analyse the impact of multiple variables on the occurrence of CB. Two samples of 2322 and 2182 subjects were included in the study. In non-smokers, CB prevalence did not differ between the populations. However, it was significantly different between smoking populations (44.79% vs. 2.13%, P < 0.0001). Logistic regression indicated that, in addition to smoking, region, age, occupational dust exposure and type of house were associated with higher likelihood of CB. An interaction between smoking and area of residence was found (P < 0.001) and appeared to explain the effect of region (without interaction). A significant difference in CB prevalence was observed between male populations from two areas of Karnataka state, including when stratified by smoking status. No significant difference was observed between non-smokers.

[Diabetic Foot Neuropathy and Related Factors in Patients With Type 2 Diabetes Mellitus].

PubMed

Chen, Tzu-Yu; Lin, Chia-Huei; Chang, Yue-Cune; Wang, Chih-Hsin; Hung, Yi-Jen; Tzeng, Wen-Chii

2018-06-01

Patients with type 2 diabetes mellitus (T2DM) face a higher risk of diabetic foot neuropathy, which increases the risk of death. The early detection of factors that influence diabetic neuropathy reduces the risk of foot lesions, including foot ulcerations, lower extremity amputation, and mortality. To explore the demographic, disease-characteristic, health-literacy, and foot-self-care-behavior factors that affect diabetic foot neuropathy in patients with T2DM. A case-control study design was employed in which cases (Michigan Neuropathy Screening Instrument, MNSI) ≥ 2 were matched to controls based on age and gender in a medical center. A total of 114 patients diagnosed with T2DM in a medical center were recruited as participants. Data were collected using a structured questionnaire. The collected data were analyzed using Fisher's exact test, Mann-Whitney U test, and logistic regression. The results of multiple logistic regression showed that glycated hemoglobin (B = 1.696, p = .041) and communication and critical health literacy (B = -0.082, p = .034) were significant factors of diabetic foot neuropathy. The findings of this study suggest that nurses should assess the health literacy of patients with T2DM before providing health education and should develop a specific foot-care intervention for individuals with poor glycemic control.

Knowledge, Attitudes, and Substance Use Practices Among Street Children in Western Kenya

PubMed Central

Embleton, Lonnie; Ayuku, David; Atwoli, Lukoye; Vreeman, Rachel; Braitstein, Paula

2013-01-01

The study describes the knowledge of and attitudes toward substance use among street-involved youth in Kenya, and how they relate to their substance use practices. In 2011, 146 children and youth ages 10–19 years, classified as either children on the street or children of the street were recruited to participate in a cross-sectional survey in Eldoret, Kenya. Bivariate analysis using χ2 or Fisher’s Exact Test was used to test the associations between variables, and multiple logistic regression analysis was used to identify independent covariates associated with lifetime and current drug use. The study’s limitations and source of funding are noted. PMID:22780841

Sociodemographic factors associated with pregnant women's level of knowledge about oral health

PubMed Central

Barbieri, Wander; Peres, Stela Verzinhasse; Pereira, Carla de Britto; Peres, João; de Sousa, Maria da Luz Rosário; Cortellazzi, Karine Laura

2018-01-01

ABSTRACT Objective To evaluate knowledge on oral health and associated sociodemographic factors in pregnant women. Methods A cross-sectional study with a sample of 195 pregnant women seen at the Primary Care Unit Paraisópolis I, in São Paulo (SP), Brazil. For statistical analysis, χ2 or Fisher's exact test and multiple logistic regression were used. A significance level of 5% was used in all analyses. Results Schooling level equal to or greater than 8 years and having one or two children were associated with an adequate knowledge about oral health. Conclusion Oral health promotion strategies during prenatal care should take into account sociodemographic aspects. PMID:29694612

Incidence and predictors of onset of injection drug use in a San Francisco cohort of homeless youth.

PubMed

Parriott, Andrea M; Auerswald, Colette L

2009-01-01

Few studies document incidence of injection drug use among homeless youth. We followed a cohort of 70 street-recruited homeless youth in San Francisco, California who had never injected drugs for six months in 2004-5. We examined initiation of injection drug use and its predictors, informed by prior ethnographic findings. Data were analyzed using exact logistic regression. 11.4% of youth initiated injection drug use. Having no high school education, being over 21 years old, and being in disequilibrium predicted initiation. Limitations, implications and suggestions for future research are noted. Funding was provided by the National Institute for Child Health and Development.

Who Gets Promoted? Gender Differences in Science and Engineering Academia

NASA Astrophysics Data System (ADS)

Olson, Kristen

Using a nationally representative sample of doctoral academic scientists and engineers, this study examines gender differences in the likelihood of having tenure and senior faculty ranks after controlling for academic age, field, doctoral origins, employing educational institution, productivity, postdoctoral positions, work activities, and family characteristics. Logistic regressions show that many of these controls are significant; that biology and employment at comprehensive universities have a gender-specific advantage for women; and that postdoctoral positions, teaching instead of doing administrative work, and having children have a gender-specific disadvantage. Although the statistical methods employed here do not reveal the exact nature of how gender inequities in science and engineering careers arise, the author suggests that they exist.

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.

Accounting for center in the Early External Cephalic Version trials: an empirical comparison of statistical methods to adjust for center in a multicenter trial with binary outcomes.

PubMed

Reitsma, Angela; Chu, Rong; Thorpe, Julia; McDonald, Sarah; Thabane, Lehana; Hutton, Eileen

2014-09-26

Clustering of outcomes at centers involved in multicenter trials is a type of center effect. The Consolidated Standards of Reporting Trials Statement recommends that multicenter randomized controlled trials (RCTs) should account for center effects in their analysis, however most do not. The Early External Cephalic Version (EECV) trials published in 2003 and 2011 stratified by center at randomization, but did not account for center in the analyses, and due to the nature of the intervention and number of centers, may have been prone to center effects. Using data from the EECV trials, we undertook an empirical study to compare various statistical approaches to account for center effect while estimating the impact of external cephalic version timing (early or delayed) on the outcomes of cesarean section, preterm birth, and non-cephalic presentation at the time of birth. The data from the EECV pilot trial and the EECV2 trial were merged into one dataset. Fisher's exact method was used to test the overall effect of external cephalic version timing unadjusted for center effects. Seven statistical models that accounted for center effects were applied to the data. The models included: i) the Mantel-Haenszel test, ii) logistic regression with fixed center effect and fixed treatment effect, iii) center-size weighted and iv) un-weighted logistic regression with fixed center effect and fixed treatment-by-center interaction, iv) logistic regression with random center effect and fixed treatment effect, v) logistic regression with random center effect and random treatment-by-center interaction, and vi) generalized estimating equations. For each of the three outcomes of interest approaches to account for center effect did not alter the overall findings of the trial. The results were similar for the majority of the methods used to adjust for center, illustrating the robustness of the findings. Despite literature that suggests center effect can change the estimate of effect in multicenter trials, this empirical study does not show a difference in the outcomes of the EECV trials when accounting for center effect. The EECV2 trial was registered on 30 July 30 2005 with Current Controlled Trials: ISRCTN 56498577.

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…

Epidemiology of Human Papillomavirus Detected in the Oral Cavity and Fingernails of Mid-Adult Women.

PubMed

Fu, Tsung-chieh Jane; Hughes, James P; Feng, Qinghua; Hulbert, Ayaka; Hawes, Stephen E; Xi, Long Fu; Schwartz, Stephen M; Stern, Joshua E; Koutsky, Laura A; Winer, Rachel L

2015-12-01

Oral and fingernail human papillomavirus (HPV) detection may be associated with HPV-related carcinoma risk at these nongenital sites and foster transmission to the genitals. We describe the epidemiology of oral and fingernail HPV among mid-adult women. Between 2011 and 2012, 409 women aged 30 to 50 years were followed up for 6 months. Women completed health and behavior surveys and provided self-collected oral, fingernail, and vaginal specimens at enrollment and exit for type-specific HPV DNA testing. Concordance of type-specific HPV detection across anatomical sites was described with κ statistics. Using generalized estimating equations or exact logistic regression, we measured the univariate associations of various risk factors with type-specific oral and fingernail HPV detection. Prevalence of detecting HPV in the oral cavity (2.4%) and fingernails (3.8%) was low compared with the vagina (33.1%). Concordance across anatomical sites was poor (κ < 0.20 for all comparisons). However, concurrent vaginal infection with the same HPV type (odds ratio [OR], 101.0; 95% confidence interval [CI], 31.4-748.6) and vaginal HPV viral load (OR per 1 log10 viral load increase, 2.2; 95% CI, 1.5-5.5) were each associated with fingernail HPV detection. Abnormal Papanicolaou history (OR, 11.1; 95% CI, 2.8-infinity), lifetime number of male vaginal sex partners at least 10 (OR vs. 0-3 partners, 5.0; 95% CI, 1.2-infinity), and lifetime number of open-mouth kissing partners at least 16 (OR vs. 0-15 partners, infinity; 95% CI, 2.6-infinity, by exact logistic regression) were each associated with oral HPV detection. Although our findings support HPV DNA deposition or autoinoculation between anatomical sites in mid-adult women, the rarity of HPV in the oral cavity and fingernails suggests that oral/fingernail HPV does not account for a significant fraction of HPV in genital sites.

Epidemiology of Human Papillomavirus (HPV) Detected in the Oral Cavity and Fingernails of Mid-Adult Women

PubMed Central

Fu, Tsung-chieh (Jane); Hughes, James P.; Feng, Qinghua; Hulbert, Ayaka; Hawes, Stephen E.; Xi, Long Fu; Schwartz, Stephen M.; Stern, Joshua E.; Koutsky, Laura A.; Winer, Rachel L.

2015-01-01

Background Oral and fingernail human papillomavirus (HPV) detection may be associated with HPV-related carcinoma risk at these non-genital sites and foster transmission to the genitals. We describe the epidemiology of oral and fingernail HPV among mid-adult women. Methods Between 2011–2012, 409 women aged 30–50 years were followed for 6 months. Women completed health and behavior surveys and provided self-collected oral, fingernail, and vaginal specimens at enrollment and exit for type-specific HPV DNA testing. Concordance of type-specific HPV detection across anatomic sites was described with kappa statistics. Using generalized estimating equations or exact logistic regression, we measured the univariate associations of various risk factors with type-specific oral and fingernail HPV detection. Results Prevalence of detecting HPV in the oral cavity (2.4%) and fingernails (3.8%) was low compared to the vagina (33.1%). Concordance across anatomic sites was poor (kappa<.20 for all comparisons). However, concurrent vaginal infection with the same HPV type (OR=101.0;95%CI: 31.4–748.6) and vaginal HPV viral load (OR per one log10 viral load increase=2.2;95%CI:1.5–5.5) were each associated with fingernail HPV detection. Abnormal Pap history (OR=11.1;95%CI:2.8-infinity), lifetime number of male vaginal sex partners ≥10 (OR vs. 0–3 partners=5.0;95%CI:1.2-infinity), and lifetime number of open-mouth kissing partners ≥16 (OR vs. 0–15 partners=infinity;95%CI:2.6-infinity, by exact logistic regression) were each associated with oral HPV detection. Conclusions While our findings support HPV DNA deposition or autoinoculation between anatomic sites in mid-adult women, the rarity of HPV in the oral cavity and fingernails suggests that oral/fingernail HPV does not account for a significant fraction of HPV in genital sites. PMID:26562696

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.

Color vision impairment in multiple sclerosis points to retinal ganglion cell damage.

PubMed

Lampert, E J; Andorra, M; Torres-Torres, R; Ortiz-Pérez, S; Llufriu, S; Sepúlveda, M; Sola, N; Saiz, A; Sánchez-Dalmau, B; Villoslada, P; Martínez-Lapiscina, Elena H

2015-11-01

Multiple Sclerosis (MS) results in color vision impairment regardless of optic neuritis (ON). The exact location of injury remains undefined. The objective of this study is to identify the region leading to dyschromatopsia in MS patients' NON-eyes. We evaluated Spearman correlations between color vision and measures of different regions in the afferent visual pathway in 106 MS patients. Regions with significant correlations were included in logistic regression models to assess their independent role in dyschromatopsia. We evaluated color vision with Hardy-Rand-Rittler plates and retinal damage using Optical Coherence Tomography. We ran SIENAX to measure Normalized Brain Parenchymal Volume (NBPV), FIRST for thalamus volume and Freesurfer for visual cortex areas. We found moderate, significant correlations between color vision and macular retinal nerve fiber layer (rho = 0.289, p = 0.003), ganglion cell complex (GCC = GCIP) (rho = 0.353, p < 0.001), thalamus (rho = 0.361, p < 0.001), and lesion volume within the optic radiations (rho = -0.230, p = 0.030). Only GCC thickness remained significant (p = 0.023) in the logistic regression model. In the final model including lesion load and NBPV as markers of diffuse neuroaxonal damage, GCC remained associated with dyschromatopsia [OR = 0.88 95 % CI (0.80-0.97) p = 0.016]. This association remained significant when we also added sex, age, and disease duration as covariates in the regression model. Dyschromatopsia in NON-eyes is due to damage of retinal ganglion cells (RGC) in MS. Color vision can serve as a marker of RGC damage in MS.

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.

Risk factors for indications of intraoperative blood transfusion among patients undergoing surgical treatment for colorectal adenocarcinoma.

PubMed

Gonçalves, Iara; Linhares, Marcelo; Bordin, Jose; Matos, Delcio

2009-01-01

Identification of risk factors for requiring transfusions during surgery for colorectal cancer may lead to preventive actions or alternative measures, towards decreasing the use of blood components in these procedures, and also rationalization of resources use in hemotherapy services. This was a retrospective case-control study using data from 383 patients who were treated surgically for colorectal adenocarcinoma at 'Fundação Pio XII', in Barretos-SP, Brazil, between 1999 and 2003. To recognize significant risk factors for requiring intraoperative blood transfusion in colorectal cancer surgical procedures. Univariate analyses were performed using Fisher's exact test or the chi-squared test for dichotomous variables and Student's t test for continuous variables, followed by multivariate analysis using multiple logistic regression. In the univariate analyses, height (P = 0.06), glycemia (P = 0.05), previous abdominal or pelvic surgery (P = 0.031), abdominoperineal surgery (P<0.001), extended surgery (P<0.001) and intervention with radical intent (P<0.001) were considered significant. In the multivariate analysis using logistic regression, intervention with radical intent (OR = 10.249, P<0.001, 95% CI = 3.071-34.212) and abdominoperineal amputation (OR = 3.096, P = 0.04, 95% CI = 1.445-6.623) were considered to be independently significant. This investigation allows the conclusion that radical intervention and the abdominoperineal procedure in the surgical treatment of colorectal adenocarcinoma are risk factors for requiring intraoperative blood transfusion.

Factors associated with abnormal eating attitudes among Greek adolescents.

PubMed

Bilali, Aggeliki; Galanis, Petros; Velonakis, Emmanuel; Katostaras, Theofanis

2010-01-01

To estimate the prevalence of abnormal eating attitudes among Greek adolescents and identify possible risk factors associated with these attitudes. Cross-sectional, school-based study. Six randomly selected schools in Patras, southern Greece. The study population consisted of 540 Greek students aged 13-18 years, and the response rate was 97%. The dependent variable was scores on the Eating Attitudes Test-26, with scores > or = 20 indicating abnormal eating attitudes. Bivariate analysis included independent Student t test, chi-square test, and Fisher's exact test. Multivariate logistic regression analysis was applied for the identification of the predictive factors, which were associated independently with abnormal eating attitudes. A 2-sided P value of less than .05 was considered statistically significant. The prevalence of abnormal eating attitudes was 16.7%. Multivariate logistic regression analysis demonstrated that females, urban residents, and those with a body mass index outside normal range, a perception of being overweight, body dissatisfaction, and a family member on a diet were independently related to abnormal eating attitudes. The results indicate that a proportion of Greek adolescents report abnormal eating attitudes and suggest that multiple factors contribute to the development of these attitudes. These findings are useful for further research into this topic and would be valuable in designing preventive interventions. Copyright 2010 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.

Prevalence of Tobacco Smoking and Factors Associated with the Initiation of Smoking among University Students in Dhaka, Bangladesh.

PubMed

Hossain, Sahadat; Hossain, Shakhaoat; Ahmed, Fahad; Islam, Rabiul; Sikder, Tajuddin; Rahman, Abdur

2017-01-01

Tobacco smoking is considered to be the key preventable risk factor for morbidity and mortality at the global level. The aim of this study was to determine the prevalence of tobacco smoking and factors associated with the initiation of smoking among university students in Dhaka, Bangladesh. A cross-sectional survey study was conducted with 264 students of Jahangirnagar University, Dhaka, Bangladesh in 2015. A standard, self-administered questionnaire consisting of questions on socio-demographic variables, tobacco smoking status, family and peer tobacco smoking history, attitudes and beliefs about tobacco smoking, as well as knowledge about the negative health consequences of tobacco smoking was administered to participants. Data were analyzed using logistic regression models, chi square, and Fisher exact tests. The overall prevalence of tobacco smoking was 60.2%, where males smoked at higher rates than females (68.81% and 19.56%, respectively). The influence of friends was the most significant reason for initiating tobacco smoking (OR: 0.862; CI: 0.810-0.917). Perception regarding tobacco smoking was significantly related to continuing tobacco use. Logistic regression models identified that smoking-related attitudes, potential health problems, and family members dying from cardiovascular disease and cancer were significantly associated with tobacco smoking. The current tobacco smoking prevalence among university students in Bangladesh is over 60%. We suggest adopting WHO Framework Convention on Tobacco Control (FCTC) policies, especially for university students.

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

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

EPA Science Inventory

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

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

DTIC Science & Technology

2015-06-01

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

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

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2005-01-01

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

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

PubMed

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

2014-09-01

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

Logistic Regression: Concept and Application

ERIC Educational Resources Information Center

Cokluk, Omay

2010-01-01

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

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

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

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

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

PubMed Central

Weiss, Brandi A.; Dardick, William

2015-01-01

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

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

NASA Astrophysics Data System (ADS)

Guns, M.; Vanacker, V.

2012-06-01

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

Large unbalanced credit scoring using Lasso-logistic regression ensemble.

PubMed

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

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

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

PubMed

Weiss, Brandi A; Dardick, William

2016-12-01

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

Exact and Approximate Statistical Inference for Nonlinear Regression and the Estimating Equation Approach.

PubMed

Demidenko, Eugene

2017-09-01

The exact density distribution of the nonlinear least squares estimator in the one-parameter regression model is derived in closed form and expressed through the cumulative distribution function of the standard normal variable. Several proposals to generalize this result are discussed. The exact density is extended to the estimating equation (EE) approach and the nonlinear regression with an arbitrary number of linear parameters and one intrinsically nonlinear parameter. For a very special nonlinear regression model, the derived density coincides with the distribution of the ratio of two normally distributed random variables previously obtained by Fieller (1932), unlike other approximations previously suggested by other authors. Approximations to the density of the EE estimators are discussed in the multivariate case. Numerical complications associated with the nonlinear least squares are illustrated, such as nonexistence and/or multiple solutions, as major factors contributing to poor density approximation. The nonlinear Markov-Gauss theorem is formulated based on the near exact EE density approximation.

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…

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.

Is there a relationship between periodontal conditions and number of medications among the elderly?

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alshaeri, Heba K; Alasqah, Mohammed; Papas, Athena

2016-03-01

To investigate possible correlations of clinical attachment level and pocket depth with number of medications in elderly individuals. Intra-oral examinations for 139 patients visiting Tufts dental clinic were done. Periodontal assessments were performed with a manual UNC-15 periodontal probe to measure probing depth (PD) and clinical attachment level (CAL) at 6 sites. Complete lists of patients' medications were obtained during the examinations. Statistical analysis involved Kruskal-Wallis, chi square and multivariate logistic regression analyses. Age and health status attained statistical significance (p< 0.05), in contingency table analysis with number of medications. Number of medications had an effect on CAL: increased attachment loss was observed when 4 or more medications were being taken by the patient. Number of medications did not have any effect on periodontal PD. In multivariate logistic regression analysis, 6 or more medications had a higher risk of attachment loss (>3mm) when compared to the no-medication group, in crude OR (1.20, 95% CI:0.22-6.64), and age adjusted (OR=1.16, 95% CI:0.21-6.45), but not with the multivariate model (OR=0.71, 95% CI:0.11-4.39). CAL seems to be more sensitive to the number of medications taken, when compared to PD. However, it is not possible to discriminate at exactly what number of drug combinations the breakdown in CAL will happen. We need to do further analysis, including more subjects, to understand the possible synergistic mechanisms for different drug and periodontal responses.

Magnetic Resonance Imaging Findings Predict the Recurrence of Chronic Subdural Hematoma

PubMed Central

GOTO, Haruo; ISHIKAWA, Osamu; NOMURA, Masashi; TANAKA, Kentaro; NOMURA, Seiji; MAEDA, Keiichiro

2015-01-01

The exact predictive factors for postoperative recurrence of chronic subdural hematoma (CSDH) are still unknown. Based on the preoperative magnetic resonance imaging (MRI), low recurrence rate of T1-hyperintensity hematoma was previously reported. We investigated the other types of radiological findings which are related to the recurrence rate of CSDH in large number of patients analyzed by multivariate logistic regression model. Preoperative MRI and postoperative computed tomography (CT) were performed and the influence of the preoperative use of antiplatelet or anticoagulant drugs was also studied. The overall recurrence rate was 9.3% (47 of 505 hematomas). The MRI T1-iso/hypointensity group showed a significantly higher recurrence rate (18.2%, 29 of 159) compared to the other groups (5.2%, 18 of 346; p < 0.001). Multivariate logistic regression analysis showed T1 classification was the solo significant prognostic predictor among various factors such as bilateral hematoma, antiplatelet or anticoagulant drug usage, residual hematoma on postoperative CT, and MRI classification (p < 0.001): adjusted odds ratio for the recurrence in T1-iso/hypointensity group relative to the T1-hyperintensity group was 5.58 [95% confidence interval (CI), 2.09–14.86] (p = 0.001). Postoperative residual hematoma and antiplatelet or anticoagulant drug usage did not increase the recurrence risk. The preoperative MRI findings, especially T1WI findings, have predictive value for postoperative recurrence of CSDH and the T1-iso/hypointensity group can be assumed to be a high recurrence risk group. PMID:25746312

Epidemiological characteristics and deaths of premature infants in a referral hospital for high-risk pregnancies

PubMed Central

de Freitas, Brunnella Alcantara Chagas; Sant'Ana, Luciana Ferreira da Rocha; Longo, Giana Zarbato; Siqueira-Batista, Rodrigo; Priore, Silvia Eloiza; Franceschin, Sylvia do Carmo Castro

2012-01-01

Objective To analyze the process of care provided to premature infants in a neonatal intensive care unit and the factors associated with their mortality. Methods Cross-sectional retrospective study of premature infants in an intensive care unit between 2008 and 2010. The characteristics of the mothers and premature infants were described, and a bivariate analysis was performed on the following characteristics: the study period and the "death" outcome (hospital, neonatal and early) using Pearson's chi-square test, Fisher's exact test or a chi-square test for linear trends. Bivariate and multivariable logistic regression analyses were performed using a stepwise backward logistic regression method between the variables with p<0.20 and the "death" outcome. A p value <0.05 was considered to be significant. Results In total, 293 preterm infants were studied. Increased access to complementary tests (transfontanellar ultrasound and Doppler echocardiogram) and breastfeeding rates were indicators of improving care. Mortality was concentrated in the neonatal period, especially in the early neonatal period, and was associated with extreme prematurity, small size for gestational age and an Apgar score <7 at 5 minutes after birth. The late-onset sepsis was also associated with a greater chance of neonatal death, and antenatal corticosteroids were protective against neonatal and early deaths. Conclusions Although these results are comparable to previous findings regarding mortality among premature infants in Brazil, the study emphasizes the need to implement strategies that promote breastfeeding and reduce neonatal mortality and its early component. PMID:23917938

Fibrinogen: cardiometabolic risk marker in obese or overweight children and adolescents.

PubMed

Azevedo, Waldeneide F; Cantalice, Anajás S C; Gonzaga, Nathalia C; Simões, Mônica O da S; Guimarães, Anna Larissa V; Carvalho, Danielle F de; Medeiros, Carla Campos Muniz

2015-01-01

To determine the prevalence of increased serum fibrinogen levels and its association with cardiometabolic risk factors in overweight or obese children and adolescents. Cross-sectional study with 138 children and adolescents (overweight or obese) followed at a reference outpatient clinic of the public health care network. Fibrinogen concentration was divided into quartiles, and values above or equal to the third quartile were considered high. The association between high fibrinogen values and cardiometabolic risk factors was assessed using Pearson's chi-squared test or Fisher's exact test, as necessary. Logistic regression was used to adjust variables predictive of fibrinogen levels. Analyses were performed using SPSS version 22.0 and SAS software, considering a confidence interval of 95%. Serum fibrinogen levels were elevated in 28.3% of individuals, showing association with the presence of high CRP (p=0.003, PR: 2.41, 95% CI: 1.30-4.46) and the presence of four or more risk factors (p=0.042; PR: 1.78, 95% CI: 1.00-3.17). After a logistic regression, only elevated CRP remained associated with altered fibrinogen levels (p=0.024; PR: 1.32; 95% CI: 1.09-5.25). Increased fibrinogen was prevalent in the study population and was associated with ultrasensitive C-reactive protein and the presence of four or more cardiovascular risk factors; it should be included in the assessment of individuals at risk. Copyright © 2015 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Assessing physician-parent communication during emergency medical procedures in children: an observational study in a low-literacy Latino patient population.

PubMed

Dahl, Aaron; Sinha, Madhumita; Rosenberg, David I; Tran, Melissa; Valdez, André

2015-05-01

Effective physician-patient communication is critical to the clinical decision-making process. We studied parental recall of information provided during an informed consent discussion process before performance of emergency medical procedures in a pediatric emergency department of an inner-city hospital with a large bilingual population. Fifty-five parent/child dyads undergoing emergency medical procedures were surveyed prospectively in English/Spanish postprocedure for recall of informed consent information. Exact logistic regression was used to predict the ability to name a risk, benefit, and alternative to the procedure based on a parent's language, education, and acculturation. Among English-speaking parents, there tended to be higher proportions that could name a risk, benefit, or alternative. Our regression models showed overall that the parents with more than a high school education tended to have nearly 5 times higher odds of being able to name a risk. A gap in communication may exist between physicians and patients (or parents of patients) during the consent-taking process, and this gap may be impacted by socio-demographic factors such as language and education level.

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.

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.

Applying the Principles of Specific Objectivity and of Generalizability to the Measurement of Change.

ERIC Educational Resources Information Center

Fischer, Gerhard H.

1987-01-01

A natural parameterization and formalization of the problem of measuring change in dichotomous data is developed. Mathematically-exact definitions of specific objectivity are presented, and the basic structures of the linear logistic test model and the linear logistic model with relaxed assumptions are clarified. (SLD)

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.

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.

Lay Consultations in Heart Failure Symptom Evaluation.

PubMed

Reeder, Katherine M; Sims, Jessica L; Ercole, Patrick M; Shetty, Shivan S; Wallendorf, Michael

2017-01-01

Lay consultations can facilitate or impede healthcare. However, little is known about how lay consultations for symptom evaluation affect treatment decision-making. The purpose of this study was to explore the role of lay consultations in symptom evaluation prior to hospitalization among patients with heart failure. Semi-structured interviews were conducted with 60 patients hospitalized for acute decompensated heart failure. Chi-square and Fisher's exact tests, along with logistic regression were used to characterize lay consultations in this sample. A large proportion of patients engaged in lay consultations for symptom evaluation and decision-making before hospitalization. Lay consultants provided attributions and advice and helped make the decision to seek medical care. Men consulted more often with their spouse than women, while women more often consulted with adult children. Findings have implications for optimizing heart failure self-management interventions, improving outcomes, and reducing hospital readmissions.

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.

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…

TERT promoter mutated WHO grades II and III gliomas are located preferentially in the frontal lobe and avoid the midline.

PubMed

Sun, Ze-Lin; Chan, Aden Ka-Yin; Chen, Ling-Chao; Tang, Chao; Zhang, Zhen-Yu; Ding, Xiao-Jie; Wang, Yang; Sun, Chong-Ran; Ng, Ho-Keung; Yao, Yu; Zhou, Liang-Fu

2015-01-01

The promoter region of telomerase reverse transcriptase (TERTp) and isocitrate dehydrogenase (IDH) have been regarded as biomarkers with distinct clinical and phenotypic features. Investigated the possible correlations between tumor location and genetic alterations would enhance our understanding of gliomagenesis and heterogeneity of glioma. We examined mutations of TERTp and IDH by direct sequencing and fluorescence in-situ hybridization in a cohort of 225 grades II and III diffuse gliomas. Correlation analysis between molecular markers and tumor locations was performed by Chi-square tests/Fisher's exact test and multivariate logistic regression analysis. We found gliomas in frontal lobe showed higher frequency of TERTp mutation (P=0.0337) and simultaneously mutations of IDH and TERTp (IDH (mut)-TERTp(mut)) (P=0.0281) than frequency of biomarkers mutation of tumors in no-Frontal lobes, while lower frequency of TERTp mutation (P<0.0001) and simultaneously wild type of IDH and TERTp (IDH (wt)-TERTp(wt)) (P<0.0001) in midline than no-midline lobes. Logistic regression analysis indicated that locations of tumors associated with TERTp mutation (OR=0.540, 95% CI 0.324-0.900, P=0.018) and status of combinations of IDH and TERTp (IDH (mut)-TERTp (mut) vs. IDH (wt)-TERTp (wt) OR=0.162, 95% CI 0.075-0.350, P<0.001). In conclusion, grades II and III gliomas harboring TERTp mutation were located preferentially in the frontal lobe and rarely in midline. Association of IDH-TERTp status and tumor location suggests their potential values in molecular classification of grades II and III gliomas.

