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.

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

Neurophysiological correlates of depressive symptoms in young adults: A quantitative EEG study.

PubMed

Lee, Poh Foong; Kan, Donica Pei Xin; Croarkin, Paul; Phang, Cheng Kar; Doruk, Deniz

2018-01-01

There is an unmet need for practical and reliable biomarkers for mood disorders in young adults. Identifying the brain activity associated with the early signs of depressive disorders could have important diagnostic and therapeutic implications. In this study we sought to investigate the EEG characteristics in young adults with newly identified depressive symptoms. Based on the initial screening, a total of 100 participants (n = 50 euthymic, n = 50 depressive) underwent 32-channel EEG acquisition. Simple logistic regression and C-statistic were used to explore if EEG power could be used to discriminate between the groups. The strongest EEG predictors of mood using multivariate logistic regression models. Simple logistic regression analysis with subsequent C-statistics revealed that only high-alpha and beta power originating from the left central cortex (C3) have a reliable discriminative value (ROC curve >0.7 (70%)) for differentiating the depressive group from the euthymic group. Multivariate regression analysis showed that the single most significant predictor of group (depressive vs. euthymic) is the high-alpha power over C3 (p = 0.03). The present findings suggest that EEG is a useful tool in the identification of neurophysiological correlates of depressive symptoms in young adults with no previous psychiatric history. Our results could guide future studies investigating the early neurophysiological changes and surrogate outcomes in depression. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sperm Retrieval in Patients with Klinefelter Syndrome: A Skewed Regression Model Analysis.

PubMed

Chehrazi, Mohammad; Rahimiforoushani, Abbas; Sabbaghian, Marjan; Nourijelyani, Keramat; Sadighi Gilani, Mohammad Ali; Hoseini, Mostafa; Vesali, Samira; Yaseri, Mehdi; Alizadeh, Ahad; Mohammad, Kazem; Samani, Reza Omani

2017-01-01

The most common chromosomal abnormality due to non-obstructive azoospermia (NOA) is Klinefelter syndrome (KS) which occurs in 1-1.72 out of 500-1000 male infants. The probability of retrieving sperm as the outcome could be asymmetrically different between patients with and without KS, therefore logistic regression analysis is not a well-qualified test for this type of data. This study has been designed to evaluate skewed regression model analysis for data collected from microsurgical testicular sperm extraction (micro-TESE) among azoospermic patients with and without non-mosaic KS syndrome. This cohort study compared the micro-TESE outcome between 134 men with classic KS and 537 men with NOA and normal karyotype who were referred to Royan Institute between 2009 and 2011. In addition to our main outcome, which was sperm retrieval, we also used logistic and skewed regression analyses to compare the following demographic and hormonal factors: age, level of follicle stimulating hormone (FSH), luteinizing hormone (LH), and testosterone between the two groups. A comparison of the micro-TESE between the KS and control groups showed a success rate of 28.4% (38/134) for the KS group and 22.2% (119/537) for the control group. In the KS group, a significantly difference (P<0.001) existed between testosterone levels for the successful sperm retrieval group (3.4 ± 0.48 mg/mL) compared to the unsuccessful sperm retrieval group (2.33 ± 0.23 mg/mL). The index for quasi Akaike information criterion (QAIC) had a goodness of fit of 74 for the skewed model which was lower than logistic regression (QAIC=85). According to the results, skewed regression is more efficient in estimating sperm retrieval success when the data from patients with KS are analyzed. This finding should be investigated by conducting additional studies with different data structures.

Development and validation of a mortality risk model for pediatric sepsis.