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

PubMed

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

2015-03-01

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

Substance use disorder comorbidity with schizophrenia in families of Mexican and Central American Ancestry

PubMed Central

Jiménez-Castro, Lorena; Hare, Elizabeth; Medina, Rolando; Raventos, Henriette; Nicolini, Humberto; Mendoza, Ricardo; Ontiveros, Alfonso; Jerez, Alvaro; Muñoz, Rodrigo; Dassori, Albana; Escamilla, Michael

2010-01-01

Objectives The aims of this study were to estimate the frequency and course of substances use disorders in Latino patients with schizophrenia and to ascertain risk factors associated with substance use disorders in this population. Method We studied 518 subjects with schizophrenia recruited for a genetic study from the Southwest United States, Mexico, and Central America (Costa Rica and Guatemala). Subjects were assessed using structured interviews and a best estimate consensus process. Logistic regression, χ2, t- test, Fisher’s exact test, and Yates’ correction, as appropriate, were performed to assess the sociodemographic variables associated with dual diagnosis. We defined substance use disorder as either alcohol or substance abuse or dependence. Results Out of 518 patients with schizophrenia, 121 (23.4%) had substance use disorders. Comorbid substance use disorders were associated with male gender, residence in the United States, immigration of Mexican men to the United State, history of depressive syndrome or episode, and being unemployed. The most frequent substance use disorder was alcohol abuse/dependence, followed by marijuana abuse/dependence, and solvent abuse/dependence. Conclusion This study provides data suggesting that depressive episode or syndrome, unemployment, male gender, and immigration of Mexican men to the United States were factors associated with substance use disorder comorbidity in schizophrenia. Binary logistic regression showed that country of residence was associated with substance use disorder in schizophrenic patients. The percentage of subjects with comorbid substance use disorders was higher in the Latinos living in the United States compared with subjects living in Central America and Mexico. PMID:20303714

A case-control study of the risk factors for obstetric fistula in Tigray, Ethiopia.

PubMed

Lewis Wall, L; Belay, Shewaye; Haregot, Tesfahun; Dukes, Jonathan; Berhan, Eyoel; Abreha, Melaku

2017-12-01

We tested the null hypothesis that there were no differences between patients with obstetric fistula and parous controls without fistula. A unmatched case-control study was carried out comparing 75 women with a history of obstetric fistula with 150 parous controls with no history of fistula. Height and weight were measured for each participant, along with basic socio-demographic and obstetric information. Descriptive statistics were calculated and differences between the groups were analyzed using Student's t test, Mann-Whitney U test where appropriate, and Chi-squared or Fisher's exact test, along with backward stepwise logistic regression analyses to detect predictors of obstetric fistula. Associations with a p value <0.05 were considered significant. Patients with fistulas married earlier and delivered their first pregnancies earlier than controls. They had significantly less education, a higher prevalence of divorce/separation, and lived in more impoverished circumstances than controls. Fistula patients had worse reproductive histories, with greater numbers of stillbirths/abortions and higher rates of assisted vaginal delivery and cesarean section. The final logistic regression model found four significant risk factors for developing an obstetric fistula: age at marriage (OR 1.23), history of assisted vaginal delivery (OR 3.44), lack of adequate antenatal care (OR 4.43), and a labor lasting longer than 1 day (OR 14.84). Our data indicate that obstetric fistula results from the lack of access to effective obstetrical services when labor is prolonged. Rural poverty and lack of adequate transportation infrastructure are probably important co-factors in inhibiting access to needed care.

Association between polycystic ovary syndrome and hot flash presentation during the midlife period.

PubMed

Yin, Ophelia; Zacur, Howard A; Flaws, Jodi A; Christianson, Mindy S

2018-06-01

Polycystic ovary syndrome (PCOS) is the most common endocrinopathy in reproductive-aged women; however, the impact of PCOS on menopausal symptoms remains poorly understood. This study aims to determine the influence of PCOS on hot flash presentation in midlife women. Participants were recruited from the Midlife Women's Health Study involving 780 women aged 45 to 54 years. All women completed detailed questionnaires on hot flash symptoms. Between June 2014 and March 2015, participants were screened for history of PCOS based on the Rotterdam criteria. Fisher's exact tests and Wilcoxon rank-sum tests were used for analysis. Multivariate logistic regression was performed to identify factors associated with hot flashes at midlife. In all, 453 women (69%) consented to the telephone interview and 9.3% (n = 42) met diagnostic criteria for PCOS; 411 were included as controls. Mean age was 48.0 and body mass index was 27.3 for women with PCOS. The majority of participants were white (72%). There was no difference between PCOS and control women for levels of follicle-stimulating hormone, testosterone, progesterone, or estradiol. Multivariate logistic regression demonstrated that PCOS was not associated with increased odds of hot flash incidence. Smoking was the only variable associated with experiencing hot flashes (odds ratio 2.0, 95% confidence interval 1.05-3.98). A history of PCOS was not associated with increased hot flash symptoms during the midlife period. Additional research should continue to investigate the health and quality of life associated with a history of PCOS in the aging population.

Sasang Constitution as a Risk Factor for Diabetes Mellitus: A Cross-Sectional Study

PubMed Central

Lee, Tae-Gyu; Koh, Byunghee

2009-01-01

Sasang Constitutional Medicine, which is a branch of traditional Korean medicine, states that medications for diabetes should be individualized according to the patient's individual constitution. However, the effect of constitution on diabetes has not been evaluated to date. Therefore, this study was conducted to determine if constitution is an independent risk factor for diabetes by comparing the prevalence and odds ratios (ORs) of the disease according to constitution. The medical records of 1443 adults who had been examined and classified based on their constitution at Kyung Hee University Hospital in Seoul, Korea were reviewed. A chi-squared test and Fisher's exact test were used to compare the prevalence of diabetes according to constitution, and multiple logistic regression was used to calculate the ORs for diabetes. The prevalence of diabetes differed significantly according to constitution (χ2 = 36.20, df = 2, P < 0.001). Specifically, the prevalence of the disease was higher in Tae-eumin (11.4%) individuals than in Soyangin (5.0%) or Soeumin (1.7%) individuals. In addition, multiple logistic regression revealed that Tae-eumin individuals had a greater risk for diabetes than Soeumin individuals. When compared to Soeumin individuals, the adjusted ORs were 2.01 (95% CI 0.77–5.26) for Soyangin individuals and 3.96 (95% CI 1.48–10.60) for Tae-eumin individuals. These results show that constitution has a significant and independent association with diabetes, which suggests that constitution is an independent risk factor for diabetes that should be considered when attempting to detect and prevent the disease. PMID:19745018

Undervaccination of Perinatally HIV-Infected and HIV-Exposed Uninfected Children in Latin America and the Caribbean

PubMed Central

Succi, Regina C. M.; Krauss, Margot R.; Harris, D. Robert; Machado, Daisy M.; de Moraes-Pinto, Maria Isabel; Mussi-Pinhata, Marisa M.; Ruz, Noris Pavia; Pierre, Russell B.; Kolevic, Lenka; Joao, Esau; Foradori, Irene; Hazra, Rohan

2013-01-01

Background Perinatally HIV-infected children (PHIV) may be at risk of undervaccination. Vaccination coverage rates among PHIV and HIV-exposed uninfected children (HEU) in Latin America and the Caribbean were compared. Methods All PHIV and HEU children born from 2002–2007 that were enrolled in a multi-site observational study conducted in Latin America and the Caribbean were included in this analysis. Children were classified as up to date (UTD) if they had received the recommended number of doses of each vaccine at the appropriate intervals by 12 and 24 months of age. Fisher’s exact test was used to analyze the data. Covariates potentially associated with a child’s HIV status were considered in multivariable logistic regression modeling. Results Of 1156 eligible children, 768 (66.4%) were HEU and 388 (33.6%) were PHIV. HEU children were significantly (p<0.01) more likely to be UTD by 12 and 24 months of age for all vaccines examined. Statistically significant differences persisted when the analyses were limited to children enrolled prior to 12 months of age. Controlling for birth weight, sex, primary caregiver education and any use of tobacco, alcohol or illegal drugs during pregnancy did not contribute significantly to the logistic regression models. Conclusions PHIV children were significantly less likely than HEU children to be UTD for their childhood vaccinations at 12 and 24 months of age, even when limited to children enrolled before 12 months of age. Strategies to increase vaccination rates in PHIV are needed. PMID:23860480

Predictors of Post-Traumatic Stress Disorder among Victims of Serious Motor Vehicle Accidents.

PubMed

Khodadadi-Hassankiadeh, Naema; Dehghan Nayeri, Nahid; Shahsavari, Hooman; Yousefzadeh-Chabok, Shahrokh; Haghani, Hamid

2017-10-01

Compelling evidence has shown that motor vehicle accidents have an enormous impact on mental health. Post-traumatic Stress Disorder (PTSD) is one of the most common psychological consequences in adult survivors of accidents, so it is important to understand the prevalence and predictors of this issue since delay causes damage to crucial daily functioning. This study aimed at investigating the prevalence and predictors of PTSD after motor vehicle accident. This cross-sectional study was conducted on 528 injured patients six weeks to six months after motor vehicle accident in Imam Reza Clinic of Poursina hospital, Rasht in 2015. Data collection tools were three questionnaires including post-traumatic stress-self report (PSS), Beck Depression Inventory (BDI-II), and the Numeric Rating Scale (NRS) for pain. The data were analyzed in SPSS (Version 19) using Chi-square, Fischer's exact test and multivariate logistic regression. Significance level was considered P≤0.05. The prevalence of PTSD and depression was 30.49% and 19.89% in participants, respectively. Chi-square test indicated a significant relationship among age (P=0.02), sex (P<0.001), education level (P<0.001), work status (P<0.001) and PTSD. Participants who reported pain (P<0.001) and depression (P<0.001) were more likely to have high score of PTSD than the others. Multivariate logistic regression showed this significance in sex, depression, age, educational status and pain, as constant risk factors in developing PTSD after accident. This study suggests that primary care setting should be readily prompted for diagnosis of these disorders in non-treatment seeking individuals in the community.

Factors That Influence Surgical Margin State in Patients Undergoing Cold Knife Conization - A Single Center Experience.

PubMed

Aluloski, Igor; Tanturovski, Mile; Petrusevska, Gordana; Jovanovic, Rubens; Kostadinova-Kunovska, Slavica

2017-12-01

To evaluate the factors that influence the surgical margin state in patients undergoing cold knife conization at the University Clinic of Gynecology and Obstetrics in Skopje, Republic of Macedonia Materials and methods: We have retrospectively analyzed the medical records of all patients that underwent a cold knife conization at our Clinic in 2015. We cross-referenced the surgical margin state with the histopathological diagnosis (LSIL, HSIL or micro-invasive/invasive cancer), menopausal status of the patients, number of pregnancies, surgeon experience, operating time and cone depth. The data was analyzed with the Chi square test, Fisher's exact test for categorical data and Student's T test for continuous data and univariate and multivariate logistical regressions were performed. A total of 246 medical records have neen analyzed, out of which 29 (11.79%) patients had LSIL, 194 (78.86%) had HSIL and 23 (9.34%) patients suffered micro-invasive/invasive cervical cancer. The surgical margins were positive in 78 (31.7%) of the patients. The average age of the patients was 41.13 and 35 (14.23%) of the patients were menopausal. The multivariate logistic regression identified preoperative forceps biopsy of micro-invasive SCC, HSIL or higher cone specimen histology and shorter cone depth as independent predictors of surgical margin involvement in patients undergoing cold knife conization. In the current study, we have found no association between the inherent characteristics of the patient and the surgeon and the surgical margin state after a CKC. The most important predictors for positive margins were the severity of the lesion and the cone depth.

Frequent hospital admissions in Singapore: clinical risk factors and impact of socioeconomic status.

PubMed

Low, Lian Leng; Tay, Wei Yi; Ng, Matthew Joo Ming; Tan, Shu Yun; Liu, Nan; Lee, Kheng Hock

2018-01-01

Frequent admitters to hospitals are high-cost patients who strain finite healthcare resources. However, the exact risk factors for frequent admissions, which can be used to guide risk stratification and design effective interventions locally, remain unknown. Our study aimed to identify the clinical and sociodemographic risk factors associated with frequent hospital admissions in Singapore. An observational study was conducted using retrospective 2014 data from the administrative database at Singapore General Hospital, Singapore. Variables were identified a priori and included patient demographics, comorbidities, prior healthcare utilisation, and clinical and laboratory variables during the index admission. Multivariate logistic regression analysis was used to identify independent risk factors for frequent admissions. A total of 16,306 unique patients were analysed and 1,640 (10.1%) patients were classified as frequent admitters. On multivariate logistic regression, 16 variables were independently associated with frequent hospital admissions, including age, cerebrovascular disease, history of malignancy, haemoglobin, serum creatinine, serum albumin, and number of specialist outpatient clinic visits, emergency department visits, admissions preceding index admission and medications dispensed at discharge. Patients staying in public rental housing had a 30% higher risk of being a frequent admitter after adjusting for demographics and clinical conditions. Our study, the first in our knowledge to examine the clinical risk factors for frequent admissions in Singapore, validated the use of public rental housing as a sensitive indicator of area-level socioeconomic status in Singapore. These risk factors can be used to identify high-risk patients in the hospital so that they can receive interventions that reduce readmission risk. Copyright: © Singapore Medical Association

Analysis of vaccination status of preschool children in Teresina (PI), Brazil.

PubMed

Fernandes, Ana Catharina Nunes; Gomes, Keila Rejane Oliveira; de Araújo, Telma Maria Evangelista; Moreira-Araújo, Regilda Saraiva dos Reis

2015-01-01

Immunization is a priority action of the Ministry of Health for contributing to reducing child mortality; however, studies show increased vaccination delays and non-vaccination. This study aims to analyze the immunization status of preschool children in Teresina - PI. Cross-sectional study involving 542 children, aged 2-6 years, enrolled in local public schools in four Municipal Childhood Education Centers selected at random, following the proportional division by regions of the city. Data were collected through a pre-coded and pre-tested form, in addition to scanning the children's vaccination card. For univariate descriptive statistical analysis, Pearson's χ2 Test and Fisher's Exact Test were used, and for multivariate analysis, multiple logistic regression was conducted using SPSS version 17.0. The study complied with the ethical aspects in accordance with current legislation. The frequency of delayed vaccination/non-vaccination was 24.9%. The average of non-administered vaccines was 1.7 (SD ± 1.2) and of delayed vaccines was 3.3 (SD ± 1.6). The binomial logistic regression model showed a significant association (p < 0.05) between young caregivers (under 24 years) and low frequency in childcare consultations with delayed vaccination/non-vaccination. There was no association with the variables related to the experience of children in the vaccination room and with the implementation of the Family Health Strategy. Ensuring and strengthening primary healthcare actions are essential tools to reduce non-vaccination and vaccine delays. Professionals who care for children in vaccination rooms need to sensitize themselves to guide and encourage parents/caregivers to meet the vaccination schedules without delays or errors.

Computed Tomographic Evaluation of Posttreatment Soft-Tissue Changes by Using a Lymphedema Scoring System in Patients with Oral Cancer.

PubMed

Akashi, Masaya; Teraoka, Shun; Kakei, Yasumasa; Kusumoto, Junya; Hasegawa, Takumi; Minamikawa, Tsutomu; Hashikawa, Kazunobu; Komori, Takahide

2018-04-01

This study aimed to evaluate posttreatment soft-tissue changes in patients with oral cancer with computed tomography (CT). To accomplish that purpose, a scoring system was established, referring to the criteria of lower leg lymphedema (LE). One hundred and six necks in 95 patients who underwent oral oncologic surgery with neck dissection (ND) were analyzed retrospectively using routine follow-up CT images. A two-point scoring system to evaluate soft-tissue changes (so-called "LE score") was established as follows: Necks with a "honeycombing" appearance were assigned 1 point. Necks with "taller than wide" fat lobules were assigned 1 point. Necks with neither appearance were assigned 0 points. Comparisons between patients with LE score ≥1 and LE score = 0 at 6 months postoperatively were performed using the Fisher exact test for discrete variables and the Mann-Whitney U test for continuous variables. Univariate predictors associated with posttreatment changes (i.e., LE score ≥1 at 6 months postoperatively) were entered into a multivariate logistic regression analysis. Values of p < 0.05 were considered to indicate statistical significance. The occurrence of the posttreatment soft-tissue changes was 32%. Multivariate logistic regression analysis showed that postoperative radiation therapy (RT) and bilateral ND were potential risk factors of posttreatment soft-tissue changes on CT images. Sequential evaluation of "honeycombing" and the "taller than wide" appearances on routine follow-up CT revealed the persistence of posttreatment soft-tissue changes in patients who underwent oral cancer treatment, and those potential risk factors were postoperative RT and bilateral ND.

Migration and symptom reporting at menopause: a comparative survey of migrant women from Turkey in Berlin, German women in Berlin, and women in Istanbul.

PubMed

Boral, Şengül; Borde, Theda; Kentenich, Heribert; Wernecke, Klaus D; David, Matthias

2013-02-01

The goal of this study was to compare perceptions of menopausal symptoms among migrant women from Turkey in Berlin (TB), German women in Berlin (GB), and women in Istanbul (TI). The aim was to analyze findings in light of the possible influences of sociodemographic, psychosocial, and migration-related aspects. The study participants (aged 45-60 y) were recruited via random and snowball sampling and surveyed with a structured questionnaire in the German and Turkish languages, which contained questions about their experiences with the menopausal phase and related symptoms (Menopause Rating Scale II), menopausal hormone therapy, and sociodemographic, psychosocial, and migration-related aspects. Statistical analysis was performed with univariate Fisher's exact test, factor analysis, and multivariate logistic regression. A total of 963 women participated in the study. Premenopausal/perimenopausal migrant women from Turkey in Berlin most frequently reported severe vegetative complaints (TB, 49.9%; GB, 34.9%; TI, 34.9%) and genital complaints (TB, 39.2%; GB, 32.3%; TI, 29.4%), as defined by factor analysis. In postmenopausal migrant women from Turkey in Berlin, the most frequently reported symptoms belonged to the domain of psychological complaints (TB, 52.7% vs GB, 24.0%; TI, 55.7%). Gradual multivariate logistic regression revealed sociodemographic and health-related risk factors as predictive factors for the defined menopausal complaints. Migration-related factors might be decisive for women's experience of menopause. Improvement of population-tailored access to factual information about menopause and treatment options is an area of great potential to support women in this phase.

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.

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

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.

Research design and statistical methods in Pakistan Journal of Medical Sciences (PJMS).

PubMed

Akhtar, Sohail; Shah, Syed Wadood Ali; Rafiq, M; Khan, Ajmal

2016-01-01

This article compares the study design and statistical methods used in 2005, 2010 and 2015 of Pakistan Journal of Medical Sciences (PJMS). Only original articles of PJMS were considered for the analysis. The articles were carefully reviewed for statistical methods and designs, and then recorded accordingly. The frequency of each statistical method and research design was estimated and compared with previous years. A total of 429 articles were evaluated (n=74 in 2005, n=179 in 2010, n=176 in 2015) in which 171 (40%) were cross-sectional and 116 (27%) were prospective study designs. A verity of statistical methods were found in the analysis. The most frequent methods include: descriptive statistics (n=315, 73.4%), chi-square/Fisher's exact tests (n=205, 47.8%) and student t-test (n=186, 43.4%). There was a significant increase in the use of statistical methods over time period: t-test, chi-square/Fisher's exact test, logistic regression, epidemiological statistics, and non-parametric tests. This study shows that a diverse variety of statistical methods have been used in the research articles of PJMS and frequency improved from 2005 to 2015. However, descriptive statistics was the most frequent method of statistical analysis in the published articles while cross-sectional study design was common study design.

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

Treesearch

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

2011-01-01

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

Discrete post-processing of total cloud cover ensemble forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

2017-04-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

A 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…

Lay Consultations in Heart Failure Symptom Evaluation

PubMed Central

Reeder, Katherine M.; Sims, Jessica L.; Ercole, Patrick M.; Shetty, Shivan S.; Wallendorf, Michael

2017-01-01

Purpose Lay consultations can facilitate or impede healthcare. However, little is known about how lay consultations for symptom evaluation affect treatment decision-making. The purpose of this study was to explore the role of lay consultations in symptom evaluation prior to hospitalization among patients with heart failure. Methods Semi-structured interviews were conducted with 60 patients hospitalized for acute decompensated heart failure. Chi-square and Fisher’s exact tests, along with logistic regression were used to characterize lay consultations in this sample. Results A large proportion of patients engaged in lay consultations for symptom evaluation and decision-making before hospitalization. Lay consultants provided attributions and advice and helped make the decision to seek medical care. Men consulted more often with their spouse than women, while women more often consulted with adult children. Conclusions Findings have implications for optimizing heart failure self-management interventions, improving outcomes, and reducing hospital readmissions. PMID:29399657

An assessment of primary care attributes from the perspective of female healthcare users1

PubMed Central

Lima, Eliane de Fátima Almeida; Sousa, Ana Inês; Primo, Cândida Caniçali; Leite, Francielie Marabotti Costa; Lima, Rita de Cassia Duarte; Maciel, Ethel Leonor Nóia

2015-01-01

OBJECTIVE: this study sought to assess the quality of the Family Health Strategy (FHS) and investigated the association between primary care attributes (PCAs) and the sociodemographic characteristics of users. METHOD: a total of 215 female FHS users were interviewed for this descriptive and cross-sectional study. The Primary Care Assessment Tool (PCATool), Adult Edition was used, and the results were analyzed using Fisher's exact tests, Pearson's chi-square tests and logistic regressions. RESULTS: the lowest average score corresponded to the dimension "accessibility" (1.80), and the highest score corresponded to "access" (8.76). The results corresponding to the attributes "longitudinality", "coordination", "comprehensiveness", and "orientation" were not significant. No association was found between the participants' sociodemographic characteristics and the essential, derivative, and general attributes (p>0.05). CONCLUSION: several attributes must be improved across all the investigated services from the perspective of female FHS users. PMID:26155006

Motivations and Predictors of Cheating in Pharmacy School

PubMed Central

Nguyen, Kathy; Shah, Bijal M.; Doroudgar, Shadi; Bidwal, Monica K.

2016-01-01

Objective. To assess the prevalence, methods, and motivations for didactic cheating among pharmacy students and to determine predictive factors for cheating in pharmacy colleges and schools. Methods. A 45-item cross-sectional survey was conducted at all four doctor of pharmacy programs in Northern California. For data analysis, t test, Fisher exact test, and logistic regression were used. Results. Overall, 11.8% of students admitted to cheating in pharmacy school. Primary motivations for cheating included fear of failure, procrastination, and stress. In multivariate analysis, the only predictor for cheating in pharmacy school was a history of cheating in undergraduate studies. Conclusion. Cheating occurs in pharmacy schools and is motivated by fear of failure, procrastination, and stress. A history of past cheating predicts pharmacy school cheating. The information presented may help programs better understand their student population and lead to a reassessment of ethical culture, testing procedures, and prevention programs. PMID:27899829

Financial Care for Older Adults With Dementia.

PubMed

Pan, Xi; Lee, Yeonjung; Dye, Cheryl; Roley, Laurie Theriot

2017-06-01

This article describes an examination of the sociodemographic characteristics of adult children, particularly Baby Boomer caregivers, who provide financial care to older parents with dementia. The sample including 1,011adult children dementia caregivers aged 50 to 64 years is selected from a nationally representative sample in the 2010 Health and Retirement Study. Exact logistic regression revealed that race, provision of financial assistance to caregiver children, and the number of their children are significantly associated with financial caregiving of parents. Non-White caregivers are more likely to provide financial care to their parents or parents-in-law with dementia; those who have more children and provide financial assistance to their children are less likely to provide financial care to parents with dementia. The current findings present valuable new information on the sociodemographic characteristics of adult children who provide financial assistance to parents with dementia and inform research, programs, and services on dementia caregiving.

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.

History of falls, gait, balance, and fall risks in older cancer survivors living in the community.

PubMed

Huang, Min H; Shilling, Tracy; Miller, Kara A; Smith, Kristin; LaVictoire, Kayle

2015-01-01

Older cancer survivors may be predisposed to falls because cancer-related sequelae affect virtually all body systems. The use of a history of falls, gait speed, and balance tests to assess fall risks remains to be investigated in this population. This study examined the relationship of previous falls, gait, and balance with falls in community-dwelling older cancer survivors. At the baseline, demographics, health information, and the history of falls in the past year were obtained through interviewing. Participants performed tests including gait speed, Balance Evaluation Systems Test, and short-version of Activities-specific Balance Confidence scale. Falls were tracked by mailing of monthly reports for 6 months. A "faller" was a person with ≥1 fall during follow-up. Univariate analyses, including independent sample t-tests and Fisher's exact tests, compared baseline demographics, gait speed, and balance between fallers and non-fallers. For univariate analyses, Bonferroni correction was applied for multiple comparisons. Baseline variables with P<0.15 were included in a forward logistic regression model to identify factors predictive of falls with age as covariate. Sensitivity and specificity of each predictor of falls in the model were calculated. Significance level for the regression analysis was P<0.05. During follow-up, 59% of participants had one or more falls. Baseline demographics, health information, history of falls, gaits speed, and balance tests did not differ significantly between fallers and non-fallers. Forward logistic regression revealed that a history of falls was a significant predictor of falls in the final model (odds ratio =6.81; 95% confidence interval =1.594-29.074) (P<0.05). Sensitivity and specificity for correctly identifying a faller using the positive history of falls were 74% and 69%, respectively. Current findings suggested that for community-dwelling older cancer survivors with mixed diagnoses, asking about the history of falls may help detect individuals at risk of falling.

History of falls, gait, balance, and fall risks in older cancer survivors living in the community

PubMed Central

Huang, Min H; Shilling, Tracy; Miller, Kara A; Smith, Kristin; LaVictoire, Kayle

2015-01-01

Older cancer survivors may be predisposed to falls because cancer-related sequelae affect virtually all body systems. The use of a history of falls, gait speed, and balance tests to assess fall risks remains to be investigated in this population. This study examined the relationship of previous falls, gait, and balance with falls in community-dwelling older cancer survivors. At the baseline, demographics, health information, and the history of falls in the past year were obtained through interviewing. Participants performed tests including gait speed, Balance Evaluation Systems Test, and short-version of Activities-specific Balance Confidence scale. Falls were tracked by mailing of monthly reports for 6 months. A “faller” was a person with ≥1 fall during follow-up. Univariate analyses, including independent sample t-tests and Fisher’s exact tests, compared baseline demographics, gait speed, and balance between fallers and non-fallers. For univariate analyses, Bonferroni correction was applied for multiple comparisons. Baseline variables with P<0.15 were included in a forward logistic regression model to identify factors predictive of falls with age as covariate. Sensitivity and specificity of each predictor of falls in the model were calculated. Significance level for the regression analysis was P<0.05. During follow-up, 59% of participants had one or more falls. Baseline demographics, health information, history of falls, gaits speed, and balance tests did not differ significantly between fallers and non-fallers. Forward logistic regression revealed that a history of falls was a significant predictor of falls in the final model (odds ratio =6.81; 95% confidence interval =1.594–29.074) (P<0.05). Sensitivity and specificity for correctly identifying a faller using the positive history of falls were 74% and 69%, respectively. Current findings suggested that for community-dwelling older cancer survivors with mixed diagnoses, asking about the history of falls may help detect individuals at risk of falling. PMID:26425079

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.

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 ...

Colorectal cancer stage at diagnosis in migrants versus non-migrants (KoMigra): study protocol of a cross-sectional study in Germany

PubMed Central

2014-01-01

Background In Germany, about 20% of the total population have a migration background. Differences exist between migrants and non-migrants in terms of health care access and utilisation. Colorectal cancer is the second most common malignant tumour in Germany, and incidence, staging and survival chances depend, amongst other things, on ethnicity and lifestyle. The current study investigates whether stage at diagnosis differs between migrants and non-migrants with colorectal cancer in an area of high migration and attempts to identify factors that can explain any differences. Methods/Design Data on tumour and migration status will be collected for 1,200 consecutive patients that have received a new, histologically verified diagnosis of colorectal cancer in a high migration area in Germany in the previous three months. The recruitment process is expected to take 16 months and will include gastroenterological private practices and certified centres for intestinal diseases. Descriptive and analytical analysis will be performed: the distribution of variables for migrants versus non-migrants and participants versus non-participants will be analysed using appropriate χ2-, t-, F- or Wilcoxon tests. Multivariable, logistic regression models will be performed, with the dependent variable being the dichotomized stage of the tumour (UICC stage I versus more advanced than UICC stage I). Odds ratios and associated 95%-confidence intervals will be calculated. Furthermore, ordered logistic regression models will be estimated, with the exact stage of the tumour at diagnosis as the dependent variable. Predictors used in the ordered logistic regression will be patient characteristics that are specific to migrants as well as patient characteristics that are not. Interaction models will be estimated in order to investigate whether the effects of patient characteristics on stage of tumour at the time of the initial diagnosis is different in migrants, compared to non-migrants. Discussion An association of migration status or other socioeconomic variables with stage at diagnosis of colorectal cancer would be an important finding with respect to equal health care access among migrants. It would point to access barriers or different symptom appraisal and, in the long term, could contribute to the development of new health care concepts for migrants. Trial registration German Clinical Trials Register DRKS00005056. PMID:24559172

Clinical and Radiologic Predictive Factors of Rib Fractures in Outpatients With Chest Pain.

PubMed

Zhang, Liang; McMahon, Colm J; Shah, Samir; Wu, Jim S; Eisenberg, Ronald L; Kung, Justin W

To identify the clinical and radiologic predictive factors of rib fractures in stable adult outpatients presenting with chest pain and to determine the utility of dedicated rib radiographs in this population of patients. Following Institutional Review Board approval, we performed a retrospective review of 339 consecutive cases in which a frontal chest radiograph and dedicated rib series had been obtained for chest pain in the outpatient setting. The frontal chest radiograph and dedicated rib series were sequentially reviewed in consensus by two fellowship-trained musculoskeletal radiologists blinded to the initial report. The consensus interpretation of the dedicated rib series was used as the gold standard. Multiple variable logistic regression analysis assessed clinical and radiological factors associated with rib fractures. Fisher exact test was used to assess differences in medical treatment between the 2 groups. Of the 339 patients, 53 (15.6%) had at least 1 rib fracture. Only 20 of the 53 (37.7%) patients' fractures could be identified on the frontal chest radiograph. The frontal chest radiograph had a sensitivity of 38% and specificity of 100% when using the rib series as the reference standard. No pneumothorax, new mediastinal widening or pulmonary contusion was identified. Multiple variable logistic regression analysis of clinical factors associated with the presence of rib fractures revealed a significant association of trauma history (odds ratio 5.7 [p < 0.05]) and age ≥40 (odds radio 3.1 [p < 0.05]). Multiple variable logistic regression analysis of radiographic factors associated with rib fractures in this population demonstrated a significant association of pleural effusion with rib fractures (odds ratio 18.9 [p < 0.05]). Patients with rib fractures received narcotic analgesia in 47.2% of the cases, significantly more than those without rib fractures (21.3%, p < 0.05). None of the patients required hospitalization. In the stable outpatient setting, rib fractures have a higher association with a history of minor trauma and age ≥40 in the adult population. Radiographic findings associated with rib fractures include pleural effusion. The frontal chest radiograph alone has low sensitivity in detecting rib fractures. The dedicated rib series detected a greater number of rib fractures. Although no patients required hospitalization, those with rib fractures were more likely to receive narcotic analgesia. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models

ERIC Educational Resources Information Center

Shieh, Gwowen

2009-01-01

In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…

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.