PubMed

Chen, Mengshi; Lu, Xiulan; Hu, Li; Liu, Pingping; Zhao, Wenjiao; Yan, Haipeng; Tang, Liang; Zhu, Yimin; Xiao, Zhenghui; Chen, Lizhang; Tan, Hongzhuan

2017-05-01

Pediatric sepsis is a burdensome public health problem. Assessing the mortality risk of pediatric sepsis patients, offering effective treatment guidance, and improving prognosis to reduce mortality rates, are crucial.We extracted data derived from electronic medical records of pediatric sepsis patients that were collected during the first 24 hours after admission to the pediatric intensive care unit (PICU) of the Hunan Children's hospital from January 2012 to June 2014. A total of 788 children were randomly divided into a training (592, 75%) and validation group (196, 25%). The risk factors for mortality among these patients were identified by conducting multivariate logistic regression in the training group. Based on the established logistic regression equation, the logit probabilities for all patients (in both groups) were calculated to verify the model's internal and external validities.According to the training group, 6 variables (brain natriuretic peptide, albumin, total bilirubin, D-dimer, lactate levels, and mechanical ventilation in 24 hours) were included in the final logistic regression model. The areas under the curves of the model were 0.854 (0.826, 0.881) and 0.844 (0.816, 0.873) in the training and validation groups, respectively.The Mortality Risk Model for Pediatric Sepsis we established in this study showed acceptable accuracy to predict the mortality risk in pediatric sepsis patients.

Development and validation of a mortality risk model for pediatric sepsis

PubMed Central

Chen, Mengshi; Lu, Xiulan; Hu, Li; Liu, Pingping; Zhao, Wenjiao; Yan, Haipeng; Tang, Liang; Zhu, Yimin; Xiao, Zhenghui; Chen, Lizhang; Tan, Hongzhuan

2017-01-01

Abstract Pediatric sepsis is a burdensome public health problem. Assessing the mortality risk of pediatric sepsis patients, offering effective treatment guidance, and improving prognosis to reduce mortality rates, are crucial. We extracted data derived from electronic medical records of pediatric sepsis patients that were collected during the first 24 hours after admission to the pediatric intensive care unit (PICU) of the Hunan Children's hospital from January 2012 to June 2014. A total of 788 children were randomly divided into a training (592, 75%) and validation group (196, 25%). The risk factors for mortality among these patients were identified by conducting multivariate logistic regression in the training group. Based on the established logistic regression equation, the logit probabilities for all patients (in both groups) were calculated to verify the model's internal and external validities. According to the training group, 6 variables (brain natriuretic peptide, albumin, total bilirubin, D-dimer, lactate levels, and mechanical ventilation in 24 hours) were included in the final logistic regression model. The areas under the curves of the model were 0.854 (0.826, 0.881) and 0.844 (0.816, 0.873) in the training and validation groups, respectively. The Mortality Risk Model for Pediatric Sepsis we established in this study showed acceptable accuracy to predict the mortality risk in pediatric sepsis patients. PMID:28514310

Logistic regression function for detection of suspicious performance during baseline evaluations using concussion vital signs.

PubMed

Hill, Benjamin David; Womble, Melissa N; Rohling, Martin L

2015-01-01

This study utilized logistic regression to determine whether performance patterns on Concussion Vital Signs (CVS) could differentiate known groups with either genuine or feigned performance. For the embedded measure development group (n = 174), clinical patients and undergraduate students categorized as feigning obtained significantly lower scores on the overall test battery mean for the CVS, Shipley-2 composite score, and California Verbal Learning Test-Second Edition subtests than did genuinely performing individuals. The final full model of 3 predictor variables (Verbal Memory immediate hits, Verbal Memory immediate correct passes, and Stroop Test complex reaction time correct) was significant and correctly classified individuals in their known group 83% of the time (sensitivity = .65; specificity = .97) in a mixed sample of young-adult clinical cases and simulators. The CVS logistic regression function was applied to a separate undergraduate college group (n = 378) that was asked to perform genuinely and identified 5% as having possibly feigned performance indicating a low false-positive rate. The failure rate was 11% and 16% at baseline cognitive testing in samples of high school and college athletes, respectively. These findings have particular relevance given the increasing use of computerized test batteries for baseline cognitive testing and return-to-play decisions after concussion.

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

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.

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.

Impact of Contextual Factors on Prostate Cancer Risk and Outcomes

DTIC Science & Technology

2013-07-01

framework with random effects (“frailty models”) while the case-control analyses (Aim 4) will use multilevel unconditional logistic regression models...contextual-level SES on prostate cancer risk within racial/ethnic groups. The survival analyses (Aims 1-3) will utilize a proportional hazards regression

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.

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.

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.

Effect of duration of denervation on outcomes of ansa-recurrent laryngeal nerve reinnervation.

PubMed

Li, Meng; Chen, Shicai; Wang, Wei; Chen, Donghui; Zhu, Minhui; Liu, Fei; Zhang, Caiyun; Li, Yan; Zheng, Hongliang

2014-08-01

To investigate the efficacy of laryngeal reinnervation with ansa cervicalis among unilateral vocal fold paralysis (UVFP) patients with different denervation durations. We retrospectively reviewed 349 consecutive UVFP cases of delayed ansa cervicalis to the recurrent laryngeal nerve (RLN) anastomosis. Potential influencing factors were analyzed in multivariable logistic regression analysis. Stratification analysis performed was aimed at one of the identified significant variables: denervation duration. Videostroboscopy, perceptual evaluation, acoustic analysis, maximum phonation time (MPT), and laryngeal electromyography (EMG) were performed preoperatively and postoperatively. Gender, age, preoperative EMG status and denervation duration were analyzed in multivariable logistic regression analysis. Stratification analysis was performed on denervation duration, which was divided into three groups according to the interval between RLN injury and reinnervation: group A, 6 to 12 months; group B, 12 to 24 months; and group C, > 24 months. Age, preoperative EMG, and denervation duration were identified as significant variables in multivariable logistic regression analysis. Stratification analysis on denervation duration showed significant differences between group A and C and between group B and C (P < 0.05)-but showed no significant difference between group A and B (P > 0.05) with regard to parameters overall grade, jitter, shimmer, noise-to-harmonics ratio, MPT, and postoperative EMG. In addition, videostroboscopic and laryngeal EMG data, perceptual and acoustic parameters, and MPT values were significantly improved postoperatively in each denervation duration group (P < 0.01). Although delayed laryngeal reinnervation is proved valid for UVFP, surgical outcome is better if the procedure is performed within 2 years after nerve injury than that over 2 years. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

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.

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…

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…

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.

Prediction of body mass index status from voice signals based on machine learning for automated medical applications.

PubMed

Lee, Bum Ju; Kim, Keun Ho; Ku, Boncho; Jang, Jun-Su; Kim, Jong Yeol

2013-05-01

The body mass index (BMI) provides essential medical information related to body weight for the treatment and prognosis prediction of diseases such as cardiovascular disease, diabetes, and stroke. We propose a method for the prediction of normal, overweight, and obese classes based only on the combination of voice features that are associated with BMI status, independently of weight and height measurements. A total of 1568 subjects were divided into 4 groups according to age and gender differences. We performed statistical analyses by analysis of variance (ANOVA) and Scheffe test to find significant features in each group. We predicted BMI status (normal, overweight, and obese) by a logistic regression algorithm and two ensemble classification algorithms (bagging and random forests) based on statistically significant features. In the Female-2030 group (females aged 20-40 years), classification experiments using an imbalanced (original) data set gave area under the receiver operating characteristic curve (AUC) values of 0.569-0.731 by logistic regression, whereas experiments using a balanced data set gave AUC values of 0.893-0.994 by random forests. AUC values in Female-4050 (females aged 41-60 years), Male-2030 (males aged 20-40 years), and Male-4050 (males aged 41-60 years) groups by logistic regression in imbalanced data were 0.585-0.654, 0.581-0.614, and 0.557-0.653, respectively. AUC values in Female-4050, Male-2030, and Male-4050 groups in balanced data were 0.629-0.893 by bagging, 0.707-0.916 by random forests, and 0.695-0.854 by bagging, respectively. In each group, we found discriminatory features showing statistical differences among normal, overweight, and obese classes. The results showed that the classification models built by logistic regression in imbalanced data were better than those built by the other two algorithms, and significant features differed according to age and gender groups. Our results could support the development of BMI diagnosis tools for real-time monitoring; such tools are considered helpful in improving automated BMI status diagnosis in remote healthcare or telemedicine and are expected to have applications in forensic and medical science. Copyright © 2013 Elsevier B.V. All rights reserved.