Operation Desert Shield: Thunderstorms of Logistics: Did We Do Any Better During Post Cold War Interventions?

DTIC Science & Technology

2007-02-20

above hypothesis, we must examine the seams of the operation. They are force structuring, distribution management , logistics intelligence, and customer...Iron Mountains, which is exactly what happened. Distribution Management ALOC distribution management problems included an ineffective theater tracking...deployments later the problems remained the same. Force structure and distribution management issues, the use of manual “non-standard” requisition

Risk factors for unavoidable removal of instrumentation after surgical site infection of spine surgery

PubMed Central

Tominaga, Hiroyuki; Setoguchi, Takao; Kawamura, Hideki; Kawamura, Ichiro; Nagano, Satoshi; Abematsu, Masahiko; Tanabe, Fumito; Ishidou, Yasuhiro; Yamamoto, Takuya; Komiya, Setsuro

2016-01-01

Abstract Surgical site infection (SSI) after spine instrumentation is difficult to treat, and often requires removal of instrumentation. The removal of instrumentation after spine surgery is a severe complication that can lead to the deterioration of activities of daily living and poor prognosis. Although there are many reports on SSI after spine surgery, few reports have investigated the risk factors for the removal of instrumentation after spine surgery SSI. This study aimed to identify the risk factors for unavoidable removal of instrumentation after SSI of spine surgery. We retrospectively reviewed 511 patients who underwent spine surgery with instrumentation at Kagoshima University Hospital from January 2006 to December 2014. Risk factors associated with SSI were analyzed via multiple logistic regression analysis. Parameters of the group that needed instrumentation removal were compared with the group that did not require instrumentation removal using the Mann–Whitney U and Fisher's exact tests. The posterior approach was used in most cases (453 of 511 cases, 88.6%). SSI occurred in 16 of 511 cases (3.14%) of spine surgery with instrumentation. Multivariate logistic regression analysis identified 2 significant risk factors for SSI: operation time, and American Society of Anesthesiologists physical status classification ≥ 3. Twelve of the 16 patients with SSI (75%) were able to keep the instrumentation after SSI. Pseudarthrosis occurred in 2 of 4 cases (50%) after instrumentation removal. Risk factors identified for instrumentation removal after spine SSI were a greater number of past surgeries, low preoperative hemoglobin, high preoperative creatinine, high postoperative infection treatment score for the spine, and the presence of methicillin-resistant Staphylococcus aureus. In these high risk cases, attempts should be made to decrease the risk factors preoperatively, and careful postoperative monitoring should be conducted. PMID:27787365

Risk factors for unavoidable removal of instrumentation after surgical site infection of spine surgery: A retrospective case-control study.

PubMed

Tominaga, Hiroyuki; Setoguchi, Takao; Kawamura, Hideki; Kawamura, Ichiro; Nagano, Satoshi; Abematsu, Masahiko; Tanabe, Fumito; Ishidou, Yasuhiro; Yamamoto, Takuya; Komiya, Setsuro

2016-10-01

Surgical site infection (SSI) after spine instrumentation is difficult to treat, and often requires removal of instrumentation. The removal of instrumentation after spine surgery is a severe complication that can lead to the deterioration of activities of daily living and poor prognosis. Although there are many reports on SSI after spine surgery, few reports have investigated the risk factors for the removal of instrumentation after spine surgery SSI. This study aimed to identify the risk factors for unavoidable removal of instrumentation after SSI of spine surgery. We retrospectively reviewed 511 patients who underwent spine surgery with instrumentation at Kagoshima University Hospital from January 2006 to December 2014. Risk factors associated with SSI were analyzed via multiple logistic regression analysis. Parameters of the group that needed instrumentation removal were compared with the group that did not require instrumentation removal using the Mann-Whitney U and Fisher's exact tests. The posterior approach was used in most cases (453 of 511 cases, 88.6%). SSI occurred in 16 of 511 cases (3.14%) of spine surgery with instrumentation. Multivariate logistic regression analysis identified 2 significant risk factors for SSI: operation time, and American Society of Anesthesiologists physical status classification ≥ 3. Twelve of the 16 patients with SSI (75%) were able to keep the instrumentation after SSI. Pseudarthrosis occurred in 2 of 4 cases (50%) after instrumentation removal. Risk factors identified for instrumentation removal after spine SSI were a greater number of past surgeries, low preoperative hemoglobin, high preoperative creatinine, high postoperative infection treatment score for the spine, and the presence of methicillin-resistant Staphylococcus aureus. In these high risk cases, attempts should be made to decrease the risk factors preoperatively, and careful postoperative monitoring should be conducted.

A breaking down of the Mediterranean diet in the land where it was discovered. A cross sectional survey among the young generation of adolescents in the heart of Cilento, Southern Italy.

PubMed

Saulle, R; Del Prete, G; Stelmach-Mardas, M; De Giusti, M; La Torre, G

2016-01-01

To investigate dietary habits among young people in the Mediterranean lands, exactly where the health benefits of the Mediterranean diet (MD) were discovered by Ancel Keys. A cross-sectional study design. A 10-items food-frequency questionnaire was administered to 1117 students in the schools of the Cilento area. Adherence to the MD was appraised according to a scale of 0-10. A logistic regression model was used to identify possible factors associated with "Following an unhealthy diet". Results were expressed as Odds Ratio with 95% confidence interval and the level of significance was set at p<0.05. A percentage of 63.8 reached a score under six, indicating that the majority of the students did not respect the rules of the Mediterranean diet and only 36.2% (n. 371) exceeded a score of 6 adhering to it in varying degrees. At the logistic regression analysis smokers resulted to be affected by almost a double risk of getting away from the Mediterranean dietary pattern (OR = 1.93; CI 95% 1.44-2.57); on the contrary, those with a higher PCS12 (Physical Component Summary score) were in a lower risk to move away from the MD style (OR = 0.98; 95% CI = 0.96-0.99). Despite its increasing popularity worldwide, adherence to the MD model is decreasing. The new generation of young people does not adhere to the MD pattern although they live in the lands characterized by the tradition and culture of healthy diet and where the benefits from this pattern were initially discovered. Interventions and specific education about the healthy diet may be useful to recover student's dietary patterns as in the old eating tradition.

Patient satisfaction with wait times at an emergency ophthalmology on-call service.

PubMed

Chan, Brian J; Barbosa, Joshua; Moinul, Prima; Sivachandran, Nirojini; Donaldson, Laura; Zhao, Lily; Mullen, Sarah J; McLaughlin, Christopher R; Chaudhary, Varun

2018-04-01

To assess patient satisfaction with emergency ophthalmology care and determine the effect provision of anticipated appointment wait time has on scores. Single-centre, randomized control trial. Fifty patients triaged at the Hamilton Regional Eye Institute (HREI) from November 2015 to July 2016. Fifty patients triaged for next-day appointments at the HREI were randomly assigned to receive standard-of-care preappointment information or standard-of-care information in addition to an estimated appointment wait time. Patient satisfaction with care was assessed postvisit using the modified Judgements of Hospital Quality Questionnaire (JHQQ). In determining how informing patients of typical wait times influenced satisfaction, the Mann-Whitney U test was performed. As secondary study outcomes, we sought to determine patient satisfaction with the intervention material using the Fisher exact test and the effect that wait time, age, sex, education, mobility, and number of health care providers seen had on satisfaction scores using logistic regression analysis. The median JHQQ response was "very good" (4/5) and between "very good" and "excellent" (4.5/5) in the intervention and control arms, respectively. There was no difference in patient satisfaction between the cohorts (Mann-Whitney U = 297.00, p = 0.964). Logistic regression analysis demonstrated that wait times influenced patient satisfaction (OR = 0.919, 95% CI 0.864-0.978, p = 0.008). Of the intervention arm patients, 92.0% (N = 23) found the preappointment information useful, whereas only 12.5% (N = 3) of the control cohort patients noted the same (p < 0.001). Provision of anticipated wait time information to patients in an emergency on-call ophthalmology clinic did not influence satisfaction with care as captured by the JHQQ. Copyright © 2018 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.

Missed opportunities for concurrent HIV-STD testing in an academic emergency department.

PubMed

Klein, Pamela W; Martin, Ian B K; Quinlivan, Evelyn B; Gay, Cynthia L; Leone, Peter A

2014-01-01

We evaluated emergency department (ED) provider adherence to guidelines for concurrent HIV-sexually transmitted disease (STD) testing within an expanded HIV testing program and assessed demographic and clinical factors associated with concurrent HIV-STD testing. We examined concurrent HIV-STD testing in a suburban academic ED with a targeted, expanded HIV testing program. Patients aged 18-64 years who were tested for syphilis, gonorrhea, or chlamydia in 2009 were evaluated for concurrent HIV testing. We analyzed demographic and clinical factors associated with concurrent HIV-STD testing using multivariate logistic regression with a robust variance estimator or, where applicable, exact logistic regression. Only 28.3% of patients tested for syphilis, 3.8% tested for gonorrhea, and 3.8% tested for chlamydia were concurrently tested for HIV during an ED visit. Concurrent HIV-syphilis testing was more likely among younger patients aged 25-34 years (adjusted odds ratio [AOR] = 0.36, 95% confidence interval [CI] 0.78, 2.10) and patients with STD-related chief complaints at triage (AOR=11.47, 95% CI 5.49, 25.06). Concurrent HIV-gonorrhea/chlamydia testing was more likely among men (gonorrhea: AOR=3.98, 95% CI 2.25, 7.02; chlamydia: AOR=3.25, 95% CI 1.80, 5.86) and less likely among patients with STD-related chief complaints at triage (gonorrhea: AOR=0.31, 95% CI 0.13, 0.82; chlamydia: AOR=0.21, 95% CI 0.09, 0.50). Concurrent HIV-STD testing in an academic ED remains low. Systematic interventions that remove the decision-making burden of ordering an HIV test from providers may increase HIV testing in this high-risk population of suspected STD patients.

Gender-specific relationships between socioeconomic disadvantage and obesity in elementary school students.

PubMed

Zahnd, Whitney E; Rogers, Valerie; Smith, Tracey; Ryherd, Susan J; Botchway, Albert; Steward, David E

2015-12-01

To assess the gender-specific effect of socioeconomic disadvantage on obesity in elementary school students. We evaluated body mass index (BMI) data from 2,648 first- and fourth-grade students (1,377 male and 1,271 female students) in eight elementary schools in Springfield, Illinois, between 2012 and 2014. Other factors considered in analysis were grade level, year of data collection, school, race/ethnicity, gender, and socioeconomic disadvantage (SD). Students were considered SD if they were eligible for free/reduced price lunch, a school-based poverty measure. We performed Fisher's exact test or chi-square analysis to assess differences in gender and obesity prevalence by the other factors and gender-stratified logistic regression analysis to determine if SD contributed to increased odds of obesity. A higher proportion of SD female students (20.8%) were obese compared to their non-SD peers (15.2%) (p=0.01). Unadjusted and adjusted logistic regression analysis indicated no difference in obesity in SD and non-SD male students. However, in both unadjusted and adjusted analyses, SD female students had higher odds of obesity than their peers. Even after controlling for grade level, school, year of data collection, and race/ethnicity, SD female students had 49% higher odds of obesity than their non-SD classmates (odds ratio:1.49; 95% confidence interval: 1.09-2.04). Obesity was elevated in SD female students, even after controlling for factors such as race/ethnicity, but such an association was not seen in male students. Further study is warranted to determine the cause of this disparity, and interventions should be developed to target SD female students. Copyright © 2015 Elsevier Inc. All rights reserved.

Does hypervascularity of liver metastases as detected on MRI predict disease progression in breast cancer patients?

PubMed

Braga, Larissa; Semelka, Richard C; Pietrobon, Ricardo; Martin, Diego; de Barros, Nestor; Guller, Ulrich

2004-05-01

The aim of our study was to evaluate the association of the vascularity of liver metastases, as characterized by MRI, and disease progression in breast cancer patients. Sixteen breast cancer patients with liver metastases who underwent MRI before and after systemic therapy were retrospectively identified. On the basis of comparison of each MRI examination with the previous examination, disease status of the patients was classified as complete response, partial response, stable disease, or progressive disease. Liver metastases were characterized as hyper- or hypovascular on the basis of the degree of enhancement in the arterial, portal, and interstitial phases of imaging after administration of a contrast agent. Fisher's exact test and ordinal logistic regression models, including the type of systemic therapy, presence of multiple metastases, and hormone receptor status, were used to estimate the unadjusted and risk-adjusted association between the presence of hypervascular liver metastases and disease progression. All patients in our sample (n = 16) were women and most (12/16, 75%) were white. Their median age was 51.5 years. In unadjusted analyses, the association between the presence of hypervascular liver metastases and disease progression was statistically significant (p < 0.0001). In multiple logistic regression analyses, hypervascular liver metastases were found to be an independent predictor of disease progression. Patients with hypervascular liver lesions were 20.5 times more likely to experience disease progression than patients without hypervascular metastases (odds ratio, 20.5; 95% confidence interval, 5.1-83.5; p < 0.0001). Our analysis provides suggestive evidence that disease progression can be predicted through MRI assessment of the vascularity of liver metastases in patients with breast cancer.

Qualitative and Semiquantitative Elastography for the Diagnosis of Intermediate Suspicious Thyroid Nodules Based on the 2015 American Thyroid Association Guidelines.

PubMed

Yang, Bo Ra; Kim, Eun-Kyung; Moon, Hee Jung; Yoon, Jung Hyun; Park, Vivian Y; Kwak, Jin Young

2018-04-01

To evaluate qualitative and semiquantitative elastography for the diagnosis of intermediate suspicious thyroid nodules based on the 2015 American Thyroid Association (ATA) guidelines. Through a retrospective search of our institutional database, 746 solid thyroid nodules found on grayscale ultrasonography, strain elastography, and ultrasound-guided fine-needle aspiration between June and November 2009 were collected. Among them, 80 nodules from 80 patients with an intermediate suspicion of malignancy based on the 2015 ATA guidelines that were 10 mm or larger were recruited as the final study nodules. Elastographic findings were categorized according to the criteria of Rago et al (J Clin Endocrinol Metab 2007; 92:2917-2922) and Asteria et al (Thyroid 2008; 18:523-531), and strain ratio values were calculated and recorded. The independent 2-sample t test and χ 2 test (or Fisher exact test) were used to evaluate differences in clinical parameters between benign and malignant thyroid nodules. All variables were compared by univariate and multivariate logistic regression analyses, and odds ratios with 95% confidence intervals were calculated. Of the 80 nodules, 6 (7.5%) were malignant, and 74 (92.5%) were benign. No significant differences were observed in age, sex, nodule size, elasticity score, and strain ratio between benign and malignant nodules. No variables significantly predicted thyroid malignancy on the univariate analysis. On the multivariate logistic regression analysis, there were no independent variables associated with thyroid malignancy, including the elasticity score and strain ratio (all P > .05). Elastographic analysis using the elasticity score and strain ratio has limited ability to characterize the benignity or malignancy of thyroid nodules with an intermediate suspicion of malignancy based on the 2015 ATA guidelines. © 2017 by the American Institute of Ultrasound in Medicine.

Relationship Between Visceral Infarction and Ischemic Stroke Subtype.

PubMed

Finn, Caitlin; Hung, Peter; Patel, Praneil; Gupta, Ajay; Kamel, Hooman

2018-03-01

Most cryptogenic strokes are thought to have an embolic source. We sought to determine whether cryptogenic strokes are associated with visceral infarcts, which are usually embolic. Among patients prospectively enrolled in CAESAR (Cornell Acute Stroke Academic Registry), we selected those with a contrast-enhanced abdominal computed tomographic scan within 1 year of admission. Our exposure variable was adjudicated stroke subtype per the Trial of ORG 10172 in Acute Stroke Treatment classification. Our outcome was renal or splenic infarction as assessed by a single radiologist blinded to stroke subtype. We used Fisher exact test and multiple logistic regression to compare the prevalence of visceral infarcts among cardioembolic strokes, strokes of undetermined etiology, and noncardioembolic strokes (large- or small-vessel strokes). Among 227 patients with ischemic stroke and a contrast-enhanced abdominal computed tomographic scan, 59 had a visceral infarct (35 renal and 27 splenic). The prevalence of visceral infarction was significantly different among cardioembolic strokes (34.2%; 95% confidence interval [CI], 23.7%-44.6%), strokes of undetermined etiology (23.9%; 95% CI, 15.0%-32.8%), and strokes from large-artery atherosclerosis or small-vessel occlusion (12.5%; 95% CI, 1.8%-23.2%; P =0.03). In multiple logistic regression models adjusted for demographics and vascular comorbidities, we found significant associations with visceral infarction for both cardioembolic stroke (odds ratio, 3.5; 95% CI, 1.2-9.9) and stroke of undetermined source (odds ratio, 3.3; 95% CI, 1.1-10.5) as compared with noncardioembolic stroke. The prevalence of visceral infarction differed significantly across ischemic stroke subtypes. Cardioembolic and cryptogenic strokes were associated with a higher prevalence of visceral infarcts than noncardioembolic strokes. © 2018 American Heart Association, Inc.

Menstrual cycle phase and single tablet antiretroviral medication adherence in women with HIV.

PubMed

Hessol, Nancy A; Holman, Susan; Minkoff, Howard; Cohen, Mardge H; Golub, Elizabeth T; Kassaye, Seble; Karim, Roksana; Sosanya, Oluwakemi; Shaheen, Christopher; Merhi, Zaher

2016-01-01

Suboptimal adherence to antiretroviral (ARV) therapy among HIV-infected individuals is associated with increased risk of progression to AIDS and the development of HIV resistance to ARV medications. To examine whether the luteal phase of the menstrual cycle is independently associated with suboptimal adherence to single tablet regimen (STR) ARV medication, data were analyzed from a multicenter cohort study of HIV-infected women who reported regular menstrual cycles and were taking an STR. In a cross-sectional analysis, suboptimal adherence to an STR among women in their follicular phase was compared with suboptimal adherence among women in their luteal phase. In two-way crossover analyses, whereby the same woman was assessed for STR medication adherence in both her follicular and luteal phases, the estimated exact conditional odds of non-adherence to an STR was measured. In adjusted logistic regression analysis of the cross-sectional data (N=327), women with ≤12 years of education were more than three times more likely to have suboptimal adherence (OR=3.6, p=.04) compared to those with >12 years of education. Additionally, women with Center for Epidemiological Studies Depression Scale (CES-D) scores ≥23 were 2.5-times more likely to have suboptimal adherence (OR=2.6, p=.02) compared to those with CES-D scores <23. In conditional logistic regression analyses of the crossover data (N=184), having childcare responsibilities was associated with greater odds of ≤95% adherence. Menstrual cycle phase was not associated with STR adherence in either the cross-sectional or crossover analyses. The lack of association between phase of the menstrual cycle and adherence to an STR in HIV-infected women means attention can be given to other more important risk factors for suboptimal adherence, such as depression, level of education, and childcare responsibilities.

Multidetector CT features of pulmonary focal ground-glass opacity: differences between benign and malignant

PubMed Central

Fan, L; Liu, S-Y; Li, Q-C; Yu, H; Xiao, X-S

2012-01-01

Objective To evaluate different features between benign and malignant pulmonary focal ground-glass opacity (fGGO) on multidetector CT (MDCT). Methods 82 pathologically or clinically confirmed fGGOs were retrospectively analysed with regard to demographic data, lesion size and location, attenuation value and MDCT features including shape, margin, interface, internal characteristics and adjacent structure. Differences between benign and malignant fGGOs were analysed using a χ2 test, Fisher's exact test or Mann–Whitney U-test. Morphological characteristics were analysed by binary logistic regression analysis to estimate the likelihood of malignancy. Results There were 21 benign and 61 malignant lesions. No statistical differences were found between benign and malignant fGGOs in terms of demographic data, size, location and attenuation value. The frequency of lobulation (p=0.000), spiculation (p=0.008), spine-like process (p=0.004), well-defined but coarse interface (p=0.000), bronchus cut-off (p=0.003), other air-containing space (p=0.000), pleural indentation (p=0.000) and vascular convergence (p=0.006) was significantly higher in malignant fGGOs than that in benign fGGOs. Binary logistic regression analysis showed that lobulation, interface and pleural indentation were important indicators for malignant diagnosis of fGGO, with the corresponding odds ratios of 8.122, 3.139 and 9.076, respectively. In addition, a well-defined but coarse interface was the most important indicator of malignancy among all interface types. With all three important indicators considered, the diagnostic sensitivity, specificity and accuracy were 93.4%, 66.7% and 86.6%, respectively. Conclusion An fGGO with lobulation, a well-defined but coarse interface and pleural indentation gives a greater than average likelihood of being malignant. PMID:22128130

Angioarchitectural features of brain arteriovenous malformations associated with seizures: a single center retrospective series.

PubMed

Sturiale, C L; Rigante, L; Puca, A; Di Lella, G; Albanese, A; Marchese, E; Di Rocco, C; Maira, G; Colicchio, G

2013-05-01

Epileptic seizures account for 24-40% of all clinical onsets in patients with brain arteriovenous malformations (AVMs). We retrospectively reviewed the angioarchitectural features of AVMs associated with seizures in 168 patients admitted to our Department from 1997 to 2012. Patients were dichotomized according to demographic characteristics, type of treatment, bleeding occurrence, and morphological and topographic features. Clinical status at admission and discharge was also recorded. The association of each one of these variables with seizures occurrence was statistically tested. Continuous variables and outcome were compared with Student's t-test, whereas categorical ones were compared using Fisher's exact test. The independent contribution of some seizures predictors was assessed with a logistic regression model. Associations were considered significant for P < 0.05. About 29% patients showed seizures and 47% bleeding. No significant difference in age and sex was observed between patients with and without seizures. AVMs > 4 cm (P = 0.001) and those fed by dilated arterial feeders (P = 0.02) were associated with increased risk of seizures. A higher risk of seizures occurrence was also observed in cortical AVMs compared with deeper ones (75.5% vs. 55.4%; P = 0.01), and in AVMs fed by middle and posterior cerebral arteries branches compared with the other vessels (81.6% vs. 45.3%; P < 0.001 and 48.9% vs. 23.5%; P = 0.002, respectively). No lobar predisposition was observed. A nidus > 4 cm also appeared as an independent risk factor of seizures occurrence (OR 2.82; 95% CI, 1.26-6.31; P = 0.009) at logistic regression analysis. AVM morphology, especially nidus dimension, appeared to more significantly influence seizures occurrence than their topography. © 2013 The Author(s) European Journal of Neurology © 2013 EFNS.

Trends and outcomes of malignant hyperthermia in the United States, 2000 to 2005.

PubMed

Rosero, Eric B; Adesanya, Adebola O; Timaran, Carlos H; Joshi, Girish P

2009-01-01

Malignant hyperthermia (MH) is a potentially fatal pharmacogenetic disorder with an estimated mortality of less than 5%. The purpose of this study was to evaluate the current incidence of MH and the predictors associated with in-hospital mortality in the United States. The Nationwide Inpatient Sample, which is the largest all-payer inpatient database in the United States, was used to identify patients discharged with a diagnosis of MH during the years 2000-2005. The weighted exact Cochrane-Armitage test and multivariate logistic regression analyses were used to assess trends in the incidence and risk-adjusted mortality from MH, taking into account the complex survey design. From 2000 to 2005, the number of cases of MH increased from 372 to 521 per year. The occurrence of MH increased from 10.2 to 13.3 patients per million hospital discharges (P = 0.001). Mortality rates from MH ranged from 6.5% in 2005 to 16.9% in 2001 (P < 0.0001). The median age of patients with MH was 39 (interquartile range, 23-54 yr). Only 17.8% of the patients were children, who had lower mortality than adults (0.7% vs. 14.1%, P < 0.0001). Logistic regression analyses revealed that risk-adjusted in-hospital mortality was associated with increasing age, female sex, comorbidity burden, source of admission to hospital, and geographic region of the United States. The incidence of MH in the United States has increased in recent years. The in-hospital mortality from MH remains elevated and higher than previously reported. The results of this study should enable the identification of areas requiring increased focus in MH-related education.

Does Stone Removal Help Patients with Recurrent Urinary Tract Infections?

PubMed

Omar, Mohamed; Abdulwahab-Ahmed, Abdullahi; Chaparala, Hemant; Monga, Manoj

2015-10-01

We evaluated the impact of surgical extraction of nonobstructing asymptomatic stones on recurrent urinary tract infections and identified predictors of patients who may be rendered infection-free. We retrospectively reviewed charts to identify patients with recurrent urinary tract infections who underwent surgical stone extraction and were rendered stone-free. Demographic variables as well as procedure, infectious etiology, stone composition and the systemic inflammatory response syndrome rate were also recorded. Patients were divided into 2 groups. Group 1 had no evidence of recurrent infection following surgery while recurrent infection developed in group 2. Univariate analysis was performed using the Wilcoxon signed rank and Fisher exact tests. Logistic regression was used for multivariate analysis. We identified 120 patients with recurrent urinary tract infections and a nonobstructive renal stone. Surgical management included shock wave lithotripsy in 32% of cases, ureteroscopy in 7% and percutaneous nephrolithotomy in 61%. Of the 120 patients 58 (48%) remained infection-free after surgery while 62 (52%) experienced recurrent infection. Factors associated with a higher risk of recurrent infections included type 2 diabetes mellitus (OR 1.73, p = 0.01), hypertension (OR 2.8, p = 0.007) and black ethnicity (OR 13.7, p = 0.009). Escherichia coli infections were more likely to resolve (OR 0.34, p = 0.01). In contrast, Enterococcus infections were more likely to persist (OR 2.5, p = 0.04). On multiple logistic regression analysis only race, hypertension and E. coli infections were significant predictors of infection clearance. Of patients with recurrent urinary tract infections and asymptomatic renal calculi 50% may be rendered infection-free following stone extraction. Patients with risk factors for recurrent infections after surgery should be counseled that stone extraction might not eradicate the infection. Copyright © 2015 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Is maternal education a social vaccine for childhood malaria infection? A cross-sectional study from war-torn Democratic Republic of Congo.

PubMed

Ma, Cary; Claude, Kasereka Masumbuko; Kibendelwa, Zacharie Tsongo; Brooks, Hannah; Zheng, Xiaonan; Hawkes, Michael

2017-03-01

In zones of violent conflict in the tropics, social disruption leads to elevated child mortality, of which malaria is the leading cause. Understanding the social determinants of malaria transmission may be helpful to optimize malaria control efforts. We conducted a cross-sectional study of healthy children aged 2 months to 5 years attending well-child and/or immunization visits in the Democratic Republic of Congo (DRC). Six hundred and forty-seven children were tested for malaria antigenemia by rapid diagnostic test and the accompanying parent or legal guardian simultaneously completed a survey questionnaire related to demographics, socioeconomic status, maternal education, as well as bednet use and recent febrile illness. We examined the associations between variables using multivariable logistic regression analysis, chi-squared statistic, Fisher's exact test, and Spearman's rank correlation, as appropriate. One hundred and twenty-three out of the 647 (19%) children in the study tested positive for malaria. Higher levels of maternal education were associated with a lower risk of malaria in their children. The prevalence of malaria in children of mothers with no education, primary school, and beyond primary was 41/138 (30%), 41/241 (17%), and 39/262 (15%), respectively (p = 0.001). In a multivariable logistic regression model adjusting for the effect of a child's age and study site, the following remained significant predictors of malaria antigenemia: maternal education, number of children under five per household, and HIV serostatus. Higher maternal education, through several putative causal pathways, was associated with lower malaria prevalence among children in the DRC. Our findings suggest that maternal education might be an effective 'social vaccine' against malaria in the DRC and globally.

Defining patients' knowledge and perceptions of vaginal mesh surgery.

PubMed

Brown, Lindsay K; Fenner, Dee E; Berger, Mitchell B; Delancey, John O L; Morgan, Daniel M; Patel, Divya A; Schimpf, Megan O

2013-01-01

Given recent government investigations and media coverage of the controversy regarding mesh surgery, we sought to define patients' knowledge and perceptions of vaginal mesh surgery. An anonymous survey was distributed to a convenience sample of new patients at urogynecology and female urology clinics at a single medical center during April to June 2012. The survey assessed patients' demographics, information sources, and beliefs and concerns regarding mesh surgery. The Fisher's exact test was used to identify predictors of patients' beliefs regarding mesh. Logistic and linear regressions were used to identify predictors of aversion to surgery and higher concern regarding future surgery. One hundred sixty-four women completed the survey; 62.2% (102/164) indicated knowledge of mesh surgery for prolapse and/or incontinence and were included in subsequent analyses. The mean ± SD age was 58.0 ± 12.5 years, and 24.5% reported prior mesh surgery. The most common information source was television commercials (57.8%); only 23.5% of the women reported receiving information from a medical professional. Participants indicated the following regarding vaginal mesh: class-action lawsuit in progress (55/102 [54.0%]), causes pain (47/102 [47.1%]), possibility of rejection (35/102 [34.3%]), can cause bleeding and become exposed vaginally (30/102 [29.4%]), and should be removed owing to recall (28/102 [27.5%]). Of these women, 22.1% (19/86) indicated they would not consider mesh surgery. On multivariable logistic regression, level of concern, information from friends/family, and knowledge of class-action lawsuit predicted aversion to mesh surgery. Nearly two thirds of new patients had knowledge of vaginal mesh surgery. We identified considerable misinformation and aversion to future mesh surgery among these women.

A characterization of factors determining postoperative ileus after laparoscopic colectomy enables the generation of a novel predictive score.