An examination of constraints to wilderness visitation

Treesearch

Gary T. Green; J. Michael Bowker; Cassandra Y. Johnson; H. Ken Cordell; Xiongfei Wang

2007-01-01

Certain social groups appear notably less in wilderness visitation surveys than their population proportion. This study examines whether different social groups in American society (minorities, women, rural dwellers, low income and less educated populations) perceive more constraints to wilderness visitation than other groups. Logistic regressions were fit to data from...

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.

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

America's Democracy Colleges: The Civic Engagement of Community College Students

ERIC Educational Resources Information Center

Angeli Newell, Mallory

2014-01-01

This study explored the civic engagement of current two- and four-year students to explore whether differences exist between the groups and what may explain the differences. Using binary logistic regression and Ordinary Least Squares regression it was found that community-based engagement was lower for two- than four-year students, though…

Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran

NASA Astrophysics Data System (ADS)

Zeraatpisheh, Mojtaba; Ayoubi, Shamsollah; Jafari, Azam; Finke, Peter

2017-05-01

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the 'noisiness' of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and suborder levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

Detection of high GS risk group prostate tumors by diffusion tensor imaging and logistic regression modelling.

PubMed

Ertas, Gokhan

2018-07-01

To assess the value of joint evaluation of diffusion tensor imaging (DTI) measures by using logistic regression modelling to detect high GS risk group prostate tumors. Fifty tumors imaged using DTI on a 3 T MRI device were analyzed. Regions of interests focusing on the center of tumor foci and noncancerous tissue on the maps of mean diffusivity (MD) and fractional anisotropy (FA) were used to extract the minimum, the maximum and the mean measures. Measure ratio was computed by dividing tumor measure by noncancerous tissue measure. Logistic regression models were fitted for all possible pair combinations of the measures using 5-fold cross validation. Systematic differences are present for all MD measures and also for all FA measures in distinguishing the high risk tumors [GS ≥ 7(4 + 3)] from the low risk tumors [GS ≤ 7(3 + 4)] (P < 0.05). Smaller value for MD measures and larger value for FA measures indicate the high risk. The models enrolling the measures achieve good fits and good classification performances (R 2 adj  = 0.55-0.60, AUC = 0.88-0.91), however the models using the measure ratios perform better (R 2 adj  = 0.59-0.75, AUC = 0.88-0.95). The model that employs the ratios of minimum MD and maximum FA accomplishes the highest sensitivity, specificity and accuracy (Se = 77.8%, Sp = 96.9% and Acc = 90.0%). Joint evaluation of MD and FA diffusion tensor imaging measures is valuable to detect high GS risk group peripheral zone prostate tumors. However, use of the ratios of the measures improves the accuracy of the detections substantially. Logistic regression modelling provides a favorable solution for the joint evaluations easily adoptable in clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

Risk factors for displaced abomasum or ketosis in Swedish dairy herds.