PubMed

Kronberg, Udo; Kiran, Ravi P; Soliman, Mohamed S M; Hammel, Jeff P; Galway, Ursula; Coffey, John Calvin; Fazio, Victor W

2011-01-01

Postoperative ileus (POI) after colorectal surgery is associated with prolonged hospital stay and increased costs. The aim of this study is to investigate pre-, intra-, and postoperative risk factors associated with the development of POI in patients undergoing laparoscopic partial colectomy. Patients operated between 2004 and 2008 were retrospectively identified from a prospectively maintained database, and clinical, metabolic, and pharmacologic data were obtained. Postoperative ileus was defined as the absence of bowel function for 5 or more days or the need for reinsertion of a nasogastric tube after starting oral diet in the absence of mechanical obstruction. Associations between likelihood of POI and study variables were assessed univariably by using χ tests, Fisher exact tests, and logistic regression models. A scoring system for prediction of POI was constructed by using a multivariable logistic regression model based on forward stepwise selection of preoperative factors. A total of 413 patients (mean age, 58 years; 53.5% women) were included, and 42 (10.2%) of them developed POI. Preoperative albumin, postoperative deep-vein thrombosis, and electrolyte levels were associated with POI. Age, previous abdominal surgery, and chronic preoperative use of narcotics were independently correlated with POI on multivariate analysis, which allowed the creation of a predictive score. Patients with a score of 2 or higher had an 18.3% risk of POI (P < 0.001). Postoperative ileus after laparoscopic partial colectomy is associated with specific preoperative and postoperative factors. The likelihood of POI can be predicted by using a preoperative scoring system. Addressing the postoperative factors may be expected to reduce the incidence of this common complication in high-risk patients.

Predictors of Post-Traumatic Stress Disorder among Victims of Serious Motor Vehicle Accidents

PubMed Central

Khodadadi-Hassankiadeh, Naema; Dehghan Nayeri, Nahid; Shahsavari, Hooman; Yousefzadeh-Chabok, Shahrokh; Haghani, Hamid

2017-01-01

ABSTRACT Background: Compelling evidence has shown that motor vehicle accidents have an enormous impact on mental health. Post-traumatic Stress Disorder (PTSD) is one of the most common psychological consequences in adult survivors of accidents, so it is important to understand the prevalence and predictors of this issue since delay causes damage to crucial daily functioning. This study aimed at investigating the prevalence and predictors of PTSD after motor vehicle accident. Methods: This cross-sectional study was conducted on 528 injured patients six weeks to six months after motor vehicle accident in Imam Reza Clinic of Poursina hospital, Rasht in 2015. Data collection tools were three questionnaires including post-traumatic stress-self report (PSS), Beck Depression Inventory (BDI-II), and the Numeric Rating Scale (NRS) for pain. The data were analyzed in SPSS (Version 19) using Chi-square, Fischer’s exact test and multivariate logistic regression. Significance level was considered P≤0.05. Results: The prevalence of PTSD and depression was 30.49% and 19.89% in participants, respectively. Chi-square test indicated a significant relationship among age (P=0.02), sex (P<0.001), education level (P<0.001), work status (P<0.001) and PTSD. Participants who reported pain (P<0.001) and depression (P<0.001) were more likely to have high score of PTSD than the others. Multivariate logistic regression showed this significance in sex, depression, age, educational status and pain, as constant risk factors in developing PTSD after accident. Conclusion: This study suggests that primary care setting should be readily prompted for diagnosis of these disorders in non-treatment seeking individuals in the community. PMID:29043281

Can We Identify Parents Who Do Not Verbally Share Concerns for Their Children's Development?

PubMed

Eremita, Matthew; Semancik, Eileen; Lerer, Trudy; Dworkin, Paul H

2017-04-01

We aimed to identify characteristics of parents who do not voice developmental concerns when prompted by their children's nurse and/or primary care provider (PCP), despite reporting concerns on parent-completed questionnaires. We reviewed 376 medical records of children seen for a 9-month well-child visit in an urban pediatric clinic between September 2011 and December 2012 for sociodemographic variables hypothesized to affect parents' sharing of developmental concerns: the child's birth order and gender; parents' education level, employment, relationship status, and primary language; and family size and racial/ethnic background. The target population was parents who reported concerns on the Parents' Evaluation of Developmental Status (PEDS), a routinely administered, parent-completed screening questionnaire. We subdivided parents who reported concerns on the PEDS (N = 86) based on whether they voiced developmental concerns when prompted by their children's nurse and/or PCP. Two-sided Fisher's exact tests and logistic regression evaluated the relationship between sociodemographic variables and parents' voicing of developmental concerns. Only parent education approached significance, as parents with less than a high school education (

Is maternal education a social vaccine for childhood malaria infection? A cross-sectional study from war-torn Democratic Republic of Congo

PubMed Central

Ma, Cary; Claude, Kasereka Masumbuko; Kibendelwa, Zacharie Tsongo; Brooks, Hannah; Zheng, Xiaonan; Hawkes, Michael

2017-01-01

In zones of violent conflict in the tropics, social disruption leads to elevated child mortality, of which malaria is the leading cause. Understanding the social determinants of malaria transmission may be helpful to optimize malaria control efforts. We conducted a cross-sectional study of healthy children aged 2 months to 5 years attending well-child and/or immunization visits in the Democratic Republic of Congo (DRC). Six hundred and forty-seven children were tested for malaria antigenemia by rapid diagnostic test and the accompanying parent or legal guardian simultaneously completed a survey questionnaire related to demographics, socioeconomic status, maternal education, as well as bednet use and recent febrile illness. We examined the associations between variables using multivariable logistic regression analysis, chi-squared statistic, Fisher’s exact test, and Spearman’s rank correlation, as appropriate. One hundred and twenty-three out of the 647 (19%) children in the study tested positive for malaria. Higher levels of maternal education were associated with a lower risk of malaria in their children. The prevalence of malaria in children of mothers with no education, primary school, and beyond primary was 41/138 (30%), 41/241 (17%), and 39/262 (15%), respectively (p = 0.001). In a multivariable logistic regression model adjusting for the effect of a child’s age and study site, the following remained significant predictors of malaria antigenemia: maternal education, number of children under five per household, and HIV serostatus. Higher maternal education, through several putative causal pathways, was associated with lower malaria prevalence among children in the DRC. Our findings suggest that maternal education might be an effective ‘social vaccine’ against malaria in the DRC and globally. PMID:28220714

Time trends in physical activity in the state of São Paulo, Brazil: 2002-2008.

PubMed

Matsudo, Victor K R; Matsudo, Sandra M; Araújo, Timóteo L; Andrade, Douglas R; Oliveira, Luis C; Hallal, Pedro C

2010-12-01

To document time trends in physical activity in the state of São Paulo, Brazil (2002-2008). In addition, we discuss the role of Agita São Paulo at explaining such trends. Cross-sectional surveys were carried out in 2002, 2003, 2006, and 2008 in the state of São Paulo, Brazil, using comparable sampling approaches and similar sample sizes. In all surveys, physical activity was measured using the short version of the International Physical Activity Questionnaire. Separate weekly scores of walking and moderate- and vigorous-intensity physical activities were generated; cutoff points of 0 and 150 min·wk were used. Also, we created a total physical activity score by summing these three types of activity. We used logistic regression models for adjusting time trends for the different sociodemographic compositions of the samples. The prevalence of no physical activity decreased from 9.6% in 2002 to 2.7% in 2008, whereas the proportion of subjects below the 150-min threshold decreased from 43.7% in 2002 to 11.6% in 2008. These trends were mainly explained by increases in walking and moderate-intensity physical activity. Increases in physical activity were slightly greater among females than among males. Logistic regression models confirmed that these trends were not due to the different compositions of the samples. Physical activity levels are increasing in the state of São Paulo, Brazil. Considering that the few data available in Brazil using the same instrument indicate exactly the opposite trend and that Agita São Paulo primarily incentives the involvement in moderate-intensity physical activity and walking, it seems that at least part of the trends described here are explained by the Agita São Paulo program.

Association Between Socioeconomic Status and Participation in Colonoscopy Screening Program in First Degree Relatives of Colorectal Cancer Patients.

PubMed

Chouhdari, Arezoo; Yavari, Parvin; Pourhoseingholi, Mohammad Amin; Sohrabi, Mohammad-Reza

2016-04-01

Approximately 15% to 25% of colorectal cancer (CRC) cases have positive family history for disease. Colonoscopy screening test is the best way for prevention and early diagnosis. Studies have found that first degree relatives (FDRs) with low socioeconomic status are less likely to participate in colonoscopy screening program. The aim of this study is to determine the association between socioeconomic status and participation in colonoscopy screening program in FDRs. This descriptive cross-sectional, study has been conducted on 200 FDRs who were consulted for undergoing colonoscopy screening program between 2007 and 2013 in research institute for gastroenterology and liver disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran. They were interviewed via phone by a valid questionnaire about socioeconomic status. For data analysis, chi-square, exact fisher and multiple logistic regression were executed by SPSS 19. The results indicated 58.5% participants underwent colonoscopy screening test at least once to the time of the interview. There was not an association between participation in colonoscopy screening program and socioeconomic status to the time of the interview in binomial analysis. But statistical significance between intention to participate and educational and income level were found. We found, in logistic regression analysis, that high educational level (Diploma and University degree in this survey) was a predictor to participate in colonoscopy screening program in FDRs. According to this survey low socioeconomic status is an important factor to hinder participation of FDRs in colonoscopy screening program. Therefore, planned interventions for elevation knowledge and attitude in FDRs with low educational level are necessary. Also, reducing colonoscopy test costs should be a major priority for policy makers.

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.

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.

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

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

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.

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

PubMed

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

2016-06-01

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

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…

Research design and statistical methods in Pakistan Journal of Medical Sciences (PJMS)

PubMed Central

Akhtar, Sohail; Shah, Syed Wadood Ali; Rafiq, M.; Khan, Ajmal

2016-01-01

Objective: This article compares the study design and statistical methods used in 2005, 2010 and 2015 of Pakistan Journal of Medical Sciences (PJMS). Methods: Only original articles of PJMS were considered for the analysis. The articles were carefully reviewed for statistical methods and designs, and then recorded accordingly. The frequency of each statistical method and research design was estimated and compared with previous years. Results: A total of 429 articles were evaluated (n=74 in 2005, n=179 in 2010, n=176 in 2015) in which 171 (40%) were cross-sectional and 116 (27%) were prospective study designs. A verity of statistical methods were found in the analysis. The most frequent methods include: descriptive statistics (n=315, 73.4%), chi-square/Fisher’s exact tests (n=205, 47.8%) and student t-test (n=186, 43.4%). There was a significant increase in the use of statistical methods over time period: t-test, chi-square/Fisher’s exact test, logistic regression, epidemiological statistics, and non-parametric tests. Conclusion: This study shows that a diverse variety of statistical methods have been used in the research articles of PJMS and frequency improved from 2005 to 2015. However, descriptive statistics was the most frequent method of statistical analysis in the published articles while cross-sectional study design was common study design. PMID:27022365

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.

Quantum regression theorem and non-Markovianity of quantum dynamics

NASA Astrophysics Data System (ADS)

Guarnieri, Giacomo; Smirne, Andrea; Vacchini, Bassano

2014-08-01

We explore the connection between two recently introduced notions of non-Markovian quantum dynamics and the validity of the so-called quantum regression theorem. While non-Markovianity of a quantum dynamics has been defined looking at the behavior in time of the statistical operator, which determines the evolution of mean values, the quantum regression theorem makes statements about the behavior of system correlation functions of order two and higher. The comparison relies on an estimate of the validity of the quantum regression hypothesis, which can be obtained exactly evaluating two-point correlation functions. To this aim we consider a qubit undergoing dephasing due to interaction with a bosonic bath, comparing the exact evaluation of the non-Markovianity measures with the violation of the quantum regression theorem for a class of spectral densities. We further study a photonic dephasing model, recently exploited for the experimental measurement of non-Markovianity. It appears that while a non-Markovian dynamics according to either definition brings with itself violation of the regression hypothesis, even Markovian dynamics can lead to a failure of the regression relation.

Analysis of the effectiveness of fertilizer subsidy policy and its effect on rice production in Karanganyar Regency

NASA Astrophysics Data System (ADS)

Mulyadiana, A. T.; Marwanti, S.; Rahayu, W.

2018-03-01

The research aims to know the factors which affecting rice production, and to know the effectiveness of fertilizer subsidy policy on rice production in Karanganyar Regency. The fertilizer subsidy policy was based on four indicators of fertilizer subsidy namely exact price, exact place, exact time, and exact quantity. Data was analyzed using descriptive quantitative and qualitative and multiple linear regression. The result of research showed that fertilizer subsidy policy in Karanganyar Regency evaluated from four indicators was not effective because the distribution of fertilizer subsidy to farmers still experience some mistakes. The result of regression analysis showed that production factors such as land area, use of urea fertilizer, use of NPK fertilizer, and effectiveness of fertilizer subsidy policy had positive correlation and significant influence on rice production, while labor utilization and use of seeds factors had no significant effect on rice production in Karanganyar Regency. This means that if the fertilizer subsidy policy is more effective, rice production is also increased.

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.

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

PubMed Central

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

2012-01-01

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

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

ERIC Educational Resources Information Center

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

2014-01-01

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

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

ERIC Educational Resources Information Center

French, Brian F.; Maller, Susan J.

2007-01-01

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

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

ERIC Educational Resources Information Center

Paek, Insu

2012-01-01

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

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

ERIC Educational Resources Information Center

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

2011-01-01

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

Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression

ERIC Educational Resources Information Center

Peng, Chao-Ying Joanne; Zhu, Jin

2008-01-01

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

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

ERIC Educational Resources Information Center

Atar, Burcu; Kamata, Akihito

2011-01-01

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

Multiple Logistic Regression Analysis of Cigarette Use among High School Students

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph

2011-01-01

A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

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

2010-01-01

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

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

ERIC Educational Resources Information Center

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

2013-01-01

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

Two-factor logistic regression in pediatric liver transplantation

NASA Astrophysics Data System (ADS)

Uzunova, Yordanka; Prodanova, Krasimira; Spasov, Lyubomir

2017-12-01

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

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

ERIC Educational Resources Information Center

Courtney, Jon R.; Prophet, Retta

2011-01-01

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

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

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

2015-08-01

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

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

PubMed

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

2012-01-01

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

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

PubMed

Hansson, Lisbeth; Khamis, Harry J

2008-12-01

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

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

PubMed

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

2016-12-01

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

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

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

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

Analysis of the single-vehicle cyclic inventory routing problem

NASA Astrophysics Data System (ADS)

Aghezzaf, El-Houssaine; Zhong, Yiqing; Raa, Birger; Mateo, Manel

2012-11-01

The single-vehicle cyclic inventory routing problem (SV-CIRP) consists of a repetitive distribution of a product from a single depot to a selected subset of customers. For each customer, selected for replenishments, the supplier collects a corresponding fixed reward. The objective is to determine the subset of customers to replenish, the quantity of the product to be delivered to each and to design the vehicle route so that the resulting profit (difference between the total reward and the total logistical cost) is maximised while preventing stockouts at each of the selected customers. This problem appears often as a sub-problem in many logistical problems. In this article, the SV-CIRP is formulated as a mixed-integer program with a nonlinear objective function. After a thorough analysis of the structure of the problem and its features, an exact algorithm for its solution is proposed. This exact algorithm requires only solutions of linear mixed-integer programs. Values of a savings-based heuristic for this problem are compared to the optimal values obtained for a set of some test problems. In general, the gap may get as large as 25%, which justifies the effort to continue exploring and developing exact and approximation algorithms for the SV-CIRP.

Trust in health information sources differs between young/middle and oldest old.

PubMed

Le, Thai; Chaudhuri, Shomir; White, Cathy; Thompson, Hilaire; Demiris, George

2014-01-01

Examine differences in trust of health information sources between the oldest old and young/middle old. Cross-sectional survey using convenience sampling. Eleven retirement communities. Older adults ≥65 years (N = 353). Self-rated trust in health information sources. Mann-Whitney U-test or Fisher exact test to compare trust between age groups; multinomial ordered logistic regression analyses to model trust in Internet information sources. The overall survey response rate was 26.6%. Differences in trust were identified between oldest old (n = 108) and young/middle old (n = 245) for pharmacist (p < .05), Internet (p < .001), television (p < .05), radio (p < .001), and newspaper (p < .05) sources. In the oldest old, we found associations between levels of trust in Internet sources and frequency of Internet use (β = 4.13, p < .001). Understanding where differences in trust arise can inform the design of resources to support the information-seeking process. When planning widespread distribution of health information to these distinct groups, program developers need to consider these differences.

Haemodialysis, nutritional disorders and hypoglycaemia in critical care.

PubMed

Crespo, Jeiel Carlos Lamonica; Gomes, Vanessa Rossato; Barbosa, Ricardo Luís; Padilha, Katia Grillo; Secoli, Silvia Regina

2017-03-09

This study aimed to determine hypoglycemia incidence and associated factors in critically ill patients. It looked at a retrospective cohort with 106 critically ill adult patients with 48 hours of glycaemic control and 72 hours of follow up. The dependent variable, hypoglycaemia (≤70 mg/dl), was assessed with respect to independent variables: age, diet, insulin, catecholamines, haemodialysis, nursing workload and the Simplified Acute Physiology Score. Statistical analysis was performed using Student's t-test, Fisher's exact test and logistic regression at 5% significance level. Incidence of hypoglycaemia was 14.2%. Hypoglycaemia was higher in the group of patients on catecholamines (p=0.040), with higher glycaemic variability (p<0.001) and death in the intensive care unit (p=0.008). Risk factors were identified as absence of oral diet (OR 5.11; 95% CI 1.04-25.10) and haemodialysis (OR 4.28; 95% CI 1.16-15.76). Patients on haemodialysis and with no oral diet should have their glycaemic control intensified in order to prevent and/or manage hypoglycaemic episodes.

Human Infection with Highly Pathogenic Avian Influenza Virus (H5N1) in Northern Vietnam, 2004–2005

PubMed Central

Hien, Nguyen Duc; Ha, Nguyen Hong; Van, Nguyen Tuong; Ha, Nguyen Thi Minh; Lien, Trinh Thi Minh; Thai, Nguyen Quoc; Trang, Van Dinh; Takahashi, Yoshimitsu; Kato, Yasuyuki; Kawana, Akihiko; Akita, Samu; Kudo, Koichiro

2009-01-01

We performed a retrospective case-series study of patients with influenza A (H5N1) admitted to the National Institute of Infectious and Tropical Diseases in Hanoi, Vietnam, from January 2004 through July 2005 with symptoms of acute respiratory tract infection, a history of high-risk exposure or chest radiographic findings such as pneumonia, and positive findings for A/H5 viral RNA by reverse transcription–PCR. We investigated data from 29 patients (mean age 35.1 years) of whom 7 (24.1%) had died. Mortality rates were 20% (5/25) and 50% (2/4) among patients treated with or without oseltamivir (p = 0.24), respectively, and were 33.3% (5/15) and 14.2% (2/14) among patients treated with and without methylprednisolone (p = 0.39), respectively. After exact logistic regression analysis was adjusted for variation in severity, no significant effectiveness for survival was observed among patients treated with oseltamivir or methylprednisolone. PMID:19116044

Influence of gender on office staff management in orthodontics.

PubMed

Holmes, Patrick B; Shroff, Bhavna; Best, Al M; Lindauer, Steven J

2010-11-01

To examine the gender differences in managing practice and staff members in orthodontic practices. All orthodontists in Virginia and Maryland (n = 427) were surveyed and demographic information was collected. For the crude analyses of the data, a Fisher's exact test or chi(2) test was performed. For the adjusted analyses, genders were compared using a logistic regression or analysis of covariance. The covariates were adjusted for age, program length, years in practice, number of years since graduation, and practice state. The length of the residency program attended did not differ with gender. No gender differences in practice ownership or creating the practice were observed. There was a significant gender difference in implementation of performance reviews: female orthodontists were more likely to provide performance reviews and tended to accept more poor reviews before staff termination than male orthodontists. However, when provided, no gender difference was observed in the number of performance reviews. Gender has a significant impact on the implementation of performance reviews in practices. Practice ownership status was not influenced by providers' gender.

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.

Comparison of exact, efron and breslow parameter approach method on hazard ratio and stratified cox regression model

NASA Astrophysics Data System (ADS)

Fatekurohman, Mohamat; Nurmala, Nita; Anggraeni, Dian

2018-04-01

Lungs are the most important organ, in the case of respiratory system. Problems related to disorder of the lungs are various, i.e. pneumonia, emphysema, tuberculosis and lung cancer. Comparing all those problems, lung cancer is the most harmful. Considering about that, the aim of this research applies survival analysis and factors affecting the endurance of the lung cancer patient using comparison of exact, Efron and Breslow parameter approach method on hazard ratio and stratified cox regression model. The data applied are based on the medical records of lung cancer patients in Jember Paru-paru hospital on 2016, east java, Indonesia. The factors affecting the endurance of the lung cancer patients can be classified into several criteria, i.e. sex, age, hemoglobin, leukocytes, erythrocytes, sedimentation rate of blood, therapy status, general condition, body weight. The result shows that exact method of stratified cox regression model is better than other. On the other hand, the endurance of the patients is affected by their age and the general conditions.

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.

Association of LMX1A genetic polymorphisms with susceptibility to congenital scoliosis in Chinese Han population.

PubMed

Wu, Nan; Yuan, Suomao; Liu, Jiaqi; Chen, Jun; Fei, Qi; Liu, Sen; Su, Xinlin; Wang, Shengru; Zhang, Jianguo; Li, Shugang; Wang, Yipeng; Qiu, Guixing; Wu, Zhihong

2014-10-01

A genetic association study of single nucleotide polymorphisms (SNPs) for the LMX1A gene with congenital scoliosis (CS) in the Chinese Han population. To determine whether LMX1A genetic polymorphisms are associated with susceptibility to CS. CS is a lateral curvature of the spine due to congenital vertebral defects, whose exact genetic cause has not been well established. The LMX1A gene was suggested as a potential human candidate gene for CS. However, no genetic study of LMX1A in CS has ever been reported. We genotyped 13 SNPs of the LMX1A gene in 154 patients with CS and 144 controls with matched sex and age. After conducting the Hardy-Weinberg equilibrium test, the data of 13 SNPs were analyzed by the allelic and genotypic association with logistic regression analysis. Furthermore, the genotype-phenotype association and haplotype association analysis were also performed. The 13 SNPs of the LMX1A gene met Hardy-Weinberg equilibrium in the controls, which was not in the cases. None of the allelic and genotypic frequencies of these SNPs showed significant difference between case and control groups (P > 0.05). However, the genotypic frequencies of rs1354510 and rs16841013 in the LMX1A gene were associated with CS predisposition in the unconditional logistic regression analysis (P = 0.02 and 0.018, respectively). Genotypic frequencies of 3 SNPs at rs6671290, rs1354510, and rs16841013 were found to exhibit significant differences between patients with CS with failure of formation and the healthy controls (P = 0.019, 0.007, and 0.006, respectively). Besides, in the model analysis by using unconditional logistic regression analysis, the optimized model for the 3 genotypic positive SNPs with failure of formation were rs6671290 (codominant; P = 0.025, Akaike information value = 316.6, Bayesian information criterion = 333.9), rs1354510 (overdominant; P = 0.0017, Akaike information value = 312.1, Bayesian information criterion = 325.9), and rsl6841013 (overdominant; P = 0.0016, Akaike information value = 311.1, Bayesian information criterion = 325), respectively. However, the haplotype distributions in the case group were not significantly different from those of the control group in the 3 haplotype blocks. To our knowledge, this is the first study to identify that the SNPs of the LMX1A gene might be associated with the susceptibility to CS and different clinical phenotypes of CS in the Chinese Han population. 4.

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…

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

USGS Publications Warehouse

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

2003-01-01

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

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

NASA Technical Reports Server (NTRS)

Messer, Bradley

2007-01-01

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

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

PubMed

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

2017-06-01

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

Two-time correlation function of an open quantum system in contact with a Gaussian reservoir

NASA Astrophysics Data System (ADS)

Ban, Masashi; Kitajima, Sachiko; Shibata, Fumiaki

2018-05-01

An exact formula of a two-time correlation function is derived for an open quantum system which interacts with a Gaussian thermal reservoir. It is provided in terms of functional derivative with respect to fictitious fields. A perturbative expansion and its diagrammatic representation are developed, where the small expansion parameter is related to a correlation time of the Gaussian thermal reservoir. The two-time correlation function of the lowest order is equivalent to that calculated by means of the quantum regression theorem. The result clearly shows that the violation of the quantum regression theorem is caused by a finiteness of the reservoir correlation time. By making use of an exactly solvable model consisting of a two-level system and a set of harmonic oscillators, it is shown that the two-time correlation function up to the first order is a good approximation to the exact one.

Cerebrovascular accidents in patients treated for choroidal neovascularization with ranibizumab in randomized controlled trials.

PubMed

Bressler, Neil M; Boyer, David S; Williams, David F; Butler, Steven; Francom, Steven F; Brown, Benton; Di Nucci, Flavia; Cramm, Timothy; Tuomi, Lisa L; Ianchulev, Tsontcho; Rubio, Roman G

2012-10-01

To analyze cerebrovascular accidents (CVAs) pooled from large, randomized, controlled clinical trials of ranibizumab treatment for neovascular age-related macular degeneration. Events in five trials (FOCUS, MARINA, ANCHOR, PIER, and SAILOR) were analyzed using a standard safety monitoring process. Exact methods, stratified by study, were used to test for treatment differences based on odds ratios. A stepwise logistic regression model was fit to classify subjects' risk for CVA based on medical history. Treatment differences in CVA rates at 1 year or 2 years were evaluated within risk groups using stratified exact methods. Pooled 2-year CVA rates were <3%; odds ratios (95% confidence intervals) for CVA risk were 1.2 (0.4-4.4) for ranibizumab 0.3-mg versus control, 2.2 (0.8-7.1) for 0.5 mg versus control, and 1.5 (0.8-3.0) for 0.5-mg versus 0.3-mg ranibizumab. No substantial increased risk of CVA for 0.5 mg versus 0.3 mg was identified in pooled analyses or any of the individual trials. In pooled analyses, the difference between 0.5-mg ranibizumab and control was larger (7.7 [1.2-177]) among high-risk CVA patients. This analysis provided some evidence, although not definitive, of a potential increased risk of CVA with ranibizumab versus control or with 0.5-mg versus 0.3-mg ranibizumab. Continued monitoring for CVA within clinical trials seems warrented.

Grading system to categorize breast MRI using BI-RADS 5th edition: a statistical study of non-mass enhancement descriptors in terms of probability of malignancy.

PubMed

Asada, Tatsunori; Yamada, Takayuki; Kanemaki, Yoshihide; Fujiwara, Keishi; Okamoto, Satoko; Nakajima, Yasuo

2018-03-01

To analyze the association of breast non-mass enhancement descriptors in the BI-RADS 5th edition with malignancy, and to establish a grading system and categorization of descriptors. This study was approved by our institutional review board. A total of 213 patients were enrolled. Breast MRI was performed with a 1.5-T MRI scanner using a 16-channel breast radiofrequency coil. Two radiologists determined internal enhancement and distribution of non-mass enhancement by consensus. Corresponding pathologic diagnoses were obtained by either biopsy or surgery. The probability of malignancy by descriptor was analyzed using Fisher's exact test and multivariate logistic regression analysis. The probability of malignancy by category was analyzed using Fisher's exact and multi-group comparison tests. One hundred seventy-eight lesions were malignant. Multivariate model analysis showed that internal enhancement (homogeneous vs others, p < 0.001, heterogeneous and clumped vs clustered ring, p = 0.003) and distribution (focal and linear vs segmental, p < 0.001) were the significant explanatory variables. The descriptors were classified into three grades of suspicion, and the categorization (3, 4A, 4B, 4C, and 5) by sum-up grades showed an incremental increase in the probability of malignancy (p < 0.0001). The three-grade criteria and categorization by sum-up grades of descriptors appear valid for non-mass enhancement.

Autism in children and correlates in Lebanon: a pilot case-control study.

PubMed

Hamadé, Aline; Salameh, Pascale; Medlej-Hashim, Myrna; Hajj-Moussa, Elie; Saadallah-Zeidan, Nina; Rizk, Francine

2013-09-17

Autism spectrum disorder (ASD) is a neurological disorder typically appearing before the age of three. The exact cause of autism remains uncertain, and several factors may be involved in its onset: genetic factors and possible environmental factors. The aim of this study was to assess the correlates of autism in the Lebanese population. We investigated the association of autism with several factors in 86 autism cases from specialized schools for children with developmental disabilities and 172 control children from regular public schools in the same regions. Several risk factors for autism were investigated after comparison with a cohort control on parental age, sex, maternal unhappy feeling during pregnancy, consanguineous marriage, and province of residence. The Chi-square test was used to compare nominal variables, and Fisher exact test was used in case expected values within cells were inferior to five. For quantitative variables, we used t-test to compare means between two groups, after checking their distribution normality. For multivariate analysis, we used a forward stepwise likelihood ratio logistic regression. We observed male predominance (79.1%) among autistic infants. There was a significant association between autism and older parents age (OR=1.27), male sex (OR=3.38), unhappy maternal feeling during pregnancy (OR=5.77), living close to industry (OR=6.58), previous childhood infection (OR=8.85), but none concerning maternal age, paternal age and consanguinity. In this pilot epidemiological study of autism in Lebanon, we found several prenatal and perinatal risk factors for autism that could be modified.

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

An integrative fuzzy Kansei engineering and Kano model for logistics services

NASA Astrophysics Data System (ADS)

Hartono, M.; Chuan, T. K.; Prayogo, D. N.; Santoso, A.

2017-11-01

Nowadays, customer emotional needs (known as Kansei) in product and especially in services become a major concern. One of the emerging services is the logistics services. In obtaining a global competitive advantage, logistics services should understand and satisfy their customer affective impressions (Kansei). How to capture, model and analyze the customer emotions has been well structured by Kansei Engineering, equipped with Kano model to strengthen its methodology. However, its methodology lacks of the dynamics of customer perception. More specifically, there is a criticism of perceived scores on user preferences, in both perceived service quality and Kansei response, whether they represent an exact numerical value. Thus, this paper is proposed to discuss an approach of fuzzy Kansei in logistics service experiences. A case study in IT-based logistics services involving 100 subjects has been conducted. Its findings including the service gaps accompanied with prioritized improvement initiatives are discussed.

Exact period-four solutions of a family of n-dimensional quadratic maps via harmonic balance and Gröbner bases.

PubMed

D'Amico, María Belén; Calandrini, Guillermo L

2015-11-01

Analytical solutions of the period-four orbits exhibited by a classical family of n-dimensional quadratic maps are presented. Exact expressions are obtained by applying harmonic balance and Gröbner bases to a single-input single-output representation of the system. A detailed study of a generalized scalar quadratic map and a well-known delayed logistic model is included for illustration. In the former example, conditions for the existence of bistability phenomenon are also introduced.

Exact period-four solutions of a family of n-dimensional quadratic maps via harmonic balance and Gröbner bases

NASA Astrophysics Data System (ADS)

D'Amico, María Belén; Calandrini, Guillermo L.

2015-11-01

Analytical solutions of the period-four orbits exhibited by a classical family of n-dimensional quadratic maps are presented. Exact expressions are obtained by applying harmonic balance and Gröbner bases to a single-input single-output representation of the system. A detailed study of a generalized scalar quadratic map and a well-known delayed logistic model is included for illustration. In the former example, conditions for the existence of bistability phenomenon are also introduced.

Dietary consumption patterns and laryngeal cancer risk.

PubMed

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

2016-06-01

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

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

ERIC Educational Resources Information Center

Guler, Nese; Penfield, Randall D.

2009-01-01

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

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

ERIC Educational Resources Information Center

Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

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

ERIC Educational Resources Information Center

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

2012-01-01

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

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

Treesearch

Susan L. King

2003-01-01

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

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

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

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

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.

A Classroom Game on a Negative Externality Correcting Tax: Revenue Return, Regressivity, and the Double Dividend

ERIC Educational Resources Information Center

Duke, Joshua M.; Sassoon, David M.

2017-01-01

The concept of negative externality is central to the teaching of environmental economics, but corrective taxes are almost always regressive. How exactly might governments return externality-correcting tax revenue to overcome regressivity and not alter marginal incentives? In addition, there is a desire to achieve a double dividend in the use of…

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.

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 survey of community members' perceptions of medical errors in Oman

PubMed Central

Al-Mandhari, Ahmed S; Al-Shafaee, Mohammed A; Al-Azri, Mohammed H; Al-Zakwani, Ibrahim S; Khan, Mushtaq; Al-Waily, Ahmed M; Rizvi, Syed

2008-01-01

Background Errors have been the concern of providers and consumers of health care services. However, consumers' perception of medical errors in developing countries is rarely explored. The aim of this study is to assess community members' perceptions about medical errors and to analyse the factors affecting this perception in one Middle East country, Oman. Methods Face to face interviews were conducted with heads of 212 households in two villages in North Al-Batinah region of Oman selected because of close proximity to the Sultan Qaboos University (SQU), Muscat, Oman. Participants' perceived knowledge about medical errors was assessed. Responses were coded and categorised. Analyses were performed using Pearson's χ2, Fisher's exact tests, and multivariate logistic regression model wherever appropriate. Results Seventy-eight percent (n = 165) of participants believed they knew what was meant by medical errors. Of these, 34% and 26.5% related medical errors to wrong medications or diagnoses, respectively. Understanding of medical errors was correlated inversely with age and positively with family income. Multivariate logistic regression revealed that a one-year increase in age was associated with a 4% reduction in perceived knowledge of medical errors (CI: 1% to 7%; p = 0.045). The study found that 49% of those who believed they knew the meaning of medical errors had experienced such errors. The most common consequence of the errors was severe pain (45%). Of the 165 informed participants, 49% felt that an uncaring health care professional was the main cause of medical errors. Younger participants were able to list more possible causes of medical errors than were older subjects (Incident Rate Ratio of 0.98; p < 0.001). Conclusion The majority of participants believed they knew the meaning of medical errors. Younger participants were more likely to be aware of such errors and could list one or more causes. PMID:18664245

Chickenpox in childhood is associated with decreased atopic disorders, IgE, allergic sensitization, and leukocyte subsets.

PubMed

Silverberg, Jonathan I; Kleiman, Edward; Silverberg, Nanette B; Durkin, Helen G; Joks, Rauno; Smith-Norowitz, Tamar A

2012-02-01

Wild-type varicella zoster infection (WTVZV) up to 8 yr of age has been shown to protect against atopic dermatitis (AD) and asthma. We sought to determine whether WTVZV in childhood protects against atopic disorders, allergic sensitization or decreases serum Immunoglobulin E (IgE) levels. We conducted a retrospective, practice-based study of outpatient pediatric practices in NY. One hundred children with WTVZV up to 8 yr of age and 323 children who received varicella vaccine (VV) were randomly selected. WTVZV up to 8 yr of age is associated with decreased odds of subsequent asthma (exact logistic regression; OR = 0.12, 95% CI = 0.03-0.57, p = 0.003), allergic rhinoconjunctivitis (OR = 0.16, 95% CI = 0.05-0.49, p = 0.0003), and AD (OR = 0.57, 95% CI = 0.33-0.96, p = 0.02), but not food allergies (p = 0.78); decreased total serum IgE levels [mixed linear model, LSM (95% CI): 129.09 (33.22-501.63) vs. 334.21 (102.38-1091.04) IU/ml; p = 0.02] remained significant at all time intervals after WTVZV (<5, 5-10, and >10) compared with VV (p = 0.003-0.03). WTVZV was associated with decreased allergic sensitization (logistic regression, OR = 0.11, 95% CI = 0.03-0.38, p = 0.0004). WTVZV is also associated with persistently decreased numbers of peripheral blood lymphocytes (p < 0.0001) for up to 12 yr (p = 0.0003-0.047), monocytes (p = 0.002) for up to 16 yr (p < 0.001) and basophils at ages 4-6, 10-12, and 14-16 (p < 0.03). WTVZV up to 8 yr of age protects against atopic disorders, which is likely mediated by suppression of IgE production and allergic sensitization, as well as altered leukocyte distributions. © 2011 John Wiley & Sons A/S.

A Cross-border Comparison of Hepatitis B Testing Among Chinese Residing in Canada and the United States

PubMed Central

Tu, Shin-Ping; Li, Lin; Tsai, Jenny Hsin-Chun; Yip, Mei-Po; Terasaki, Genji; Teh, Chong; Yasui, Yutaka; Hislop, T Gregory; Taylor, Vicky

2013-01-01

Background The Western Pacific region has the highest level of endemic hepatitis B virus (HBV) infection in the world, with the Chinese representing nearly one-third of infected persons globally. HBV carriers are potentially infectious to others and have an increased risk of chronic active hepatitis, cirrhosis, and hepatocellular carcinoma. Studies from the U.S. and Canada demonstrate that immigrants, particularly from Asia, are disproportionately affected by liver cancer. Purpose Given the different health care systems in Seattle and Vancouver, two geographically proximate cities, we examined HBV testing levels and factors associated with testing among Chinese residents of these cities. Methods We surveyed Chinese living in areas of Seattle and Vancouver with relatively high proportions of Chinese residents. In-person interviews were conducted in Cantonese, Mandarin, or English. Our bivariate analyses consisted of the chi-square test, with Fisher’s Exact test as necessary. We then performed unconditional logistic regression, first examining only the city effect as the sole explanatory variable of the model, then assessing the adjusted city effect in a final main-effects model that was constructed through backward selection to select statistically significant variables at alpha = 0.05. Results Survey cooperation rates for Seattle and Vancouver were 58% and 59%, respectively. In Seattle, 48% reported HBV testing, whereas in Vancouver, 55% reported testing. HBV testing in Seattle was lower than in Vancouver, with a crude odds ratio of 0.73 (95% CI = 0.56, 0.94). However after adjusting for demographic, health care access, knowledge, and social support variables, we found no significant differences in HBV testing between the two cities. In our logistic regression model, the odds of HBV testing were greatest when the doctor recommended the test, followed by when the employer asked for the test. Discussion Findings from this study support the need for additional research to examine the effectiveness of clinic-based and workplace interventions to promote HBV testing among immigrants to North America. PMID:19640196

A cross-border comparison of hepatitis B testing among chinese residing in Canada and the United States.

PubMed

Tu R, Shin-Ping; Li, Lin; Tsai, Jenny Hsin-Chun; Yip, Mei-Po; Terasaki, Genji; Teh, Chong; Yasui, Yutaka; Hislop, T Gregory; Taylor, Vicky

2009-01-01

The Western Pacific region has the highest level of endemic hepatitis B virus (HBV) infection in the world, with the Chinese representing nearly one-third of infected persons globally. HBV carriers are potentially infectious to others and have an increased risk of chronic active hepatitis, cirrhosis, and hepatocellular carcinoma. Studies from the U.S. and Canada demonstrate that immigrants, particularly from Asia, are disproportionately affected by liver cancer. Given the different health care systems in Seattle and Vancouver, two geographically proximate cities, we examined HBV testing levels and factors associated with testing among Chinese residents of these cities. We surveyed Chinese living in areas of Seattle and Vancouver with relatively high proportions of Chinese residents. In-person interviews were conducted in Cantonese, Mandarin, or English. Our bivariate analyses consisted of the chi-square test, with Fisher's Exact test as necessary. We then performed unconditional logistic regression, first examining only the city effect as the sole explanatory variable of the model, then assessing the adjusted city effect in a final main-effects model that was constructed through backward selection to select statistically significant variables at alpha=0.05. Survey cooperation rates for Seattle and Vancouver were 58% and 59%, respectively. In Seattle, 48% reported HBV testing, whereas in Vancouver, 55% reported testing. HBV testing in Seattle was lower than in Vancouver, with a crude odds ratio of 0.73 (95% CI = 0.56, 0.94). However after adjusting for demographic, health care access, knowledge, and social support variables, we found no significant differences in HBV testing between the two cities. In our logistic regression model, the odds of HBV testing were greatest when the doctor recommended the test, followed by when the employer asked for the test. Findings from this study support the need for additional research to examine the effectiveness of clinic-based and workplace interventions to promote HBV testing among immigrants to North America.

Community acquired bacterial meningitis in Cuba: a follow up of a decade

PubMed Central

2010-01-01

Background Community acquired Bacterial Meningitis (BM) remains a serious threat to global health. Cuban surveillance system for BM allowed to characterize the main epidemiological features of this group of diseases, as well as to assess the association of some variables with mortality. Results of the BM surveillance in Cuba are presented in this paper. Methods A follow up of BM cases reported to the Institute "Pedro Kourí" by the National Bacterial Meningitis Surveillance System from 1998 to 2007 was completed. Incidence and case-fatality rate (CFR) were calculated. Univariate analysis and logistic regression were used to elucidate associated factors to mortality comparing death versus survival. Relative Risk (RR) or odds ratio and its 95% confidence interval (CI 95%) were estimated, using either a Chi-squared Test or Fisher's Exact Test as appropriate. A Holt-Winters model was used to assess seasonality. Results 4 798 cases of BM (4.3 per 100 000 population) were reported, with a decreasing trend of the incidence. Highest incidence was observed in infants and elderly. Overall CFR reached 24.1% affecting mostly older adults. S. pneumoniae (23.6%), N. meningitidis(8.2%) and H. influenzaetype b (6.0%) were the main causative agents. Males predominate in the incidence. Highest incidence and CFR were mainly clustered in the centre of the island. The univariate analysis did not show association between delayed medical consultation (RR = 1.20; CI = 1.07-1.35) or delayed hospitalization (RR = 0.98; CI = 0.87-1.11) and the fatal outcome. Logistic regression model showed association of categories housewife, pensioned, imprisoned, unemployed, S. peumoniae and other bacteria with mortality. Seasonality during September, January and March was observed. Conclusions The results of the National Program for Control and Prevention of the Neurological Infectious Syndrome evidenced a reduction of the BM incidence, but not the CFR. Multivariate analysis identified an association of mortality with some societal groups as well as with S. peumoniae. PMID:20500858

Association factor analysis between osteoporosis with cerebral artery disease: The STROBE study.

PubMed

Jin, Eun-Sun; Jeong, Je Hoon; Lee, Bora; Im, Soo Bin

2017-03-01

The purpose of this study was to determine the clinical association factors between osteoporosis and cerebral artery disease in Korean population. Two hundred nineteen postmenopausal women and men undergoing cerebral computed tomography angiography were enrolled in this study to evaluate the cerebral artery disease by cross-sectional study. Cerebral artery disease was diagnosed if there was narrowing of 50% higher diameter in one or more cerebral vessel artery or presence of vascular calcification. History of osteoporotic fracture was assessed using medical record, and radiographic data such as simple radiography, MRI, and bone scan. Bone mineral density was checked by dual-energy x-ray absorptiometry. We reviewed clinical characteristics in all patients and also performed subgroup analysis for total or extracranial/ intracranial cerebral artery disease group retrospectively. We performed statistical analysis by means of chi-square test or Fisher's exact test for categorical variables and Student's t-test or Wilcoxon's rank sum test for continuous variables. We also used univariate and multivariate logistic regression analyses were conducted to assess the factors associated with the prevalence of cerebral artery disease. A two-tailed p-value of less than 0.05 was considered as statistically significant. All statistical analyses were performed using R (version 3.1.3; The R Foundation for Statistical Computing, Vienna, Austria) and SPSS (version 14.0; SPSS, Inc, Chicago, Ill, USA). Of the 219 patients, 142 had cerebral artery disease. All vertebral fracture was observed in 29 (13.24%) patients. There was significant difference in hip fracture according to the presence or absence of cerebral artery disease. In logistic regression analysis, osteoporotic hip fracture was significantly associated with extracranial cerebral artery disease after adjusting for multiple risk factors. Females with osteoporotic hip fracture were associated with total calcified cerebral artery disease. Some clinical factors such as age, hypertension, and osteoporotic hip fracture, smoking history and anti-osteoporosis drug use were associated with cerebral artery disease.

PI-RADS version 2 for prediction of pathological downgrading after radical prostatectomy: a preliminary study in patients with biopsy-proven Gleason Score 7 (3+4) prostate cancer.

PubMed

Woo, Sungmin; Kim, Sang Youn; Lee, Joongyub; Kim, Seung Hyup; Cho, Jeong Yeon

2016-10-01

To evaluate PI-RADSv2 for predicting pathological downgrading after radical prostatectomy (RP) in patients with biopsy-proven Gleason score (GS) 7(3+4) PC. A total of 105 patients with biopsy-proven GS 7(3+4) PC who underwent multiparametric prostate MRI followed by RP were included. Two radiologists assigned PI-RADSv2 scores for each patient. Preoperative clinicopathological variables and PI-RADSv2 scores were compared between patients with and without downgrading after RP using the Wilcoxon rank sum test or Fisher's exact test. Logistic regression analyses with Firth's bias correction were performed to assess their association with downgrading. Pathological downgrading was identified in ten (9.5 %) patients. Prostate-specific antigen (PSA), PSA density, percentage of cores with GS 7(3+4), and greatest percentage of core length (GPCL) with GS 7(3+4) were significantly lower in patients with downgrading (p = 0.002-0.037). There was no significant difference in age and clinical stage (p = 0.537-0.755). PI-RADSv2 scores were significantly lower in patients with downgrading (3.8 versus 4.4, p = 0.012). At univariate logistic regression analysis, PSA, PSA density, and PI-RADSv2 scores were significant predictors of downgrading (p = 0.003-0.022). Multivariate analysis revealed only PSA density and PI-RADSv2 scores as independent predictors of downgrading (p = 0.014-0.042). The PI-RADSv2 scoring system was an independent predictor of pathological downgrading after RP in patients with biopsy-proven GS 7(3+4) PC. • PI-RADSv2 was an independent predictor of downgrading in biopsy-proven GS 7(3+4) PC • PSA density was also an independent predictor of downgrading • MRI may assist in identifying AS candidates in biopsy-proven GS 7(3+4) PC patients.

Optimal Combination of Non-Invasive Tools for the Early Detection of Potentially Life-Threatening Emergencies in Gynecology

PubMed Central

Varas, Catalina; Ravit, Marion; Mimoun, Camille; Panel, Pierre; Huchon, Cyrille; Fauconnier, Arnaud

2016-01-01

Objectives Potentially life-threatening gynecological emergencies (G-PLEs) are acute pelvic conditions that may spontaneously evolve into a life-threatening situation, or those for which there is a risk of sequelae or death in the absence of prompt diagnosis and treatment. The objective of this study was to identify the best combination of non-invasive diagnostic tools to ensure an accurate diagnosis and timely response when faced with G-PLEs for patients arriving with acute pelvic pain at the Gynecological Emergency Department (ED). Methods The data on non-invasive diagnostic tools were sourced from the records of patients presenting at the ED of two hospitals in the Parisian suburbs (France) with acute pelvic pain between September 2006 and April 2008. The medical history of the patients was obtained through a standardized questionnaire completed for a prospective observational study, and missing information was completed with data sourced from the medical forms. Diagnostic tool categories were predefined as a collection of signs or symptoms. We analyzed the association of each sign/symptom with G-PLEs using Pearson’s Chi-Square or Fischer’s exact tests. Symptoms and signs associated with G-PLEs (p-value < 0.20) were subjected to logistic regression to evaluate the diagnostic value of each of the predefined diagnostic tools and in various combinations. Results The data of 365 patients with acute pelvic pain were analyzed, of whom 103 were confirmed to have a PLE. We analyzed five diagnostic tools by logistic regression: Triage Process, History-Taking, Physical Examination, Ultrasonography, and Biological Exams. The combination of History-Taking and Ultrasonography had a C-index of 0.83, the highest for a model combining two tools. Conclusions The use of a standardized self-assessment questionnaire for history-taking and focal ultrasound examination were found to be the most successful tool combination for the diagnosis of gynecological emergencies in a Gynecological ED. Additional tools, such as physical examination, do not add substantial diagnostic value. PMID:27583697

Community acquired bacterial meningitis in Cuba: a follow up of a decade.

PubMed

Pérez, Antonio E; Dickinson, Félix O; Rodríguez, Misladys

2010-05-25

Community acquired Bacterial Meningitis (BM) remains a serious threat to global health. Cuban surveillance system for BM allowed to characterize the main epidemiological features of this group of diseases, as well as to assess the association of some variables with mortality. Results of the BM surveillance in Cuba are presented in this paper. A follow up of BM cases reported to the Institute "Pedro Kourí" by the National Bacterial Meningitis Surveillance System from 1998 to 2007 was completed. Incidence and case-fatality rate (CFR) were calculated. Univariate analysis and logistic regression were used to elucidate associated factors to mortality comparing death versus survival. Relative Risk (RR) or odds ratio and its 95% confidence interval (CI 95%) were estimated, using either a Chi-squared Test or Fisher's Exact Test as appropriate. A Holt-Winters model was used to assess seasonality. 4798 cases of BM (4.3 per 100,000 population) were reported, with a decreasing trend of the incidence. Highest incidence was observed in infants and elderly. Overall CFR reached 24.1% affecting mostly older adults. S. pneumoniae (23.6%), N. meningitidis (8.2%) and H. influenzae type b (6.0%) were the main causative agents. Males predominate in the incidence. Highest incidence and CFR were mainly clustered in the centre of the island. The univariate analysis did not show association between delayed medical consultation (RR = 1.20; CI = 1.07-1.35) or delayed hospitalization (RR = 0.98; CI = 0.87-1.11) and the fatal outcome. Logistic regression model showed association of categories housewife, pensioned, imprisoned, unemployed, S. pneumoniae and other bacteria with mortality. Seasonality during September, January and March was observed. The results of the National Program for Control and Prevention of the Neurological Infectious Syndrome evidenced a reduction of the BM incidence, but not the CFR. Multivariate analysis identified an association of mortality with some societal groups as well as with S. pneumoniae.

Highly specific reasons for nonadherence to antiretroviral therapy: results from the German adherence study.

PubMed

Boretzki, Johanna; Wolf, Eva; Wiese, Carmen; Noe, Sebastian; Balogh, Annamaria; Meurer, Anja; Krznaric, Ivanka; Zink, Alexander; Lersch, Christian; Spinner, Christoph D

2017-01-01

Reasons for and frequency of nonadherence to antiretroviral therapy (ART) may have changed due to pharmacological improvements. In addition, the importance of known non-pharmacologic reasons for nonadherence is unclear. We performed a cross-sectional, noninterventional, multicenter study to identify current reasons for nonadherence. Patients were categorized by physicians into the following adherence groups: good, unstable, or poor adherence. Co-variables of interest included age, sex, time since HIV diagnosis, ART duration, current ART regimen, HIV transmission route, comorbidity, HIV-1 RNA viral load (VL), and CD4 cell count. Patients self-reported the number of missed doses and provided their specific reasons for nonadherent behavior. Statistical analyses were performed using Fisher's extended exact test, Kruskal-Wallis test, and logistic regression models. Our study assessed 215 participants with good (n=162), unstable (n=36), and poor adherence (n=17). Compared to patients with good adherence, patients with unstable and poor adherence reported more often to have missed at least one dose during the last week (good 11% vs unstable 47% vs poor 63%, p <0.001). Physicians' adherence assessment was concordant with patients' self-reports of missed doses during the last week (no vs one or more) in 81% cases. Similarly, we found a strong association of physicians' assessment with viral suppression. Logistic regression analysis showed that "reduced adherence" - defined as unstable or poor - was significantly associated with patients <30 years old, intravenous drug use, history of acquired immune deficiency syndrome (AIDS), and psychiatric disorders ( p <0.05). Univariate analyses showed that specific reasons, such as questioning the efficacy/dosing of ART, HIV stigma, interactive toxicity beliefs regarding alcohol and/or party drugs, and dissatisfaction with regimen complexity, correlated with unstable or poor adherence ( p <0.05). Identification of factors associated with poor adherence helps in identifying patients with a higher risk for nonadherence. Reasons for nonadherence should be directly addressed in every patient, because they are common and constitute possible adherence intervention points.

Delayed Enhancement of Ascites After IV Contrast Material Administration at CT: Time Course and Clinical Correlation

PubMed Central

Benedetti, Nancy; Aslam, Rizwan; Wang, Zhen J.; Joe, Bonnie N.; Fu, Yanjun; Yee, Judy; Yeh, Benjamin M.

2010-01-01

OBJECTIVE The objective of our study was to determine the prevalence and clinical predictors of delayed contrast enhancement of ascites. MATERIALS AND METHODS In this retrospective study, 132 consecutive patients with ascites who underwent repeated abdominopelvic CT examinations performed within 7 days of each other were identified. These patients included 112 patients who received and 20 who did not receive IV contrast material at the initial CT examination. For each examination, we recorded the CT attenuation of the ascites. For the follow-up scan, the presence of delayed enhancement of ascites was defined as an increase in CT attenuation > 10 HU over baseline. The Fisher’s exact test, unpaired Student’s t test, and logistic regression were used to determine predictors of delayed enhancement of ascites. RESULTS A threshold increase in the attenuation of ascites by > 10 HU or more between the initial and follow-up CT examinations occurred only when IV contrast material was given with the initial examination. The increased attenuation was due to delayed contrast enhancement of ascites and occurred in 15 of the 112 patients (13%). Of the 16 patients scanned less than 1 day apart, 10 (63%) showed delayed enhancement of ascites. Delayed enhancement was not observed 3 or more days after IV contrast material administration. For each 1 mg/dL increase in serum creatinine level, the likelihood of delayed enhancement of ascites increased (odds ratio, 2.02; 95% CI, 1.11–3.69). Multivariate logistic regression showed that a short time interval between examinations (p < 0.001), increased serum creatinine level (p < 0.001), and presence of loculated ascites (p = < 0.01) were independent predictors of the magnitude of delayed enhancement of ascites. CONCLUSION Delayed contrast enhancement of ascites occurs commonly after recent prior IV contrast material administration and should not be mistaken for hemoperitoneum or proteinaceous fluid such as pus. PMID:19696286

Are nurses verbally abused? A cross-sectional study of nurses at a university hospital, Eastern Province, Saudi Arabia

PubMed Central

Al-Shamlan, Nouf A.; Jayaseeli, Nithya; Al-Shawi, Moneera M.; Al-Joudi, Abdullah S.

2017-01-01

BACKGROUND: Workplace violence against health-care workers is a significant problem worldwide. Nurses are at a higher risk of exposure to violence. Studies available in Saudi Arabia are few. OBJECTIVES: The objective of the study was to estimate the prevalence of verbal abuse of nurses at King Fahd Hospital of the University (KFHU) in Khobar, Saudi Arabia, and to identify consequences and the demographic and work-related characteristics associated with it. MATERIALS AND METHODS: This cross-sectional study of 391 nurses by total sample was conducted between November and December 2015, using a modified self-administered questionnaire developed by the World Health Organization. Data was entered, and analyzed using SPSS Version 16.0. The descriptive statistics were reported using frequency and percentages for all categorical variables. Chi-squared tests or Fisher's Exact test, as appropriate, were performed to test the associations of verbal abuse with the demographic and work-related characteristics of the participants. Variables with p < 0.05 were considered significant. Logistic regression analysis performed to determine association between verbal abuse and independent variables. RESULTS: In a period of 1 year before the study, about three out of ten nurses experienced verbal abuse (30.7%). In the majority of cases, the victims did not report the incidents, mostly because they believed that reporting would yield no positive results. Logistic regression analysis revealed that male nurses, nurses in the emergency department, and nurses who indicated that there were procedures for reporting violence in their workplace were more vulnerable to workplace verbal abuse. CONCLUSION: Workplace verbal abuse is a significant challenge in KFHU. For decision makers, it is rather disturbing that a lot of cases go unreported even though procedures for reporting exist. Implementation of an efficient transparent reporting system that provides follow-up investigations is mandatory. In addition, all victims should be helped with counseling and support. PMID:28932162

US-guided percutaneous cholecystostomy: features predicting culture-positive bile and clinical outcome.

PubMed

Sosna, Jacob; Kruskal, Jonathan B; Copel, Laurian; Goldberg, S Nahum; Kane, Robert A

2004-03-01

To assess sonographic and clinical features that might be used to predict infected bile and/or patient outcome from ultrasonography (US)-guided percutaneous cholecystostomy. Between February 1997 and August 2002 at one institution, 112 patients underwent US-guided percutaneous cholecystostomy (59 men, 53 women; average age, 69.3 years). All US images were scored on a defined semiquantitative scale according to preset parameters: (a) gallbladder distention, (b) sludge and/or stones, (c) wall appearance, (d) pericholecystic fluid, and (e) common bile duct size and/or choledocholithiasis. Separate and total scores were generated. Retrospective evaluation of (a) the bacteriologic growth of aspirated bile and its color and (b) clinical indices (fever, white blood cell count, bilirubin level, liver function test results) was conducted by reviewing medical records. For each patient, the clinical manifestation was classified into four groups: (a) localized right upper quadrant symptoms, (b) generalized abdominal symptoms, (c) unexplained sepsis, or (d) sepsis with other known infection. Logistic regression models, exact Wilcoxon-Mann-Whitney test, and the Kruskal-Wallis test were used. Forty-seven (44%) of 107 patients had infected bile. A logistic regression model showed that wall appearance, distention, bile color, and pericholecystic fluid were not individually significant predictors for culture-positive bile, leaving sludge and/or stones (P =.003, odds ratio = 1.647), common bile duct status (P =.02, odds ratio = 2.214), and total score (P =.007, odds ratio = 1.267). No US covariates or clinical indices predicted clinical outcome. Clinical manifestation was predictive of clinical outcome (P =.001) and aspirating culture-positive bile (P =.008); specifically, 30 (86%) of 35 patients with right upper quadrant symptoms had their condition improve, compared with one (7%) of 15 asymptomatic patients with other known causes of infection. US variables can be used to predict culture-positive bile but not patient outcome. Clinical manifestation is important because patients with right upper quadrant symptoms have the best clinical outcome. Copyright RSNA, 2004

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.

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.

Role of 5-HTTLPR polymorphism in the development of the inward/outward personality organization: a genetic association study.

PubMed

Nardi, Bernardo; Marini, Alessandra; Turchi, Chiara; Arimatea, Emidio; Tagliabracci, Adriano; Bellantuono, Cesario

2013-01-01

Reciprocity with primary caregivers affects subjects' adaptive abilities toward the construction of the most useful personal meaning organization (PMO) with respect to their developmental environment. Within cognitive theory the post-rationalist approach has outlined two basic categories of identity construction and of regulation of cognitive and emotional processes: the Outward and the Inward PMO. The presence of different, consistent clinical patterns in Inward and Outward subjects is paralleled by differences in cerebral activation during emotional tasks on fMRI and by different expression of some polymorphisms in serotonin pathways. Since several lines of evidence support a role for the 5-HTTLPR polymorphism in mediating individual susceptibility to environmental emotional stimuli, this study was conducted to investigate its influence in the development of the Inward/Outward PMO. PMO was assessed and the 5-HTTLPR polymorphism investigated in 124 healthy subjects who were subdivided into an Inward (n = 52) and an Outward (n = 72) group. Case-control comparisons of short allele (S) frequencies showed significant differences between Inwards and Outwards (p = 0.036, χ2 test; p = 0.026, exact test). Genotype frequencies were not significantly different although values slightly exceeded p ≤ 0.05 (p = 0.056, χ2 test; p = 0.059, exact test). Analysis of the 5-HTTLPR genotypes according to the recessive inheritance model showed that the S/S genotype increased the likelihood of developing an Outward PMO (p = 0.0178, χ2 test; p = 0.0143, exact test; OR = 3.43, CI (95%) = 1.188-9.925). A logistic regression analysis confirmed the association between short allele and S/S genotypes with the Outward PMO also when gender and age were considered. However none of the differences remained significant after correction for multiple testing, even though using the recessive model they approach significance. Overall our data seem to suggest a putative genetic basis for interindividual differences in PMO development.