PubMed

Stengärde, L; Hultgren, J; Tråvén, M; Holtenius, K; Emanuelson, U

2012-03-01

Risk factors associated with high or low long-term incidence of displaced abomasum (DA) or clinical ketosis were studied in 60 Swedish dairy herds, using multivariable logistic regression modelling. Forty high-incidence herds were included as cases and 20 low-incidence herds as controls. Incidence rates were calculated based on veterinary records of clinical diagnoses. During the 3-year period preceding the herd classification, herds with a high incidence had a disease incidence of DA or clinical ketosis above the 3rd quartile in a national database for disease recordings. Control herds had no cows with DA or clinical ketosis. All herds were visited during the housing period and herdsmen were interviewed about management routines, housing, feeding, milk yield, and herd health. Target groups were heifers in late gestation, dry cows, and cows in early lactation. Univariable logistic regression was used to screen for factors associated with being a high-incidence herd. A multivariable logistic regression model was built using stepwise regression. A higher maximum daily milk yield in multiparous cows and a large herd size (p=0.054 and p=0.066, respectively) tended to be associated with being a high-incidence herd. Not cleaning the heifer feeding platform daily increased the odds of having a high-incidence herd twelvefold (p<0.01). Keeping cows in only one group in the dry period increased the odds of having a high incidence herd eightfold (p=0.03). Herd size was confounded with housing system. Housing system was therefore added to the final logistic regression model. In conclusion, a large herd size, a high maximum daily milk yield, keeping dry cows in one group, and not cleaning the feeding platform daily appear to be important risk factors for a high incidence of DA or clinical ketosis in Swedish dairy herds. These results confirm the importance of housing, management and feeding in the prevention of metabolic disorders in dairy cows around parturition and in early lactation. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

Comparison of the Relationship between Women' Empowerment and Fertility between Single-child and Multi-child Families

PubMed Central

Saberi, Tahereh; Ehsanpour, Soheila; Mahaki, Behzad; Kohan, Shahnaz

2018-01-01

Background: The reduction in fertility and increase in the number of single-child families in Iran will result in an increased risk of population aging. One of the factors affecting fertility is women's empowerment. This study aimed to evaluate the relationship between women's empowerment and fertility in single-child and multi-child families. Materials and Methods: This case-control study was conducted among 350 women (120 who had only 1 child as case group and 230 who had 2 or more children as control group) of 15–49 years of age in Isfahan, Iran, in 2016. For data collection, a 2-part questionnaire was designed. Data were analyzed using independent t-test, Chi-square test, and logistic regression analysis. Results: The difference between average scores of women's empowerment in the case group 54.08 (9.88) and control group 51.47 (8.57) was significant (p = 0.002). Simple logistic regression analysis showed that under diploma education, compared to postgraduate education, (OR = 0.21, p = 0.001) and being a housewife, compared to being employed, (OR = 0.45, p = 0.004) decreased the odds of having only 1 child. Multiple logistic regression results showed that the relationship between women's empowerment and fertility was not significant (p = 0.265). Conclusions: Although women in single-child families were more empowered, this was not the main reason for their preference to have only 1 child. In fact, educated and employed women postpone marriage and childbearing and limit fertility to only 1 child despite their desire. PMID:29628961

The base rates and factors associated with reported access to firearms in psychiatric inpatients.

PubMed

Kolla, Bhanu Prakash; O'Connor, Stephen S; Lineberry, Timothy W

2011-01-01

The aim of this study was to define whether specific patient demographic groups, diagnoses or other factors are associated with psychiatric inpatients reporting firearms access. A retrospective medical records review study was conducted using information on access to firearms from electronic medical records for all patients 16 years and older admitted between July 2007 and May 2008 at the Mayo Clinic Psychiatric Hospital in Rochester, MN. Data were obtained only on patients providing authorization for record review. Data were analyzed using univariate and multivariate logistic regression analyses accounting for gender, diagnostic groups, comorbid substance use, history of suicide attempts and family history of suicide/suicide attempts. Seventy-four percent (1169/1580) of patients provided research authorization. The ratio of men to women was identical in both research and nonresearch authorization groups. There were 14.6% of inpatients who reported firearms access. In univariate analysis, men were more likely (P<.0001) to report access than women, and a history of previous suicide attempt(s) was associated with decreased access (P=.02). Multiple logistic regression analyses controlling for other factors found females and patients with history of previous suicide attempt(s) less likely to report access, while patients with a family history of suicide or suicide attempts reported increased firearms access. Diagnostic groups were not associated with access on univariate or multiple logistic regression analyses. Men and inpatients with a family history of suicide/suicide attempts were more likely to report firearms access. Clinicians should develop standardized systems of identification of firearms access and provide guidance on removal. Copyright © 2011 Elsevier Inc. All rights reserved.