Role of 5-HTTLPR Polymorphism in the Development of the Inward/Outward Personality Organization: A Genetic Association Study

PubMed Central

Nardi, Bernardo; Marini, Alessandra; Turchi, Chiara; Arimatea, Emidio; Tagliabracci, Adriano; Bellantuono, Cesario

2013-01-01

Reciprocity with primary caregivers affects subjects' adaptive abilities toward the construction of the most useful personal meaning organization (PMO) with respect to their developmental environment. Within cognitive theory the post-rationalist approach has outlined two basic categories of identity construction and of regulation of cognitive and emotional processes: the Outward and the Inward PMO. The presence of different, consistent clinical patterns in Inward and Outward subjects is paralleled by differences in cerebral activation during emotional tasks on fMRI and by different expression of some polymorphisms in serotonin pathways. Since several lines of evidence support a role for the 5-HTTLPR polymorphism in mediating individual susceptibility to environmental emotional stimuli, this study was conducted to investigate its influence in the development of the Inward/Outward PMO. PMO was assessed and the 5-HTTLPR polymorphism investigated in 124 healthy subjects who were subdivided into an Inward (n = 52) and an Outward (n = 72) group. Case-control comparisons of short allele (S) frequencies showed significant differences between Inwards and Outwards (p = 0.036, χ2 test; p = 0.026, exact test). Genotype frequencies were not significantly different although values slightly exceeded p≤0.05 (p = 0.056, χ2 test; p = 0.059, exact test). Analysis of the 5-HTTLPR genotypes according to the recessive inheritance model showed that the S/S genotype increased the likelihood of developing an Outward PMO (p = 0.0178, χ2 test; p = 0.0143, exact test; OR = 3.43, CI (95%) = 1.188–9.925). A logistic regression analysis confirmed the association between short allele and S/S genotypes with the Outward PMO also when gender and age were considered. However none of the differences remained significant after correction for multiple testing, even though using the recessive model they approach significance. Overall our data seem to suggest a putative genetic basis for interindividual differences in PMO development. PMID:24358153

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.

What Exactly is Space Logistics?

DTIC Science & Technology

2011-01-01

series, movies, and video games. Such phrases as “the final frontier” (from the opening lines of Star Trek ) or “the ulti- mate high ground” (from...years as NASA , DoD, and commercial space launch customers brought individual requirements to the table; there was no single, focused development

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.

[The impact of breastfeeding promotion in women with formal employment].

PubMed

Brasileiro, Aline Alves; Possobon, Rosana de Fátima; Carrascoza, Karina Camilo; Ambrosano, Gláucia Maria Bovi; Moraes, Antônio Bento Alves de

2010-09-01

This study focused on programs to promote breastfeeding in order to prevent early weaning of working mothers' infant children. A non-randomized intervention study was conducted using a survey of mothers who had returned to work after childbirth, including both participants and non-participants in a program to promote breastfeeding. The sample consisted of 200 mothers of infants ranging from 6 to 10 months of age. Factors associated with early weaning were analyzed with the chi-square and Fisher's exact tests and multiple logistic regression (α = 0.05). The results showed statistical differences between the groups in relation to exclusive breastfeeding (p < 0.0001) and breastfeeding (p < 0.0001). There was a statistically significant difference (p = 0.0056) between the groups in relation to time between childbirth and return to work. There was no difference between the end of maternity leave and weaning time. Mothers that were unable to nurse their infants during the work shift showed 4.98 times higher odds (95%CI: 1.27-19.61) of weaning them before the fourth month of age.

Pregnancy outcomes of women with failure to retain rubella immunity.

PubMed

Schwartzenburg, Christopher J; Gilmandyar, Dzhamala; Thornburg, Loralei L; Hackney, David N

2014-12-01

We sought to explore the clinical variables associated with the loss of rubella immunity during pregnancy and to determine if these changes are linked to obstetrical complications. This is a case-control study in which women were identified whose rubella antibody titers were equivocal or non-immune and compared to those who had retained immunity. Two hundred and eighty-five cases were identified and compared to the same number of controls using Student's t test, Mann-Whitney U-test or Fisher's exact test. Univariate and multivariate logistic regressions were employed. Subjects with diminished immunity were more likely to have public insurance and higher gravidity with a trend toward increased tobacco use. Diminished rubella immunity was not associated with adverse obstetrical outcomes, including preterm birth and pre-eclampsia and is likely not a risk factor for these pregnancy outcomes. While no adverse pregnancy outcomes were associated with a loss of rubella immunity, women with greater number of pregnancies appear to lose their immunity to rubella. This relationship needs to be explored further and if proven, revaccination prior to pregnancy may need to be addressed.

[Permanence of the piamontese culture on food practices in third generation descendants in Piamonte (Province of Santa Fe, Argentina)].

PubMed

Andreatta, María M; Muñoz, Sonia E; Navarro, Alicia

2004-01-01

This paper describes the influence of the piemontese culture on food practices of students living in Piamonte, Santa Fe, Argentina. Food practices of 96 students with Piamontese ancestry (PA) (n = 57) and without Piamontese ancestry (No-PA) (n = 39) were studied along 2002 using a self-administered questionnare. Data were analysed by Chi square test, Fisher's exact test, multiple correspondance analysis and logistic regression. Consumption of bagna cauda (p < 0.05) and polenta (p < 0.1) were higher among PA. Differences on the elaboration of polenta and pasta were found: PA add them cheese (p < 0.05) and cream (p < 0.05) whereas no-PA make use of meat (p < 0.05) and tomato sauce (p < 0.05), respectively. The frequency of consumption of traditional Piamontese meals and the role of the mother in the purchase, the elaboration and the serving of the food were similar on both groups. In conclusion, food practices of Piamontese's descendants recall the food culture of their ancestry with some reasonable adaptations to the local context.

Jaggery: an avoidable cause of severe, deadly pediatric burns.

PubMed

Light, T D; Latenser, B A; Heinle, J A; Stolpen, M S; Quinn, K A; Ravindran, V; Chacko, J

2009-05-01

Jaggery is the non-industrial refinement of sugar cane into a sugar product. Sugar cane cultivation, harvest and refinement are central aspects of rural Indian life. We present a retrospective review of pediatric burns at a single institution in Southern India, drawing special attention to scald burns incurred when young children fall into the cauldron of boiling jaggery. Descriptive statistics comparing children burned by jaggery and children burned by other mechanisms were performed. Multivariable logistic regression including burn size and mechanism of burn (jaggery and non-jaggery) was performed to determine the increased risk of death when burned by jaggery. Children burned by jaggery immersions are older, more likely male, and have larger burns. They have longer hospital stays, more operations, and are more likely to die. When controlling for age, gender, size of burn, and mechanism, jaggery exposure was associated with a higher mortality. Jaggery burns are deadly, devastating burns which could be prevented. While jaggery and sugar cane production can lead to economic independence for rural Indian villages, the cost it exacts from burns and death to the youngest and most vulnerable children must be addressed and prevented.

Human Exposure to Anaplasma phagocytophilum in Two Cities of Northwestern Morocco.

PubMed

Elhamiani Khatat, Sarah; Sahibi, Hamid; Hing, Mony; Alaoui Moustain, Ismail; El Amri, Hamid; Benajiba, Mohammed; Kachani, Malika; Duchateau, Luc; Daminet, Sylvie

2016-01-01

Anaplasma phagocytophilum is an emerging tick-borne zoonosis with extensive increased interest. Epidemiological data are available in several regions of the USA, Europe and Asia in contrast to other parts of the world such as North Africa. Blood samples of 261 healthy individuals divided in two groups i.e., dog handlers and blood donors were analysed. Indirect immunofluorescent assay using a commercial kit was performed to detect specific A. phagocytophilum IgG. Two dilutions were used to assess the prevalence of seroreactive samples. Demographic variables were assessed as potential risk factors using exact logistic regression. Seropositivity rates reached 37% and 27% in dog handlers and 36% and 22% in blood donors. No statistically significant differences were found in the prevalence rates between the two groups. Analysis of risk factors such as gender, age groups, outdoor activities, self-reported previous exposure to ticks, or contact with domestic animals (dogs, cats, ruminants and horses) did not shown any significant difference. A. phagocytophilum exposure was common in both high-risk population and blood donors in Morocco.

Polycystic ovary syndrome and intervening factors in adolescents from 15 to 18 years old.

PubMed

Faria, Franciane Rocha de; Gusmão, Laís Silveira; Faria, Eliane Rodrigues de; Gonçalves, Vivian Siqueira Santos; Cecon, Roberta Stofeles; Franceschini, Sylvia do Carmo Castro; Priore, Silvia Eloiza

2013-01-01

To assess the factors related to the presence of polycystic ovary syndrome (PCOS) in adolescents. This was a cross-sectional study, with female adolescents from 15 to 18 years old, divided into: group 1 (with a medical diagnosis of PCOS) and group 2 (not diagnosed with PCOS). The height-for-age index and the body mass index were used for classifying the nutritional status, and a semi-structured questionnaire was applied. The Mann-Whitney test, Fisher's exact test, Spearman correlation coefficients, and logistic regression were used. This study evaluated 485 adolescents with an average age of 16.3 ± 0.9 years old. The prevalence of PCOS was 6.2%. No difference was found between the groups regarding anthropometric parameters and period of contraceptive use; however, there were differences regarding the age at menarche (p < 0.004). Older age at menarche was a protection factor against the syndrome. An association was found between younger age at menarche and the development of the PCOS in adolescents. Copyright © 2013 Elsevier Editora Ltda. All rights reserved.

Human Exposure to Anaplasma phagocytophilum in Two Cities of Northwestern Morocco

PubMed Central

Elhamiani Khatat, Sarah; Sahibi, Hamid; Hing, Mony; Alaoui Moustain, Ismail; El Amri, Hamid; Benajiba, Mohammed; Kachani, Malika; Duchateau, Luc; Daminet, Sylvie

2016-01-01

Anaplasma phagocytophilum is an emerging tick-borne zoonosis with extensive increased interest. Epidemiological data are available in several regions of the USA, Europe and Asia in contrast to other parts of the world such as North Africa. Blood samples of 261 healthy individuals divided in two groups i.e., dog handlers and blood donors were analysed. Indirect immunofluorescent assay using a commercial kit was performed to detect specific A. phagocytophilum IgG. Two dilutions were used to assess the prevalence of seroreactive samples. Demographic variables were assessed as potential risk factors using exact logistic regression. Seropositivity rates reached 37% and 27% in dog handlers and 36% and 22% in blood donors. No statistically significant differences were found in the prevalence rates between the two groups. Analysis of risk factors such as gender, age groups, outdoor activities, self-reported previous exposure to ticks, or contact with domestic animals (dogs, cats, ruminants and horses) did not shown any significant difference. A. phagocytophilum exposure was common in both high-risk population and blood donors in Morocco. PMID:27532208

Trends in primary and revision anterior cruciate ligament reconstruction among National Basketball Association team physicians.

PubMed

Mall, Nathan A; Abrams, Geoffrey D; Azar, Frederick M; Traina, Steve M; Allen, Answorth A; Parker, Richard; Cole, Brian J

2014-06-01

Anterior cruciate ligament (ACL) tears are common in athletes. Techniques and methods of treatment for these injuries continue to vary among surgeons. Thirty National Basketball Association (NBA) team physicians were surveyed during the NBA Pre-Draft Combine. Survey questions involved current and previous practice methods of primary and revision ACL reconstruction, including technique, graft choice, rehabilitation, and treatment of combined ACL and medial collateral ligament injuries. Descriptive parametric statistics, Fisher exact test, and logistic regression were used, and significance was set at α = 0.05. All 30 team physicians completed the survey. Eighty-seven percent indicated they use autograft (81% bone-patellar tendon-bone) for primary ACL reconstruction in NBA athletes, and 43% indicated they use autograft for revision cases. Fourteen surgeons (47%) indicated they use an anteromedial portal (AMP) for femoral tunnel drilling, whereas 5 years earlier only 4 (13%) used this technique. There was a significant (P = .009) positive correlation between fewer years in practice and AMP use. NBA team physicians' use of an AMP for femoral tunnel drilling has increased over the past 5 years.

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.

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.

Finite-sample and asymptotic sign-based tests for parameters of non-linear quantile regression with Markov noise

NASA Astrophysics Data System (ADS)

Sirenko, M. A.; Tarasenko, P. F.; Pushkarev, M. I.

2017-01-01

One of the most noticeable features of sign-based statistical procedures is an opportunity to build an exact test for simple hypothesis testing of parameters in a regression model. In this article, we expanded a sing-based approach to the nonlinear case with dependent noise. The examined model is a multi-quantile regression, which makes it possible to test hypothesis not only of regression parameters, but of noise parameters as well.

United States Air Force Computer-Aided Acquisition & Logistics Support (CALS): CAD/CAM/CAE Current and Future Environment (1988 - 1998). Version 3.0

DOT National Transportation Integrated Search

1988-11-01

The diffusion and adoption of new technologies across national, sectoral, and : organizational boundaries has been a topic of considerable research. While the : exact transfer mechanism remains a matter of hypothesis, it seems clear that the : direct...

Predictors of timing of pregnancy discovery.

PubMed

McCarthy, Molly; Upadhyay, Ushma; Biggs, M Antonia; Anthony, Renaisa; Holl, Jennifer; Roberts, Sarah Cm

2018-04-01

Earlier pregnancy discovery is important in the context of prenatal and abortion care. We evaluated characteristics associated with later pregnancy discovery among women seeking abortion care. Data come from a survey of women seeking abortion care at four family planning facilities in Utah. The participants completed a survey during the state-mandated abortion information visit they are required to complete prior to having an abortion. The outcome in this study was pregnancy discovery before versus after 6 weeks since respondents' last menstrual period (LMP). We used logistic regression to estimate the relationship between sociodemographic and health-related independent variables of interest and pregnancy discovery before versus after 6 weeks. Among the 458 women in the sample, 28% discovered their pregnancy later than 6 weeks since LMP. Most (n=366, 80%) knew the exact date of their LMP and a significant minority estimated it (n=92, 20%). Those who estimated the date of their LMP had higher odds of later pregnancy discovery than those who knew the exact date (adjusted odds ratio (aOR)=1.81[1.07-3.07]). Those who used illicit drugs weekly, daily, or almost daily had higher odds of later pregnancy discovery (aOR=6.33[2.44, 16.40]). Women who did not track their menstrual periods and those who frequently used drugs had higher odds of discovering their pregnancies later. Women who estimated the date of their LMP and who frequently used drugs may benefit from strategies to help them recognize their pregnancies earlier and link them to care when they discover their pregnancies later. Copyright © 2017 Elsevier Inc. All rights reserved.

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).

Unconditional or Conditional Logistic Regression Model for Age-Matched Case-Control Data?

PubMed

Kuo, Chia-Ling; Duan, Yinghui; Grady, James

2018-01-01

Matching on demographic variables is commonly used in case-control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case-control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case-control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls.

Unconditional or Conditional Logistic Regression Model for Age-Matched Case–Control Data?

PubMed Central

Kuo, Chia-Ling; Duan, Yinghui; Grady, James

2018-01-01

Matching on demographic variables is commonly used in case–control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case–control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case–control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls. PMID:29552553

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Product unit neural network models for predicting the growth limits of Listeria monocytogenes.

PubMed

Valero, A; Hervás, C; García-Gimeno, R M; Zurera, G

2007-08-01

A new approach to predict the growth/no growth interface of Listeria monocytogenes as a function of storage temperature, pH, citric acid (CA) and ascorbic acid (AA) is presented. A linear logistic regression procedure was performed and a non-linear model was obtained by adding new variables by means of a Neural Network model based on Product Units (PUNN). The classification efficiency of the training data set and the generalization data of the new Logistic Regression PUNN model (LRPU) were compared with Linear Logistic Regression (LLR) and Polynomial Logistic Regression (PLR) models. 92% of the total cases from the LRPU model were correctly classified, an improvement on the percentage obtained using the PLR model (90%) and significantly higher than the results obtained with the LLR model, 80%. On the other hand predictions of LRPU were closer to data observed which permits to design proper formulations in minimally processed foods. This novel methodology can be applied to predictive microbiology for describing growth/no growth interface of food-borne microorganisms such as L. monocytogenes. The optimal balance is trying to find models with an acceptable interpretation capacity and with good ability to fit the data on the boundaries of variable range. The results obtained conclude that these kinds of models might well be very a valuable tool for mathematical modeling.

Analysis of a database to predict the result of allergy testing in vivo in patients with chronic nasal symptoms.

PubMed

Lacagnina, Valerio; Leto-Barone, Maria S; La Piana, Simona; Seidita, Aurelio; Pingitore, Giuseppe; Di Lorenzo, Gabriele

2014-01-01

This article uses the logistic regression model for diagnostic decision making in patients with chronic nasal symptoms. We studied the ability of the logistic regression model, obtained by the evaluation of a database, to detect patients with positive allergy skin-prick test (SPT) and patients with negative SPT. The model developed was validated using the data set obtained from another medical institution. The analysis was performed using a database obtained from a questionnaire administered to the patients with nasal symptoms containing personal data, clinical data, and results of allergy testing (SPT). All variables found to be significantly different between patients with positive and negative SPT (p < 0.05) were selected for the logistic regression models and were analyzed with backward stepwise logistic regression, evaluated with area under the curve of the receiver operating characteristic curve. A second set of patients from another institution was used to prove the model. The accuracy of the model in identifying, over the second set, both patients whose SPT will be positive and negative was high. The model detected 96% of patients with nasal symptoms and positive SPT and classified 94% of those with negative SPT. This study is preliminary to the creation of a software that could help the primary care doctors in a diagnostic decision making process (need of allergy testing) in patients complaining of chronic nasal symptoms.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Prediction of siRNA potency using sparse logistic regression.

PubMed

Hu, Wei; Hu, John

2014-06-01

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

Regression sampling: some results for resource managers and researchers

Treesearch

William G. O' Regan; Robert W. Boyd

1974-01-01

Regression sampling is widely used in natural resources management and research to estimate quantities of resources per unit area. This note brings together results found in the statistical literature in the application of this sampling technique. Conditional and unconditional estimators are listed and for each estimator, exact variances and unbiased estimators for the...

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.

Caudal Regression Syndrome with Partial Agenesis of the Corpus callosum and Partial Lobar Holoprosencephaly

PubMed Central

Hashami, Hilal Al; Bataclan, Maria F; Mathew, Mariam; Krishnan, Lalitha

2010-01-01

Caudal regression syndrome is a rare fetal condition of diabetic pregnancy. Although the exact mechanism is not known, hyperglycaemia during embryogenesis seems to act as a teratogen. Independently, caudal regression syndrome (CRS), agenesis of the corpus callosum (ACC) and partial lobar holoprosencephaly (HPE) have been reported in infants of diabetic mothers. To our knowledge, a combination of all these three conditions has not been reported so far. PMID:21509087

Caudal Regression Syndrome with Partial Agenesis of the Corpus callosum and Partial Lobar Holoprosencephaly: Case report.

PubMed

Hashami, Hilal Al; Bataclan, Maria F; Mathew, Mariam; Krishnan, Lalitha

2010-04-01

Caudal regression syndrome is a rare fetal condition of diabetic pregnancy. Although the exact mechanism is not known, hyperglycaemia during embryogenesis seems to act as a teratogen. Independently, caudal regression syndrome (CRS), agenesis of the corpus callosum (ACC) and partial lobar holoprosencephaly (HPE) have been reported in infants of diabetic mothers. To our knowledge, a combination of all these three conditions has not been reported so far.

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.

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…

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.

Calibrated Peer Review for Interpreting Linear Regression Parameters: Results from a Graduate Course

ERIC Educational Resources Information Center

Enders, Felicity B.; Jenkins, Sarah; Hoverman, Verna

2010-01-01

Biostatistics is traditionally a difficult subject for students to learn. While the mathematical aspects are challenging, it can also be demanding for students to learn the exact language to use to correctly interpret statistical results. In particular, correctly interpreting the parameters from linear regression is both a vital tool and a…

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

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

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

Health Care Transition Services for Youth With Autism Spectrum Disorders: Perspectives of Caregivers.

PubMed

Kuhlthau, Karen A; Delahaye, Jennifer; Erickson-Warfield, Marji; Shui, Amy; Crossman, Morgan; van der Weerd, Emma

2016-02-01

This paper seeks to describe the experience of youth with autism spectrum disorder (ASD) in making the health care transition (HCT) to adult care. We surveyed 183 parents and guardians of youth with ASD, assessing the extent to which youth and families experienced and desired HCT services, their satisfaction with services, and obstacles to transition. Descriptive statistics were used to examine HCT measures and Fisher's exact and t tests assessed whether demographic or health measures were associated with service receipt. Any measures with a P value <.05 were included in a logistic regression model, with service receipt as the dependent variable. The receipt of transition services was low overall, with rates for individual services ranging from 3% to 33% and only 60% of the sample receiving any transition service. Despite these low rates, a majority of respondents reported wanting services (73.3%-91.6%), and satisfaction for received services was high (89%-100%). Regression analyses showed depression to be the only variable significantly associated with service receipt. Youth who were identified by their caregivers as having depression experienced a higher rate of transition service receipt than those not identified as having depression. Findings suggest that there is a great need to address the provision of HCT services for youth with ASD. Although families who received HCT services were generally satisfied, overall rates of service receipt were quite low, and those who were not provided with services generally desired them. Copyright © 2016 by the American Academy of Pediatrics.

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.

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.

Can Predictive Modeling Identify Head and Neck Oncology Patients at Risk for Readmission?

PubMed

Manning, Amy M; Casper, Keith A; Peter, Kay St; Wilson, Keith M; Mark, Jonathan R; Collar, Ryan M

2018-05-01

Objective Unplanned readmission within 30 days is a contributor to health care costs in the United States. The use of predictive modeling during hospitalization to identify patients at risk for readmission offers a novel approach to quality improvement and cost reduction. Study Design Two-phase study including retrospective analysis of prospectively collected data followed by prospective longitudinal study. Setting Tertiary academic medical center. Subjects and Methods Prospectively collected data for patients undergoing surgical treatment for head and neck cancer from January 2013 to January 2015 were used to build predictive models for readmission within 30 days of discharge using logistic regression, classification and regression tree (CART) analysis, and random forests. One model (logistic regression) was then placed prospectively into the discharge workflow from March 2016 to May 2016 to determine the model's ability to predict which patients would be readmitted within 30 days. Results In total, 174 admissions had descriptive data. Thirty-two were excluded due to incomplete data. Logistic regression, CART, and random forest predictive models were constructed using the remaining 142 admissions. When applied to 106 consecutive prospective head and neck oncology patients at the time of discharge, the logistic regression model predicted readmissions with a specificity of 94%, a sensitivity of 47%, a negative predictive value of 90%, and a positive predictive value of 62% (odds ratio, 14.9; 95% confidence interval, 4.02-55.45). Conclusion Prospectively collected head and neck cancer databases can be used to develop predictive models that can accurately predict which patients will be readmitted. This offers valuable support for quality improvement initiatives and readmission-related cost reduction in head and neck cancer care.

Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598

Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.

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.

Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models

PubMed Central

2014-01-01

Background Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented. Methods Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions. Results Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples. Conclusions Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose. PMID:25196829

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.

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.

Determine the optimal carrier selection for a logistics network based on multi-commodity reliability criterion

NASA Astrophysics Data System (ADS)

Lin, Yi-Kuei; Yeh, Cheng-Ta

2013-05-01

From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.

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.

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).

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.

Clinical features and risk factors of acute hepatitis E with severe jaundice.

PubMed

Xu, Bin; Yu, Hai-Bin; Hui, Wei; He, Jia-Li; Wei, Lin-Lin; Wang, Zheng; Guo, Xin-Hui

2012-12-28

To compares the clinical features of patients infected with hepatitis E virus (HEV) with or without severe jaundice. In addition, the risk factors for HEV infection with severe jaundice were investigated. We enrolled 235 patients with HEV into a cross-sectional study using multi-stage sampling to select the study group. Patients with possible acute hepatitis E showing elevated liver enzyme levels were screened for HEV infection using serologic and molecular tools.HEV infection was documented by HEV antibodies and by the detection of HEV-RNA in serum. We used χ(2) analysis, Fisher's exact test, and Student's t test where appropriate in this study. Significant predictors in the univariate analysis were then included in a forward, stepwise multiple logistic regression model. No significant differences in symptoms, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, or hepatitis B virus surface antigen between the two groups were observed. HEV infected patients with severe jaundice had significantly lower peak serum levels of γ-glutamyl-transpeptidase (GGT) (median: 170.31 U/L vs 237.96 U/L, P = 0.007), significantly lower ALB levels (33.84 g/L vs 36.89 g/L, P = 0.000), significantly lower acetylcholine esterase (CHE) levels (4500.93 U/L vs 5815.28 U/L, P = 0.000) and significantly higher total bile acid (TBA) levels (275.56 μmol/L vs 147.03 μmol/L, P = 0.000) than those without severe jaundice. The median of the lowest point time tended to be lower in patients with severe jaundice (81.64% vs 96.12%, P = 0.000). HEV infected patients with severe jaundice had a significantly higher viral load (median: 134 vs 112, P = 0.025) than those without severe jaundice. HEV infected patients with severe jaundice showed a trend toward longer median hospital stay (38.17 d vs 18.36 d, P = 0.073). Multivariate logistic regression indicated that there were significant differences in age, sex, viral load, GGT, albumin, TBA, CHE, prothrombin index, alcohol overconsumption, and duration of admission between patients infected with acute hepatitis E with and without severe jaundice. Acute hepatitis E patients may naturally present with severe jaundice.

Care-seeking patterns among families that experienced under-five child mortality in rural Rwanda.

PubMed

Kagabo, Daniel M; Kirk, Catherine M; Bakundukize, Benjamin; Hedt-Gauthier, Bethany L; Gupta, Neil; Hirschhorn, Lisa R; Ingabire, Willy C; Rouleau, Dominique; Nkikabahizi, Fulgence; Mugeni, Catherine; Sayinzoga, Felix; Amoroso, Cheryl L

2018-01-01

Over half of under-five deaths occur in sub-Saharan Africa and appropriate, timely, quality care is critical for saving children's lives. This study describes the context surrounding children's deaths from the time the illness was first noticed, through the care-seeking patterns leading up to the child's death, and identifies factors associated with care-seeking for these children in rural Rwanda. Secondary analysis of a verbal and social autopsy study of caregivers who reported the death of a child between March 2013 to February 2014 that occurred after discharge from the child's birth facility in southern Kayonza and Kirehe districts in Rwanda. Bivariate analyses using Fisher's exact tests were conducted to identify child, caregiver, and household factors associated with care-seeking from the formal health system (i.e., community health worker or health facility). Factors significant at α = 0.10 significance level were considered for backwards stepwise multivariate logistic regression, stopping when remaining factors were significantly associated with care-seeking at α = 0.05 significance level. Among the 516 eligible deaths among children under-five, 22.7% (n = 117) did not seek care from the health system. For those who did, the most common first point of contact was community health workers (45.8%). In multivariate logistic regression, higher maternal education (OR = 3.36, 95% CI: 1.89, 5.98), having diarrhea (OR = 4.21, 95%CI: 1.95, 9.07) or fever (OR = 2.03, 95%CI: 1.11, 3.72), full household insurance coverage (3.48, 95%CI: 1.79, 6.76), and longer duration of illness (OR = 22.19, 95%CI: 8.88, 55.48) were significantly associated with formal care-seeking. Interventions such as community health workers and insurance promote access to care, however a gap remains as many children had no contact with the health system prior to death and those who sought formal care still died. Further efforts are needed to respond to urgent cases in communities and further understand remaining barriers to accessing appropriate, quality care.

Computed tomography identifies patients at high risk for stroke after transient ischemic attack/nondisabling stroke: prospective, multicenter cohort study.

PubMed

Wasserman, Jason K; Perry, Jeffrey J; Sivilotti, Marco L A; Sutherland, Jane; Worster, Andrew; Émond, Marcel; Jin, Albert Y; Oczkowski, Wieslaw J; Sahlas, Demetrios J; Murray, Heather; MacKey, Ariane; Verreault, Steve; Wells, George A; Dowlatshahi, Dar; Stotts, Grant; Stiell, Ian G; Sharma, Mukul

2015-01-01

Ischemia on computed tomography (CT) is associated with subsequent stroke after transient ischemic attack. This study assessed CT findings of acute ischemia, chronic ischemia, or microangiopathy for predicting subsequent stroke after transient ischemic attack. This prospective cohort study enrolled patients with transient ischemic attack or nondisabling stroke that had CT scanning within 24 hours. Primary outcome was subsequent stroke within 90 days. Secondary outcomes were stroke at ≤2 or >2 days. CT findings were classified as ischemia present or absent and acute or chronic or microangiopathy. Analysis used Fisher exact test and multivariate logistic regression. A total of 2028 patients were included; 814 had ischemic changes on CT. Subsequent stroke rate was 3.4% at 90 days and 1.5% at ≤2 days. Stroke risk was greater if baseline CT showed acute ischemia alone (10.6%; P=0.002), acute+chronic ischemia (17.4%; P=0.007), acute ischemia+microangiopathy (17.6%; P=0.019), or acute+chronic ischemia+microangiopathy (25.0%; P=0.029). Logistic regression found acute ischemia alone (odds ratio [OR], 2.61; 95% confidence interval [CI[, 1.22-5.57), acute+chronic ischemia (OR, 5.35; 95% CI, 1.71-16.70), acute ischemia+microangiopathy (OR, 4.90; 95% CI, 1.33-18.07), or acute+chronic ischemia+microangiopathy (OR, 8.04; 95% CI, 1.52-42.63) was associated with a greater risk at 90 days, whereas acute+chronic ischemia (OR, 10.78; 95% CI, 2.93-36.68), acute ischemia+microangiopathy (OR, 8.90; 95% CI, 1.90-41.60), and acute+chronic ischemia+microangiopathy (OR, 23.66; 95% CI, 4.34-129.03) had greater risk at ≤2 days. Only acute ischemia (OR, 2.70; 95% CI, 1.01-7.18; P=0.047) was associated with a greater risk at >2 days. In patients with transient ischemic attack/nondisabling stroke, CT evidence of acute ischemia alone or acute ischemia with chronic ischemia or microangiopathy was associated with increased subsequent stroke risk within 90 days. © 2014 American Heart Association, Inc.

Seroepidemiology and associated risk factors of Toxoplasma gondii in sheep and goats in Southwestern Ethiopia.

PubMed

Tegegne, Dechassa; Kelifa, Amin; Abdurahaman, Mukarim; Yohannes, Moti

2016-12-09

T.gondii is a global zoonotic disease and is considered as the most neglected tropical disease in sub-Saharan countries. The exact seroepidemiological distribution and risk factors for the infection of food animals and humans in Ethiopia was less studied although, such studies are important. The objective of the current study was to determine the seroprevalence and potential risk factors of T. gondii infection in sheep and goats in Southwestern Ethiopia. Cross sectional study was conducted from November 2014 to March 2015 in South west Ethiopia in four selected districts of Jimma zone (n = 368). Slide agglutination test (Toxo-latex) was used to detect anti-T.gondii antibodies. Logistic regression was used to determine potential risk factors. An overall seroprevalence of 57.60% (212/368; 95% CI: 52.55-62.6) was detected. 58.18% (148/252; 95% CI: 52.75-64.88) and 55.18% (64/116; 95% CI: 46.13-64.23) sero prevalence was found in sheep and goats respectively. Multivariable logistic regression analysis showed that the risk of T. gondii infection was significantly higher in adult sheep and goats [(sheep: Odds Ratio (OR) = 2.5, confidence interval (CI): 1.19-5.23; p = 0.015), (goats: OR = 3.9, confidence interval (CI):1.64-9.41: p = 0.002)] than in young sheep and goats, in female [(sheep: OR = 1.93, CI: 1.11-3.36, p = 0.018, (goats: OR = 2.9, CI: 121-6.93, p = 0.002)] than in males sheep and goats, in Highland [(sheep: OR = 4.57, CI: 1.75-12.66, P = 0.000, (goats: OR = 4.4, CI: 1.75-13.66, p = 0.004)] than sheep and goats from lowland. This study indicates that seroprevalence of latent toxoplasmosis in small ruminants is high, therefore, it is decidedly indispensable to minimize risk factors exposing to the infection like consumption of raw meat as source of infection for humans.

Factors Influencing Migraine Recurrence After Infusion and Inpatient Migraine Treatment in Children and Adolescents.

PubMed

Cobb-Pitstick, Katherine M; Hershey, Andrew D; O'Brien, Hope L; Kabbouche, Marielle A; LeCates, Susan; White, Shannon; Vaughn, Polly; Manning, Paula; Segers, Ann; Bush, Judith; Horn, Paul S; Kacperski, Joanne

2015-01-01

To evaluate factors that influence migraine recurrence after outpatient infusion or inpatient treatment for intractable migraine. Recurrence of migraine after acute treatment in an infusion center or an inpatient setting is not well documented in children and adolescents. Given the multifactorial pathogenesis of migraines, multiple factors may influence migraine recurrence. It has been reported that treatment with steroids may reduce the risk of migraine recurrence. The efficacy of steroids as a therapeutic adjunct has not been established. Studies in the adult population have yielded conflicting results. This study is a retrospective chart review of patients presenting for treatment of an intractable migraine to the outpatient infusion unit or inpatient unit at Cincinnati Children's Hospital Medical Center (CCHMC). Data collected included: age, gender, location of treatment (outpatient, inpatient), migraine duration, diagnosis, severity, the addition of steroids to treatment protocols, and recurrence of migraine at 48 and 72 hours after discharge. Data were analyzed using Fisher's exact tests, logistic regression with backward elimination for variable selection, and least squares means slicing with associated odds ratios. Charts from 207 pediatric patients were analyzed. Using logistic regression analysis: location, gender, diagnosis, and age were all found to be significant predictors of migraine recurrence (P < .05). Patients treated in the inpatient setting were significantly less likely to experience recurrence compared to patients treated in an outpatient infusion unit (OR = 0.32; 95% CI 0.17-0.61, P = .0002). Male patients with a diagnosis of episodic migraine were significantly less likely to experience recurrence than male patients with chronic migraine (OR 0.17; 95% CI 0.04-0.73; P = .0074). The inclusion of steroids in this study population showed no significant reduction in migraine recurrence. The probability of recurrence decreased with age for episodic migraine patients, while the probability increased with age for chronic migraine patients. Recurrence is an important consideration when treating intractable migraines. Age, gender, diagnosis, and location of treatment correlate with migraine recurrence, but the inclusion of steroids does not. Considering these factors in the management of migraines may improve the outcome of these patients and reduce the risk of recurrence. © 2015 American Headache Society.

Risk perception and access to environmental information in four areas in Italy affected by natural or anthropogenic pollution.

PubMed

Coi, A; Minichilli, F; Bustaffa, E; Carone, S; Santoro, M; Bianchi, F; Cori, L

2016-10-01

A human biomonitoring (HBM) survey in four areas affected by natural or anthropogenic arsenic pollution was conducted in Italy within the framework of the SEpiAs project. A questionnaire, including the exploration of risk perception (RP) regarding environmental hazards and access to and trust in information, was administered to 282 subjects stratified by area, gender and age. The survey was designed to investigate how populations living in polluted areas could adopt prevention-oriented habits, fostered by the awareness of existing risks and, in addition, how increased knowledge of RP and information flows could support researchers in identifying recommendations, and presenting and disseminating HBM results. This study characterizes the four areas in terms of RP and access to and trust in environmental information, and provides insights into the influence of RP and environmental information on food consumption. For the data analysis, a combined random forest (RF) and logistic regression approach was carried out. RF was applied to the variables derived from the questionnaire in order to identify the most important in terms of the aims defined. Associations were then tested using Fisher's exact test and assessed with logistic regression in order to adjust for confounders. Results showed that the perception of and personal exposure to atmospheric and water pollution, hazardous industries and waste, hazardous material transportation and waste was higher in geographical areas characterized by anthropogenic pollution. Citizens living in industrial areas appeared to be aware of environmental risks and had more confidence in environmental non-governmental organizations (NGOs) than in public authorities. In addition, they reported an insufficient circulation of information. Concerning the influence of RP and environmental information on food consumption, a high perception of personal exposure to atmospheric pollution and hazardous industries was associated with a lower consumption of local fish. In conclusion, different RPs and information flow patterns were observed in areas with arsenic of natural origin or in industrial contexts. These findings may be useful for targeted risk communication plans in support of risk-management strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mini vs standard percutaneous nephrolithotomy for renal stones: a comparative study.

PubMed

ElSheemy, Mohammed S; Elmarakbi, Akram A; Hytham, Mohammed; Ibrahim, Hamdy; Khadgi, Sanjay; Al-Kandari, Ahmed M

2018-03-16

To compare the outcome of mini-percutaneous nephrolithotomy (Mini-PNL) versus standard-PNL for renal stones. Retrospective study was performed between March 2010 and May 2013 for patients treated by Mini-PNL or standard-PNL through 18 and 30 Fr tracts, respectively, using pneumatic lithotripsy. Semirigid ureteroscope (8.5/11.5 Fr) was used for Mini-PNL and 24 Fr nephroscope for standard-PNL. Both groups were compared in stone free rate(SFR), complications and operative time using Student-t, Mann-Whitney, Chi square or Fisher's exact tests as appropriate in addition to logistic regression analysis. P < 0.05 was considered statistically significant. Mini-PNL (378) and standard-PNL (151) were nearly comparable in patients and stones criteria including stone burden (3.77 ± 2.21 vs 3.77 ± 2.43 cm 2 ; respectively). There was no significant difference in number of tracts or supracostal puncture. Mini-PNL had longer operative time (68.6 ± 29.09 vs 60.49 ± 11.38 min; p = 0.434), significantly shorter hospital stay (2.43 ± 1.46 vs 4.29 ± 1.28 days) and significantly higher rate of tubeless PNL (75.1 vs 4.6%). Complications were significantly higher in standard-PNL (7.9 vs 20.5%; p < 0.001). SFR was significantly lower in Mini-PNL (89.9 vs 96%; p = 0.022). This significant difference was found with multiple stones and large stone burden (> 2 cm 2 ), but the SFR was comparable between both groups with single stone or stone burden ≤ 2 cm. Logistic regression analysis confirmed significantly higher complications and SFR with standard-PNL but with significantly shorter operative time. Mini-PNL has significantly lower SFR when compared to standard-PNL (but clinically comparable) with markedly reduced complications and hospital stay. Most of cases can be performed tubeless. The significant difference in SFR was found with multiple stones or large stone burden (> 2 cm 2 ), but not with single stones or stone burden ≤ 2 cm 2 .

Empiric antibiotic therapy in urinary tract infection in patients with risk factors for antibiotic resistance in a German emergency department.

PubMed

Bischoff, Sebastian; Walter, Thomas; Gerigk, Marlis; Ebert, Matthias; Vogelmann, Roger

2018-01-26

The aim of this study was to identify clinical risk factors for antimicrobial resistances and multidrug resistance (MDR) in urinary tract infections (UTI) in an emergency department in order to improve empirical therapy. UTI cases from an emergency department (ED) during January 2013 and June 2015 were analyzed. Differences between patients with and without resistances towards Ciprofloxacin, Piperacillin with Tazobactam (Pip/taz), Gentamicin, Cefuroxime, Cefpodoxime and Ceftazidime were analyzed with Fisher's exact tests. Results were used to identify risk factors with logistic regression modelling. Susceptibility rates were analyzed in relation to risk factors. One hundred thirty-seven of four hundred sixty-nine patients who met the criteria of UTI had a positive urine culture. An MDR pathogen was found in 36.5% of these. Overall susceptibility was less than 85% for standard antimicrobial agents. Logistic regression identified residence in nursing homes, male gender, hospitalization within the last 30 days, renal transplantation, antibiotic treatment within the last 30 days, indwelling urinary catheter and recurrent UTI as risk factors for MDR or any of these resistances. For patients with no risk factors Ciprofloxacin had 90%, Pip/taz 88%, Gentamicin 95%, Cefuroxime 98%, Cefpodoxime 98% and Ceftazidime 100% susceptibility. For patients with 1 risk factor Ciprofloxacin had 80%, Pip/taz 80%, Gentamicin 88%, Cefuroxime 78%, Cefpodoxime 78% and Ceftazidime 83% susceptibility. For 2 or more risk factors Ciprofloxacin drops its susceptibility to 52%, Cefuroxime to 54% and Cefpodoxime to 61%. Pip/taz, Gentamicin and Ceftazidime remain at 75% and 77%, respectively. We identified several risk factors for resistances and MDR in UTI. Susceptibility towards antimicrobials depends on these risk factors. With no risk factor cephalosporins seem to be the best choice for empiric therapy, but in patients with risk factors the beta-lactam penicillin Piperacillin with Tazobactam is an equal or better choice compared to fluoroquinolones, cephalosporins or gentamicin. This study highlights the importance of monitoring local resistance rates and its risk factors in order to improve empiric therapy in a local environment.

Predictors of immunological failure of antiretroviral therapy among HIV infected patients in Ethiopia: a matched case-control study.

PubMed

Teshome, Wondu; Asefa, Anteneh; Assefa, Anteneh

2014-01-01

In resource constrained settings, immunological assessment through CD4 count is used to assess response to first line Highly Active Antiretroviral Therapy (HAART). In this study, we aim to investigate factors associated with immunological treatment failure. A matched case-control study design was used. Cases were subjects who already experienced immunological treatment failure and controls were those without immunological failure after an exactly or approximately equivalent duration of first line treatment with cases. Data were analyzed using SPSS v16.0. Conditional logistic regression was carried out. A total of 134 cases and 134 controls were included in the study. At baseline, the mean age ± 1 SD of cases was 37.5 ± 9.7 years whereas it was 36.9 ± 9.2 years among controls. The median baseline CD4 counts of cases and controls were 121.0 cells/µl (IQR: 47-183 cells/µl) and 122.0 cells/µl (IQR: 80.0-189.8 cells/µl), respectively. The median rate of CD4 cells increase was comparable for the two groups in the first six months of commencing HAART (P = 0.442). However, the median rate of CD4 increase was significantly different for the two groups in the next 6 months period (M6 to M12). The rate of increment was 8.8 (IQR: 0.5, 14.6) and 1.8 (IQR: 8.8, 11.3) cells/µl/month for controls and cases, respectively (Mann-Whitney U test, P = 0.003). In conditional logistic regressions grouped baseline CD4 count (P = 0.028), old age group and higher educational status (P<0.001) were significant predictors of immunological treatment failure. Subjects with immunological treatment failure have an optimal rate of immunological recovery in the first 6 months of treatment with first line HAART, but relative to the non-failing group the rate declines at a later period, notably between 6 and 12 months. Low baseline CD4 count, old age and higher educational status were associated with immunological treatment failure.

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.

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

Comparison of the characteristics of suicide attempters with major depressive disorder and those with no psychiatric diagnosis in emergency departments of general hospitals in China.

PubMed

Wei, Shengnan; Li, Haiyan; Hou, Jinglin; Chen, Wei; Chen, Xu; Qin, Xiaoxia

2017-01-01

Major depressive disorder (MDD) is a known major risk factor for suicide due to the high suicide mortality. However, studies comparing the characteristics of suicide attempters with major depressive disorder and those with no psychiatric diagnosis in China are very limited. This study examined and compared the sociodemographic and psychological characteristics of suicide attempters with MDD and those with no psychiatric diagnosis in emergency departments of general hospitals to better understand the risk factors for suicide attempts in China. All subjects were enrolled in the study between June 2007 and January 2008. A total of 127 suicide attempters-54 with MDD and 73 with no psychiatric diagnosis-were enrolled. The sociodemographic and clinical characteristics were compared between two groups using the statistical analysis performed using frequency distribution, Student's t test, Chi-square test, and Fisher's exact test and a logistic regression model. Suicide attempters with MDD were more likely to be more depressive, older, divorced or separated, unemployed, and living alone, and more likely to write a suicide note, have suicide ideation, and be motivated by reducing pain and burden. Suicide attempters with no psychiatric diagnosis were more likely to be younger and more impulsive, have self-rescue, and be motivated by threatening or taking revenge on others. Multivariate logistic regression analysis identified the following independent predictors of suicide attempts in individuals with MDD: a lower score on the quality of life scale, more years of education, and suicide ideation. The present study found both similarities and differences in the sociodemographic and clinical characteristics of suicide attempters with MDD and those with no psychiatric diagnosis in the emergency departments of general hospitals in China. These findings will help us to recognize the characteristics of suicide attempters in both groups and develop specific interventions for the two types of suicide attempters to prevent future suicide in China. For example, the suicide attempters with MDD in the emergency departments must be advised to the psychological clinic.

Traumatic injuries among adult obese patients in southern Taiwan: a cross-sectional study based on a trauma registry system.

PubMed

Chuang, Jung-Fang; Rau, Cheng-Shyuan; Kuo, Pao-Jen; Chen, Yi-Chun; Hsu, Shiun-Yuan; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2016-03-18

The adverse impact of obesity has been extensively studied in the general population; however, the added risk of obesity on trauma-related mortality remains controversial. This study investigated and compared mortality as well injury patterns and length of stay (LOS) in obese and normal-weight patients hospitalized for trauma in the hospital and intensive care unit (ICU) of a Level I trauma center in southern Taiwan. Detailed data of 880 obese adult patients with body mass index (BMI) ≥ 30 kg/m(2) and 5391 normal-weight adult patients (25 > BMI ≥ 18.5 kg/m(2)) who had sustained a trauma injury between January 1, 2009 and December 31, 2013 were retrieved from the Trauma Registry System. Pearson's chi-squared, Fisher's exact, and independent Student's t-tests were used to compare differences between groups. Propensity score matching with logistic regression was used to evaluate the effect of obesity on mortality. In this study, obese patients were more often men, motorcycle riders and pedestrians, and had a lower proportion of alcohol intoxication compared to normal-weight patients. Analysis of Abbreviated Injury Scale scores revealed that obese trauma patients presented with a higher rate of injury to the thorax, but a lower rate of facial injuries than normal-weight patients. No significant differences were found between obese and normal-weight patients regarding Injury Severity Score (ISS), Trauma-Injury Severity Score (TRISS), mortality, the proportion of patients admitted to the ICU, or LOS in ICU. After propensity score matching, logistic regression of 66 well-matched pairs did not show a significant influence of obesity on mortality (odds ratio: 1.51, 95% confidence interval: 0.54-4.23 p = 0.438). However, significantly longer hospital LOS (10.6 vs. 9.5 days, respectively, p = 0.044) was observed in obese patients than in normal-weight patients, particularly obese patients with pelvic, tibial, or fibular fractures. Compared to normal-weight patients, obese patients presented with different injury characteristics and bodily injury patterns but no difference in mortality.

Suicide Risk in the Hospitalized Elderly in Turkey and Affecting Factors.

PubMed

Avci, Dilek; Selcuk, Kevser Tari; Dogan, Selma

2017-02-01

This study aimed to investigate the suicide risk among the elderly hospitalized and treated because of physical illnesses, and the factors affecting the risk. The study has a cross-sectional design. It was conducted with 459 elderly people hospitalized and treated in a public hospital between May 25, 2015 and December 4, 2015. Data were collected with the Personal Information Form, Suicide Probability Scale and Hospital Anxiety and Depression Scale. For the analysis, descriptive statistics, the chi-square test, Fisher's exact test and logistic regression analysis were used. In the study, 24.0% of the elderly were at high risk for suicide. Suicide risk was even higher among the elderly in the 60-74 age group, living alone, drinking alcohol, perceiving his/her religious beliefs as weak, being treated for cancer, having the diagnosis 11 years or over, having a history of admission to a psychiatry clinic, and being at risk for anxiety and depression. In the study, approximately one out of every four elderly people was at high risk for suicide. Therefore, older people should be assessed for suicide risk and programs targeting to prevent the elderly from committing suicide should be organized. Copyright © 2016 Elsevier Inc. All rights reserved.

[Nutritional status and risk factors for malnutrition in low-income urban elders].

PubMed

Hyun, Hye Sun; Lee, Insook

2014-12-01

The purpose of this study was to evaluate the nutritional status of low-income urban elders by diversified ways, and to analyze the risk factors for malnutrition. The participants in this study were 183 low-income elders registered at a visiting healthcare facility in a public health center. Data were collected using anthropometric measurements, and a questionnaire survey. For data analysis, descriptive statistics, χ²-test, t-test, Fisher's exact test, multiple logistic regression analysis were performed using SPSS 20.0. Regarding the nutritional status of low-income elders as measured by the Mini Nutritional Assessment (MNA), 10.4% of the elders were classified as malnourished; 57.4% as at high risk for malnutrition; and 32.2% as having normal nutrition levels. The main factors affecting malnutrition for low-income elders were loss of appetite (OR=3.34, 95% CI: 1.16~9.56) and difficulties in meal preparation (OR=2.35, 95% CI: 1.13~4.88). In order to effectively improve nutrition in low-income urban elders, it is necessary to develop individual intervention strategies to manage factors that increase the risk of malnutrition and to use systematic approach strategies in local communities in terms of a nutrition support system.

Work accidents and self-esteem of nursing professional in hospital settings.

PubMed

Santos, Sérgio Valverde Marques Dos; Macedo, Flávia Ribeiro Martins; Silva, Luiz Almeida da; Resck, Zelia Marilda Rodrigues; Nogueira, Denismar Alves; Terra, Fábio de Souza

2017-04-20

to analyze the occurrence of work accidents and the self-esteem of nurses in hospitals of a municipality of Minas Gerais. descriptive-analytical and cross-sectional study developed with 393 nursing professionals from three hospitals of a municipality in southern Minas Gerais. The Rosenberg Self-Esteem Scale and a questionnaire to characterize the population and work accidents were used for data collection. Data analysis was performed using Person's chi-squared test, Fisher's exact test, Cronbach's alpha, odds ratio and logistic regression. of the professionals studied, 15% had suffered an accident at work and 70.2% presented high self-esteem. Through the analysis, it was observed that smoking, religious belief and an outstanding event in the career were significantly associated with work accidents. In relation to self-esteem, family income, length of time working in the profession and an outstanding event in the career presented significant associations. factors such as smoking, religious belief, family income, length of time working in the profession and an outstanding event in the career can cause professionals to have accidents and/or cause changes in self-esteem, which can compromise their physical and mental health and their quality of life and work.

Biomechanical and psychosocial risk factors for low back pain at work.

PubMed Central

Kerr, M S; Frank, J W; Shannon, H S; Norman, R W; Wells, R P; Neumann, W P; Bombardier, C

2001-01-01

OBJECTIVES: This study determined whether the physical and psychosocial demands of work are associated with low back pain. METHODS: A case-control approach was used. Case subjects (n = 137) reported a new episode of low back pain to their employer, a large automobile manufacturing complex. Control subjects were randomly selected from the study base as cases accrued (n = 179) or were matched to cases by exact job (n = 65). Individual, clinical, and psychosocial variables were assessed by interview. Physical demands were assessed with direct workplace measurements of subjects at their usual jobs. The analysis used multiple logistic regression adjusted for individual characteristics. RESULTS: Self-reported risk factors included a physically demanding job, a poor workplace social environment, inconsistency between job and education level, better job satisfaction, and better coworker support. Low job control showed a borderline association. Physical-measure risk factors included peak lumbar shear force, peak load handled, and cumulative lumbar disc compression. Low body mass index and prior low back pain compensation claims were the only significant individual characteristics. CONCLUSIONS: This study identified specific physical and psychosocial demands of work as independent risk factors for low back pain. PMID:11441733

Factors related to the nursing student-patient relationship: the students' perspective.

PubMed

Suikkala, Arja; Leino-Kilpi, Helena; Katajisto, Jouko

2008-07-01

The aim of this study was to describe nursing students' perceptions of factors related to three types of student-patient relationship identified in an earlier study: mechanistic, authoritative and facilitative. Another aim was to identify which factors predict the type of relationship. A convenience sample of 310 Bachelor of Health Care students was recruited. The data were collected by using a questionnaire especially designed for this study. Data analysis used the chi-square test, Fisher's exact test, one-way analysis of variance and multinomial logistic regression. Older age was the only significant predictor of a facilitative relationship, whereas fourth-year studies and support received from a person other than supervisor predicted an authoritative relationship. Furthermore, students in authoritative and facilitative relationships had a more positive perception of the patient's attributes as a patient and of patient's improved health and commitment to self-care than students in a mechanistic relationship. A positive perception of the atmosphere during collaboration was more common among students in an authoritative relationship than in a mechanistic relationship. The findings of this study offer useful clues for developing nursing education and empowering patients with a view to improving the quality of nursing care.

Pharmaco-EEG: A Study of Individualized Medicine in Clinical Practice.

PubMed

Swatzyna, Ronald J; Kozlowski, Gerald P; Tarnow, Jay D

2015-07-01

Pharmaco-electroencephalography (Pharmaco-EEG) studies using clinical EEG and quantitative EEG (qEEG) technologies have existed for more than 4 decades. This is a promising area that could improve psychotropic intervention using neurological data. One of the objectives in our clinical practice has been to collect EEG and quantitative EEG (qEEG) data. In the past 5 years, we have identified a subset of refractory cases (n = 386) found to contain commonalities of a small number of electrophysiological features in the following diagnostic categories: mood, anxiety, autistic spectrum, and attention deficit disorders, Four abnormalities were noted in the majority of medication failure cases and these abnormalities did not appear to significantly align with their diagnoses. Those were the following: encephalopathy, focal slowing, beta spindles, and transient discharges. To analyze the relationship noted, they were tested for association with the assigned diagnoses. Fisher's exact test and binary logistics regression found very little (6%) association between particular EEG/qEEG abnormalities and diagnoses. Findings from studies of this type suggest that EEG/qEEG provides individualized understanding of pharmacotherapy failures and has the potential to improve medication selection. © EEG and Clinical Neuroscience Society (ECNS) 2014.

Work accidents and self-esteem of nursing professional in hospital settings

PubMed Central

dos Santos, Sérgio Valverde Marques; Macedo, Flávia Ribeiro Martins; da Silva, Luiz Almeida; Resck, Zelia Marilda Rodrigues; Nogueira, Denismar Alves; Terra, Fábio de Souza

2017-01-01

Abstract Objective: to analyze the occurrence of work accidents and the self-esteem of nurses in hospitals of a municipality of Minas Gerais. Method: descriptive-analytical and cross-sectional study developed with 393 nursing professionals from three hospitals of a municipality in southern Minas Gerais. The Rosenberg Self-Esteem Scale and a questionnaire to characterize the population and work accidents were used for data collection. Data analysis was performed using Person's chi-squared test, Fisher's exact test, Cronbach's alpha, odds ratio and logistic regression. Results: of the professionals studied, 15% had suffered an accident at work and 70.2% presented high self-esteem. Through the analysis, it was observed that smoking, religious belief and an outstanding event in the career were significantly associated with work accidents. In relation to self-esteem, family income, length of time working in the profession and an outstanding event in the career presented significant associations. Conclusion: factors such as smoking, religious belief, family income, length of time working in the profession and an outstanding event in the career can cause professionals to have accidents and/or cause changes in self-esteem, which can compromise their physical and mental health and their quality of life and work. PMID:28443993

Oral habits as risk factors for anterior open bite in the deciduous and mixed dentition - cross-sectional study.

PubMed

Urzal, V; Braga, A C; Ferreira, A P

2013-12-01

Anterior open bite (AOB) is an occlusal anomaly commonly associated with oral habits (OH). The aim of this study was to determine the prevalence of OH as a risk factor for the AOB. A group of children aged between 3 and 12 years were observed. The statistical methodology included independent chi-square test, Fisher's exact test and binary logistic regression. The frequency of oral habits was of 43.5% in the deciduous dentition and 54.2% in the mixed dentition. There was a statistically significant association of pacifier sucking: 61.7 and 16.1 odd ratios (OR), and tongue thrust: 3.9 and 9.2 OR with AOB in both groups, respectively. Thumb sucking occurred only in the deciduous dentition with 5.6 OR. OH and AOB have a high frequency in children. They hinder the normal development of dental and skeletal structures. As OH are risk factors for AOB, the damaging habits most frequently associated are: pacifier sucking, thumb sucking, and tongue thrust. Due to the correlation between the prevalence of AOB and OH, prevention strategies incorporating psychological data related to children should be integrated into a national public health programme.

Occupational accidents among mototaxi drivers.

PubMed

Amorim, Camila Rego; de Araújo, Edna Maria; de Araújo, Tânia Maria; de Oliveira, Nelson Fernandes

2012-03-01

The use of motorcycles as a means of work has contributed to the increase in traffic accidents, in particular, mototaxi accidents. The aim of this study was to estimate and characterize the incidence of occupational accidents among the mototaxis registered in Feira de Santana, BA. This is a cross-sectional study with descriptive and census data. Of the 300 professionals registered at the Municipal Transportation Service, 267 professionals were interviewed through a structured questionnaire. Then, a descriptive analysis was conducted and the incidence of accidents was estimated based on the variables studied. Relative risks were calculated and statistical significance was determined using the chi-square test and Fisher's exact test, considering p < 0.05. Logistic regression was used in order to perform simultaneous adjustment of variables. Occupational accidents were observed in 10.5% of mototaxis. There were mainly minor injuries (48.7%), 27% of them requiring leaves of absence from work. There was an association between the days of work per week, fatigue in lower limbs and musculoskeletal complaints, and accidents. Knowledge of the working conditions and accidents involved in this activity can be of great importance for the adoption of traffic education policies, and to help prevent accidents by improving the working conditions and lives of these professionals.

Validity of Teacher-Based Vision Screening and Factors Associated with the Accuracy of Vision Screening in Vietnamese Children.

PubMed

Paudel, Prakash; Kovai, Vilas; Naduvilath, Thomas; Phuong, Ha Thanh; Ho, Suit May; Giap, Nguyen Viet

2016-01-01

To assess validity of teacher-based vision screening and elicit factors associated with accuracy of vision screening in Vietnam. After brief training, teachers independently measured visual acuity (VA) in 555 children aged 12-15 years in Ba Ria - Vung Tau Province. Teacher VA measurements were compared to those of refractionists. Sensitivity, specificity, positive predictive value and negative predictive value were calculated for uncorrected VA (UVA) and presenting VA (PVA) 20/40 or worse in either eye. Chi-square, Fisher's exact test and multivariate logistic regression were used to assess factors associated with accuracy of vision screening. Level of significance was set at 5%. Trained teachers in Vietnam demonstrated 86.7% sensitivity, 95.7% specificity, 86.7% positive predictive value and 95.7% negative predictive value in identifying children with visual impairment using the UVA measurement. PVA measurement revealed low accuracy for teachers, which was significantly associated with child's age, sex, spectacle wear and myopic status, but UVA measurement showed no such associations. Better accuracy was achieved in measurement of VA and identification of children with visual impairment using UVA measurement compared to PVA. UVA measurement is recommended for teacher-based vision screening programs.

Association between Pre-Transplant Serum Malondialdehyde Levels and Survival One Year after Liver Transplantation for Hepatocellular Carcinoma

PubMed Central

Lorente, Leonardo; Rodriguez, Sergio T.; Sanz, Pablo; Abreu-González, Pedro; Díaz, Dácil; Moreno, Antonia M.; Borja, Elisa; Martín, María M.; Jiménez, Alejandro; Barrera, Manuel A.

2016-01-01

Previous studies have found higher levels of serum malondialdehyde (MDA) in hepatocellular carcinoma (HCC) patients compared to healthy controls and higher MDA concentrations in tumoral tissue of HCC patients than in non-tumoral tissue. However, the association between pre-transplant serum levels of MDA and survival in HCC patients after liver transplantation (LT) has not been described, and the aim of the present study was to determine whether such an association exists. In this observational study we measured serum MDA levels in 127 patients before LT. We found higher pre-LT serum MDA levels in 15 non-surviving than in 112 surviving patients one year after LT (p = 0.02). Exact binary logistic regression analysis revealed that pre-LT serum levels of MDA over 3.37 nmol/mL were associated with mortality after one year of LT (Odds ratio = 5.38; 95% confidence interval (CI) = from 1.580 to infinite; p = 0.007) adjusting for age of the deceased donor. The main finding of our study was that there is an association between serum MDA levels before LT for HCC and 1-year survival after LT. PMID:27058525

Pre-treatment plasma proteomic markers associated with survival in oesophageal cancer

PubMed Central

Kelly, P; Paulin, F; Lamont, D; Baker, L; Clearly, S; Exon, D; Thompson, A

2012-01-01

Background: The incidence of oesophageal adenocarcinoma is increasing worldwide but survival remains poor. Neoadjuvant chemotherapy can improve survival, but prognostic and predictive biomarkers are required. This study built upon preclinical approaches to identify prognostic plasma proteomic markers in oesophageal cancer. Methods: Plasma samples collected before and during the treatment of oesophageal cancer and non-cancer controls were analysed by surface-enhanced laser desorption/ionisation time-of-flight (SELDI-TOF) mass spectroscopy (MS). Protein peaks were identified by MS in tryptic digests of purified fractions. Associations between peak intensities obtained in the spectra and clinical endpoints (survival, disease-free survival) were tested by univariate (Fisher's exact test) and multivariate analysis (binary logistic regression). Results: Plasma protein peaks were identified that differed significantly (P<0.05, ANOVA) between the oesophageal cancer and control groups at baseline. Three peaks, confirmed as apolipoprotein A-I, serum amyloid A and transthyretin, in baseline (pre-treatment) samples were associated by univariate and multivariate analysis with disease-free survival and overall survival. Conclusion: Plasma proteins can be detected prior to treatment for oesophageal cancer that are associated with outcome and merit testing as prognostic and predictive markers of response to guide chemotherapy in oesophageal cancer. PMID:22294182

Pre-treatment plasma proteomic markers associated with survival in oesophageal cancer.

PubMed

Kelly, P; Paulin, F; Lamont, D; Baker, L; Clearly, S; Exon, D; Thompson, A

2012-02-28

The incidence of oesophageal adenocarcinoma is increasing worldwide but survival remains poor. Neoadjuvant chemotherapy can improve survival, but prognostic and predictive biomarkers are required. This study built upon preclinical approaches to identify prognostic plasma proteomic markers in oesophageal cancer. Plasma samples collected before and during the treatment of oesophageal cancer and non-cancer controls were analysed by surface-enhanced laser desorption/ionisation time-of-flight (SELDI-TOF) mass spectroscopy (MS). Protein peaks were identified by MS in tryptic digests of purified fractions. Associations between peak intensities obtained in the spectra and clinical endpoints (survival, disease-free survival) were tested by univariate (Fisher's exact test) and multivariate analysis (binary logistic regression). Plasma protein peaks were identified that differed significantly (P<0.05, ANOVA) between the oesophageal cancer and control groups at baseline. Three peaks, confirmed as apolipoprotein A-I, serum amyloid A and transthyretin, in baseline (pre-treatment) samples were associated by univariate and multivariate analysis with disease-free survival and overall survival. Plasma proteins can be detected prior to treatment for oesophageal cancer that are associated with outcome and merit testing as prognostic and predictive markers of response to guide chemotherapy in oesophageal cancer.

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.

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…

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.

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.

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.

Developmental Screening Referrals: Child and Family Factors that Predict Referral Completion

ERIC Educational Resources Information Center

Jennings, Danielle J.; Hanline, Mary Frances

2013-01-01

This study researched the predictive impact of developmental screening results and the effects of child and family characteristics on completion of referrals given for evaluation. Logistical and hierarchical logistic regression analyses were used to determine the significance of 10 independent variables on the predictor variable. The number of…

[Renal insufficiency and clinical outcomes in patients with acute coronary syndrome undergoing percutaneous coronary intervention: a multi-centre study].

PubMed

Huo, Yong; Ho, Wa

2007-12-18

To investigate the association of renal insufficiency and clinical outcomes in patients with acute coronary syndrome(ACS). The study was a multi-centre register study including 3,589 ACS patients coming from 39 centers across China who had received percutaneous coronary intervention(PCI) prior to 1st February, 2007. Estimated glomerular filtration rate (eGFR) was calculated for all patients using the 4-variable MDRD equation with the serum creatinine obtained before angiography. The association between renal insufficiency and clinical outcomes and the presence of in-hospital death and bleeding was studied by Fisher's exact test. Multi-variable analysis on the risk factors of in-hospital bleeding was done by logistic regression test. The mean age of the study population was (61.74+/-11.37) years (ranging from 23 years to 92 years)and 76.5% (2,746/3,589) of the population was male. Only 90 patients (2.51%) were known to have chronic kidney disease at the time of admission and 144 patients(4.01%) had serum creatinine levels above 133 micromol/L. However, after the evaluation of renal status by the MDRD equation, 2,250 patients (63.1%)showed a reduction in eGFR of less than 90 mL/min, of whom, 472 (13.1%) even reached the level of moderate renal insufficiency (eGFR<60 mL/min) and above. Seven patients(0.20%) were proved to have chronic total occlusion lesions(CTO) and eight (0.22%) needed shift to coronary artery bypass grafting (CABG) after angiography. Both the presence of CTO lesions and CABG were proved to be associated with decrease of renal function through Fisher's exact test (P= 0.005 8 and 0.041, respectively). The in-hospital mortality rate was 0.47%(17/3 589) which was associated with the degree of renal insufficiency (P=0.001 3). A total of 75 patients(2.09%) of in-hospital bleeding were recorded with 26 patients(0.72%) diagnosed as major bleeding events. 92% (69/75) of the bleeding events occurred after PCI. Bleeding was found to be associated with the degree of renal insufficiency in every type of antithrombotic therapy (P<0.001). After adjusting with other variables by logistic regression test, renal insufficiency (eGFR per 10 mL/min decrease, OR=1.133, 95% CI 1.011- 1.27, P=0.032)and age (above 65 years, OR=1.907, 95% CI 1.107-3.28, P=0.02) were proved to be the risk factors of in-hospital bleeding. Renal insufficiency is very common in ACS patients but self-report rate is low among this population. Renal function evaluated by eGFR should be carried out for every patient hospitalized for ACS for risk stratification. Patients with severer renal insufficiency usually have more complicated clinical manifestations and a higher rate of in-hospital bleeding.

Development of S-ARIMA Model for Forecasting Demand in a Beverage Supply Chain

NASA Astrophysics Data System (ADS)

Mircetic, Dejan; Nikolicic, Svetlana; Maslaric, Marinko; Ralevic, Nebojsa; Debelic, Borna

2016-11-01

Demand forecasting is one of the key activities in planning the freight flows in supply chains, and accordingly it is essential for planning and scheduling of logistic activities within observed supply chain. Accurate demand forecasting models directly influence the decrease of logistics costs, since they provide an assessment of customer demand. Customer demand is a key component for planning all logistic processes in supply chain, and therefore determining levels of customer demand is of great interest for supply chain managers. In this paper we deal with exactly this kind of problem, and we develop the seasonal Autoregressive IntegratedMoving Average (SARIMA) model for forecasting demand patterns of a major product of an observed beverage company. The model is easy to understand, flexible to use and appropriate for assisting the expert in decision making process about consumer demand in particular periods.

Sonographic Differentiation of Complicated From Uncomplicated Appendicitis: Implications for Antibiotics-First Therapy.

PubMed

Xu, Yingding; Jeffrey, R Brooke; Chang, Stephanie T; DiMaio, Michael A; Olcott, Eric W

2017-02-01

To evaluate sonographic findings as indicators of complicated versus uncomplicated appendicitis in the setting of known appendicitis, a necessary distinction in deciding whether to proceed with antibiotic therapy or with appendectomy. With Institutional Review Board approval and Health Insurance Portability and Accountability Act compliance, appendiceal sonograms of 119 patients with histopathologically proven appendicitis were retrospectively blindly reviewed to determine the presence or absence of the normally echogenic submucosal layer, the presence of mural hyperemia, periappendiceal fluid, appendicoliths, and hyperechoic periappendiceal fat and to determine the maximum outside diameter. Results were compared with the presence of complicated versus uncomplicated appendicitis on histopathologic examination and assessed by both univariate and mulitvariate logistic regression; confidence intervals (CIs) of proportions were assessed by the exact binomial test. Thirty-two (26.9%) of the 119 patients had complicated appendicitis, including 11 with gangrenous appendicitis without perforation and 21 with gangrenous appendicitis and perforation. Loss of the submucosal layer was the only independent significant indicator of complicated appendicitis in multivariate regression (P < .001) and provided sensitivity and specificity values of 100.0% (95% CI, 89.1%-100.0%) and 92.0% (95% CI, 84.1%-96.7%), respectively. Loss of the normally echogenic submucosal layer was the most useful sonographic finding for discriminating complicated from uncomplicated appendicitis, being the only finding independently and significantly associated with complicated appendicitis and, additionally, providing both high sensitivity and high specificity. This information may help a physician decide whether to proceed with antibiotic therapy or with appendectomy when treating a patient with appendicitis. © 2016 by the American Institute of Ultrasound in Medicine.

Dual diagnosis vs. triple diagnosis in HIV: a comparative study to evaluate the differences in psychopathology and suicidal risk in HIV positive male subjects.

PubMed

Gupta, M; Kumar, K; Garg, P D

2013-12-01

The problem of triple diagnosis of HIV, substance abuse and psychiatric disorders is a complex one with difficult solutions. HIV disease progression is affected by substance use as well as psychiatric illness burden due to both direct as well as indirect factors. Continuing substance abuse with poor drug adherence coexists with psychiatric disorders leading to increased morbidity and mortality. A total of 100 HIV positive subjects comprising of two groups each having 50 subjects with and without substance abuse were assessed using detailed history, mental state examination, WHO schedule for clinical assessment in neuropsychiatry (SCAN 2.0) and Beck's Scale for Suicidal Ideation (BSS). Statistical analysis used Chi-Square test, Fischer's exact test, Student's t-test, Pearson's correlation coefficient, univariate and multiple regression analysis, univariate and multiple logistic regression analysis. p-Value<0.05 was considered to denote statistical significance. Subjects with substance use disorder had higher rates of psychiatric morbidity (52% vs. 24%, 95% CI=0.5200, p<0.05). The rate of antiretroviral therapy default was almost double in subjects with substance abuse, as compared to subjects without substance use. Suicidal risk was significantly increased (p<0.05) in subjects with co-morbid medical disorders but substance abuse did not increase the risk. Substance abuse inflicts a much greater burden on HIV positive individuals as compared to subjects without substance use. Concomitant substance abuse resulted in significantly increased duration of illness and psychiatric morbidity. Copyright © 2013 Elsevier B.V. All rights reserved.

Does pelvicaliceal system anatomy affect success of percutaneous nephrolithotomy?

PubMed

Binbay, Murat; Akman, Tolga; Ozgor, Faruk; Yazici, Ozgur; Sari, Erhan; Erbin, Akif; Kezer, Cem; Sarilar, Omer; Berberoglu, Yalcın; Muslumanoglu, Ahmet Yaser

2011-10-01

To investigate the effect of the pelvicaliceal system (PCS) anatomy on the percutaneous nephrolithotomy (PCNL) success rate. Although the caliceal anatomy is effective for stone clearance after shock wave lithotripsy and retrograde intrarenal lithotripsy, the effect of the caliceal anatomy after PCNL has not been evaluated to date. A total of 498 patients who had undergone PCNL and preoperative intravenous urography were enrolled in our study. Kidney-related anatomic factors, such as the PCS surface area and type, degree of hydronephrosis, infundibulopelvic angle, upper-lower calix angle, infundibular length, and infundibular width were calculated using intravenous urography. The association between the PCNL success rate and kidney-related anatomic factors was retrospectively analyzed using chi-square tests, Fisher's exact test, Mann-Whitney U test, and forward stepwise regression analysis. A success rate of 78.1% was achieved. No difference was seen the success rates among the PCS types. The mean PCS surface area was 20.1 ± 9.7 cm(2) in patients with successful outcomes and 24.5 ± 10.2 cm(2) in patients with remaining stones (P = .001). The mean infundibulopelvic angle, upper-lower calix angle, infundibular length, and infundibular width were similar in both groups. Multivariate binary logistic regression analysis showed that stone configuration and PCS surface area were independent factors affecting the PCNL success rates. The results of our study have shown that the PCS surface area is the only anatomic factor that affects the PCNL success rate and patients with a PCS surface area <20.5 cm(2) have greater PCNL success. Copyright © 2011 Elsevier Inc. All rights reserved.

Comparison of Xenon-Enhanced Area-Detector CT and Krypton Ventilation SPECT/CT for Assessment of Pulmonary Functional Loss and Disease Severity in Smokers.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Takenaka, Daisuke; Kaminaga, Shigeo; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2018-02-01

The objective of this study was to compare the capability of xenon-enhanced area-detector CT (ADCT) performed with a subtraction technique and coregistered 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity in smokers. Forty-six consecutive smokers (32 men and 14 women; mean age, 67.0 years) underwent prospective unenhanced and xenon-enhanced ADCT, 81m Kr-ventilation SPECT/CT, and pulmonary function tests. Disease severity was evaluated according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. CT-based functional lung volume (FLV), the percentage of wall area to total airway area (WA%), and ventilated FLV on xenon-enhanced ADCT and SPECT/CT were calculated for each smoker. All indexes were correlated with percentage of forced expiratory volume in 1 second (%FEV 1 ) using step-wise regression analyses, and univariate and multivariate logistic regression analyses were performed. In addition, the diagnostic accuracy of the proposed model was compared with that of each radiologic index by means of McNemar analysis. Multivariate logistic regression showed that %FEV 1 was significantly affected (r = 0.77, r 2 = 0.59) by two factors: the first factor, ventilated FLV on xenon-enhanced ADCT (p < 0.0001); and the second factor, WA% (p = 0.004). Univariate logistic regression analyses indicated that all indexes significantly affected GOLD classification (p < 0.05). Multivariate logistic regression analyses revealed that ventilated FLV on xenon-enhanced ADCT and CT-based FLV significantly influenced GOLD classification (p < 0.0001). The diagnostic accuracy of the proposed model was significantly higher than that of ventilated FLV on SPECT/CT (p = 0.03) and WA% (p = 0.008). Xenon-enhanced ADCT is more effective than 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity.

[Is there either agenesis or regression of the Mullerian duct in female bird embryos under the influence of male hormone?].

PubMed

Lutz-Ostertag, Y; Lutz, H

1976-01-01

The natural occurence of "Free-Martinism" in Birds and the chorio-allantoïc grafting experiments of testis fragments on female chick host-embryos allow to the authors to define the manner provoking the entire or partial disappearance of the müllerian ducts and to state exactly if the phenomenon is a agenesis or a regression.

Detection of Differential Item Functioning with Nonlinear Regression: A Non-IRT Approach Accounting for Guessing

ERIC Educational Resources Information Center

Drabinová, Adéla; Martinková, Patrícia

2017-01-01

In this article we present a general approach not relying on item response theory models (non-IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non-IRT approach, NLR can…

Exploring students' patterns of reasoning

NASA Astrophysics Data System (ADS)

Matloob Haghanikar, Mojgan

As part of a collaborative study of the science preparation of elementary school teachers, we investigated the quality of students' reasoning and explored the relationship between sophistication of reasoning and the degree to which the courses were considered inquiry oriented. To probe students' reasoning, we developed open-ended written content questions with the distinguishing feature of applying recently learned concepts in a new context. We devised a protocol for developing written content questions that provided a common structure for probing and classifying students' sophistication level of reasoning. In designing our protocol, we considered several distinct criteria, and classified students' responses based on their performance for each criterion. First, we classified concepts into three types: Descriptive, Hypothetical, and Theoretical and categorized the abstraction levels of the responses in terms of the types of concepts and the inter-relationship between the concepts. Second, we devised a rubric based on Bloom's revised taxonomy with seven traits (both knowledge types and cognitive processes) and a defined set of criteria to evaluate each trait. Along with analyzing students' reasoning, we visited universities and observed the courses in which the students were enrolled. We used the Reformed Teaching Observation Protocol (RTOP) to rank the courses with respect to characteristics that are valued for the inquiry courses. We conducted logistic regression for a sample of 18courses with about 900 students and reported the results for performing logistic regression to estimate the relationship between traits of reasoning and RTOP score. In addition, we analyzed conceptual structure of students' responses, based on conceptual classification schemes, and clustered students' responses into six categories. We derived regression model, to estimate the relationship between the sophistication of the categories of conceptual structure and RTOP scores. However, the outcome variable with six categories required a more complicated regression model, known as multinomial logistic regression, generalized from binary logistic regression. With the large amount of collected data, we found that the likelihood of the higher cognitive processes were in favor of classes with higher measures on inquiry. However, the usage of more abstract concepts with higher order conceptual structures was less prevalent in higher RTOP courses.

A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study.

PubMed

Kabeshova, A; Annweiler, C; Fantino, B; Philip, T; Gromov, V A; Launay, C P; Beauchet, O

2014-06-01

Regression tree (RT) analyses are particularly adapted to explore the risk of recurrent falling according to various combinations of fall risk factors compared to logistic regression models. The aims of this study were (1) to determine which combinations of fall risk factors were associated with the occurrence of recurrent falls in older community-dwellers, and (2) to compare the efficacy of RT and multiple logistic regression model for the identification of recurrent falls. A total of 1,760 community-dwelling volunteers (mean age ± standard deviation, 71.0 ± 5.1 years; 49.4 % female) were recruited prospectively in this cross-sectional study. Age, gender, polypharmacy, use of psychoactive drugs, fear of falling (FOF), cognitive disorders and sad mood were recorded. In addition, the history of falls within the past year was recorded using a standardized questionnaire. Among 1,760 participants, 19.7 % (n = 346) were recurrent fallers. The RT identified 14 nodes groups and 8 end nodes with FOF as the first major split. Among participants with FOF, those who had sad mood and polypharmacy formed the end node with the greatest OR for recurrent falls (OR = 6.06 with p < 0.001). Among participants without FOF, those who were male and not sad had the lowest OR for recurrent falls (OR = 0.25 with p < 0.001). The RT correctly classified 1,356 from 1,414 non-recurrent fallers (specificity = 95.6 %), and 65 from 346 recurrent fallers (sensitivity = 18.8 %). The overall classification accuracy was 81.0 %. The multiple logistic regression correctly classified 1,372 from 1,414 non-recurrent fallers (specificity = 97.0 %), and 61 from 346 recurrent fallers (sensitivity = 17.6 %). The overall classification accuracy was 81.4 %. Our results show that RT may identify specific combinations of risk factors for recurrent falls, the combination most associated with recurrent falls involving FOF, sad mood and polypharmacy. The FOF emerged as the risk factor strongly associated with recurrent falls. In addition, RT and multiple logistic regression were not sensitive enough to identify the majority of recurrent fallers but appeared efficient in detecting individuals not at risk of recurrent falls.

Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

Testing for gene-environment interaction under exposure misspecification.

PubMed

Sun, Ryan; Carroll, Raymond J; Christiani, David C; Lin, Xihong

2017-11-09

Complex interplay between genetic and environmental factors characterizes the etiology of many diseases. Modeling gene-environment (GxE) interactions is often challenged by the unknown functional form of the environment term in the true data-generating mechanism. We study the impact of misspecification of the environmental exposure effect on inference for the GxE interaction term in linear and logistic regression models. We first examine the asymptotic bias of the GxE interaction regression coefficient, allowing for confounders as well as arbitrary misspecification of the exposure and confounder effects. For linear regression, we show that under gene-environment independence and some confounder-dependent conditions, when the environment effect is misspecified, the regression coefficient of the GxE interaction can be unbiased. However, inference on the GxE interaction is still often incorrect. In logistic regression, we show that the regression coefficient is generally biased if the genetic factor is associated with the outcome directly or indirectly. Further, we show that the standard robust sandwich variance estimator for the GxE interaction does not perform well in practical GxE studies, and we provide an alternative testing procedure that has better finite sample properties. © 2017, The International Biometric Society.

Why credit risk markets are predestined for exhibiting log-periodic power law structures

NASA Astrophysics Data System (ADS)

Wosnitza, Jan Henrik; Leker, Jens

2014-01-01

Recent research has established the existence of log-periodic power law (LPPL) patterns in financial institutions’ credit default swap (CDS) spreads. The main purpose of this paper is to clarify why credit risk markets are predestined for exhibiting LPPL structures. To this end, the credit risk prediction of two variants of logistic regression, i.e. polynomial logistic regression (PLR) and kernel logistic regression (KLR), are firstly compared to the standard logistic regression (SLR). In doing so, the question whether the performances of rating systems based on balance sheet ratios can be improved by nonlinear transformations of the explanatory variables is resolved. Building on the result that nonlinear balance sheet ratio transformations hardly improve the SLR’s predictive power in our case, we secondly compare the classification performance of a multivariate SLR to the discriminative powers of probabilities of default derived from three different capital market data, namely bonds, CDSs, and stocks. Benefiting from the prompt inclusion of relevant information, the capital market data in general and CDSs in particular increasingly outperform the SLR while approaching the time of the credit event. Due to the higher classification performances, it seems plausible for creditors to align their investment decisions with capital market-based default indicators, i.e., to imitate the aggregate opinion of the market participants. Since imitation is considered to be the source of LPPL structures in financial time series, it is highly plausible to scan CDS spread developments for LPPL patterns. By establishing LPPL patterns in governmental CDS spread trajectories of some European crisis countries, the LPPL’s application to credit risk markets is extended. This novel piece of evidence further strengthens the claim that credit risk markets are adequate breeding grounds for LPPL patterns.

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions Using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

The use of generalized estimating equations in the analysis of motor vehicle crash data.

PubMed

Hutchings, Caroline B; Knight, Stacey; Reading, James C

2003-01-01

The purpose of this study was to determine if it is necessary to use generalized estimating equations (GEEs) in the analysis of seat belt effectiveness in preventing injuries in motor vehicle crashes. The 1992 Utah crash dataset was used, excluding crash participants where seat belt use was not appropriate (n=93,633). The model used in the 1996 Report to Congress [Report to congress on benefits of safety belts and motorcycle helmets, based on data from the Crash Outcome Data Evaluation System (CODES). National Center for Statistics and Analysis, NHTSA, Washington, DC, February 1996] was analyzed for all occupants with logistic regression, one level of nesting (occupants within crashes), and two levels of nesting (occupants within vehicles within crashes) to compare the use of GEEs with logistic regression. When using one level of nesting compared to logistic regression, 13 of 16 variance estimates changed more than 10%, and eight of 16 parameter estimates changed more than 10%. In addition, three of the independent variables changed from significant to insignificant (alpha=0.05). With the use of two levels of nesting, two of 16 variance estimates and three of 16 parameter estimates changed more than 10% from the variance and parameter estimates in one level of nesting. One of the independent variables changed from insignificant to significant (alpha=0.05) in the two levels of nesting model; therefore, only two of the independent variables changed from significant to insignificant when the logistic regression model was compared to the two levels of nesting model. The odds ratio of seat belt effectiveness in preventing injuries was 12% lower when a one-level nested model was used. Based on these results, we stress the need to use a nested model and GEEs when analyzing motor vehicle crash data.

Impact of Colic Pain as a Significant Factor for Predicting the Stone Free Rate of One-Session Shock Wave Lithotripsy for Treating Ureter Stones: A Bayesian Logistic Regression Model Analysis

PubMed Central

Chung, Doo Yong; Cho, Kang Su; Lee, Dae Hun; Han, Jang Hee; Kang, Dong Hyuk; Jung, Hae Do; Kown, Jong Kyou; Ham, Won Sik; Choi, Young Deuk; Lee, Joo Yong

2015-01-01

Purpose This study was conducted to evaluate colic pain as a prognostic pretreatment factor that can influence ureter stone clearance and to estimate the probability of stone-free status in shock wave lithotripsy (SWL) patients with a ureter stone. Materials and Methods We retrospectively reviewed the medical records of 1,418 patients who underwent their first SWL between 2005 and 2013. Among these patients, 551 had a ureter stone measuring 4–20 mm and were thus eligible for our analyses. The colic pain as the chief complaint was defined as either subjective flank pain during history taking and physical examination. Propensity-scores for established for colic pain was calculated for each patient using multivariate logistic regression based upon the following covariates: age, maximal stone length (MSL), and mean stone density (MSD). Each factor was evaluated as predictor for stone-free status by Bayesian and non-Bayesian logistic regression model. Results After propensity-score matching, 217 patients were extracted in each group from the total patient cohort. There were no statistical differences in variables used in propensity- score matching. One-session success and stone-free rate were also higher in the painful group (73.7% and 71.0%, respectively) than in the painless group (63.6% and 60.4%, respectively). In multivariate non-Bayesian and Bayesian logistic regression models, a painful stone, shorter MSL, and lower MSD were significant factors for one-session stone-free status in patients who underwent SWL. Conclusions Colic pain in patients with ureter calculi was one of the significant predicting factors including MSL and MSD for one-session stone-free status of SWL. PMID:25902059

An Alternative Flight Software Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly; Gay, Robert; Stachowiak, Susan

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter to improve altitude knowledge. In order to increase overall robustness, the vehicle also has an alternate method of triggering the parachute deployment sequence based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this backup trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to semi-automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a statistical classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers improved performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles

Modeling of geogenic radon in Switzerland based on ordered logistic regression.

PubMed

Kropat, Georg; Bochud, François; Murith, Christophe; Palacios Gruson, Martha; Baechler, Sébastien

2017-01-01

The estimation of the radon hazard of a future construction site should ideally be based on the geogenic radon potential (GRP), since this estimate is free of anthropogenic influences and building characteristics. The goal of this study was to evaluate terrestrial gamma dose rate (TGD), geology, fault lines and topsoil permeability as predictors for the creation of a GRP map based on logistic regression. Soil gas radon measurements (SRC) are more suited for the estimation of GRP than indoor radon measurements (IRC) since the former do not depend on ventilation and heating habits or building characteristics. However, SRC have only been measured at a few locations in Switzerland. In former studies a good correlation between spatial aggregates of IRC and SRC has been observed. That's why we used IRC measurements aggregated on a 10 km × 10 km grid to calibrate an ordered logistic regression model for geogenic radon potential (GRP). As predictors we took into account terrestrial gamma doserate, regrouped geological units, fault line density and the permeability of the soil. The classification success rate of the model results to 56% in case of the inclusion of all 4 predictor variables. Our results suggest that terrestrial gamma doserate and regrouped geological units are more suited to model GRP than fault line density and soil permeability. Ordered logistic regression is a promising tool for the modeling of GRP maps due to its simplicity and fast computation time. Future studies should account for additional variables to improve the modeling of high radon hazard in the Jura Mountains of Switzerland. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

An Alternative Flight Software Trigger Paradigm: Applying Multivariate Logistic Regression to Sense Trigger Conditions using Inaccurate or Scarce Information

NASA Technical Reports Server (NTRS)

Smith, Kelly M.; Gay, Robert S.; Stachowiak, Susan J.

2013-01-01

In late 2014, NASA will fly the Orion capsule on a Delta IV-Heavy rocket for the Exploration Flight Test-1 (EFT-1) mission. For EFT-1, the Orion capsule will be flying with a new GPS receiver and new navigation software. Given the experimental nature of the flight, the flight software must be robust to the loss of GPS measurements. Once the high-speed entry is complete, the drogue parachutes must be deployed within the proper conditions to stabilize the vehicle prior to deploying the main parachutes. When GPS is available in nominal operations, the vehicle will deploy the drogue parachutes based on an altitude trigger. However, when GPS is unavailable, the navigated altitude errors become excessively large, driving the need for a backup barometric altimeter. In order to increase overall robustness, the vehicle also has an alternate method of triggering the drogue parachute deployment based on planet-relative velocity if both the GPS and the barometric altimeter fail. However, this velocity-based trigger results in large altitude errors relative to the targeted altitude. Motivated by this challenge, this paper demonstrates how logistic regression may be employed to automatically generate robust triggers based on statistical analysis. Logistic regression is used as a ground processor pre-flight to develop a classifier. The classifier would then be implemented in flight software and executed in real-time. This technique offers excellent performance even in the face of highly inaccurate measurements. Although the logistic regression-based trigger approach will not be implemented within EFT-1 flight software, the methodology can be carried forward for future missions and vehicles.

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

PubMed Central

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

2013-01-01

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

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.

PubMed

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu; Yoshinari, Kouichi; Honda, Hiroshi

2017-03-01

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 were 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. Copyright © 2017 Elsevier Inc. All rights reserved.

An investigation of the speeding-related crash designation through crash narrative reviews sampled via logistic regression.

PubMed

Fitzpatrick, Cole D; Rakasi, Saritha; Knodler, Michael A

2017-01-01

Speed is one of the most important factors in traffic safety as higher speeds are linked to increased crash risk and higher injury severities. Nearly a third of fatal crashes in the United States are designated as "speeding-related", which is defined as either "the driver behavior of exceeding the posted speed limit or driving too fast for conditions." While many studies have utilized the speeding-related designation in safety analyses, no studies have examined the underlying accuracy of this designation. Herein, we investigate the speeding-related crash designation through the development of a series of logistic regression models that were derived from the established speeding-related crash typologies and validated using a blind review, by multiple researchers, of 604 crash narratives. The developed logistic regression model accurately identified crashes which were not originally designated as speeding-related but had crash narratives that suggested speeding as a causative factor. Only 53.4% of crashes designated as speeding-related contained narratives which described speeding as a causative factor. Further investigation of these crashes revealed that the driver contributing code (DCC) of "driving too fast for conditions" was being used in three separate situations. Additionally, this DCC was also incorrectly used when "exceeding the posted speed limit" would likely have been a more appropriate designation. Finally, it was determined that the responding officer only utilized one DCC in 82% of crashes not designated as speeding-related but contained a narrative indicating speed as a contributing causal factor. The use of logistic regression models based upon speeding-related crash typologies offers a promising method by which all possible speeding-related crashes could be identified. Published by Elsevier Ltd.

Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio.

PubMed

Black, L E; Brion, G M; Freitas, S J

2007-06-01

Predicting the presence of enteric viruses in surface waters is a complex modeling problem. Multiple water quality parameters that indicate the presence of human fecal material, the load of fecal material, and the amount of time fecal material has been in the environment are needed. This paper presents the results of a multiyear study of raw-water quality at the inlet of a potable-water plant that related 17 physical, chemical, and biological indices to the presence of enteric viruses as indicated by cytopathic changes in cell cultures. It was found that several simple, multivariate logistic regression models that could reliably identify observations of the presence or absence of total culturable virus could be fitted. The best models developed combined a fecal age indicator (the atypical coliform [AC]/total coliform [TC] ratio), the detectable presence of a human-associated sterol (epicoprostanol) to indicate the fecal source, and one of several fecal load indicators (the levels of Giardia species cysts, coliform bacteria, and coprostanol). The best fit to the data was found when the AC/TC ratio, the presence of epicoprostanol, and the density of fecal coliform bacteria were input into a simple, multivariate logistic regression equation, resulting in 84.5% and 78.6% accuracies for the identification of the presence and absence of total culturable virus, respectively. The AC/TC ratio was the most influential input variable in all of the models generated, but producing the best prediction required additional input related to the fecal source and the fecal load. The potential for replacing microbial indicators of fecal load with levels of coprostanol was proposed and evaluated by multivariate logistic regression modeling for the presence and absence of virus.