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

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

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

PubMed

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

2013-06-01

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

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-05-01

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

Lithium and neuroleptics in combination: is there enhancement of neurotoxicity leading to permanent sequelae?

PubMed

Goldman, S A

1996-10-01

Neurotoxicity in relation to concomitant administration of lithium and neuroleptic drugs, particularly haloperidol, has been an ongoing issue. This study examined whether use of lithium with neuroleptic drugs enhances neurotoxicity leading to permanent sequelae. The Spontaneous Reporting System database of the United States Food and Drug Administration and extant literature were reviewed for spectrum cases of lithium/neuroleptic neurotoxicity. Groups taking lithium alone (Li), lithium/haloperidol (LiHal) and lithium/ nonhaloperidol neuroleptics (LiNeuro), each paired for recovery and sequelae, were established for 237 cases. Statistical analyses included pairwise comparisons of lithium levels using the Wilcoxon Rank Sum procedure and logistic regression to analyze the relationship between independent variables and development of sequelae. The Li and Li-Neuro groups showed significant statistical differences in median lithium levels between recovery and sequelae pairs, whereas the LiHal pair did not differ significantly. Lithium level was associated with sequelae development overall and within the Li and LiNeuro groups; no such association was evident in the LiHal group. On multivariable logistic regression analysis, lithium level and taking lithium/haloperidol were significant factors in the development of sequelae, with multiple possibly confounding factors (e.g., age, sex) not statistically significant. Multivariable logistic regression analyses with neuroleptic dose as five discrete dose ranges or actual dose did not show an association between development of sequelae and dose. Database limitations notwithstanding, the lack of apparent impact of serum lithium level on the development of sequelae in patients treated with haloperidol contrasts notably with results in the Li and LiNeuro groups. These findings may suggest a possible effect of pharmacodynamic factors in lithium/neuroleptic combination therapy.

Genetic prediction of type 2 diabetes using deep neural network.

PubMed

Kim, J; Kim, J; Kwak, M J; Bajaj, M

2018-04-01

Type 2 diabetes (T2DM) has strong heritability but genetic models to explain heritability have been challenging. We tested deep neural network (DNN) to predict T2DM using the nested case-control study of Nurses' Health Study (3326 females, 45.6% T2DM) and Health Professionals Follow-up Study (2502 males, 46.5% T2DM). We selected 96, 214, 399, and 678 single-nucleotide polymorphism (SNPs) through Fisher's exact test and L1-penalized logistic regression. We split each dataset randomly in 4:1 to train prediction models and test their performance. DNN and logistic regressions showed better area under the curve (AUC) of ROC curves than the clinical model when 399 or more SNPs included. DNN was superior than logistic regressions in AUC with 399 or more SNPs in male and 678 SNPs in female. Addition of clinical factors consistently increased AUC of DNN but failed to improve logistic regressions with 214 or more SNPs. In conclusion, we show that DNN can be a versatile tool to predict T2DM incorporating large numbers of SNPs and clinical information. Limitations include a relatively small number of the subjects mostly of European ethnicity. Further studies are warranted to confirm and improve performance of genetic prediction models using DNN in different ethnic groups. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

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).

Estimation of Recurrence of Colorectal Adenomas with Dependent Censoring Using Weighted Logistic Regression

PubMed Central

Hsu, Chiu-Hsieh; Li, Yisheng; Long, Qi; Zhao, Qiuhong; Lance, Peter

2011-01-01

In colorectal polyp prevention trials, estimation of the rate of recurrence of adenomas at the end of the trial may be complicated by dependent censoring, that is, time to follow-up colonoscopy and dropout may be dependent on time to recurrence. Assuming that the auxiliary variables capture the dependence between recurrence and censoring times, we propose to fit two working models with the auxiliary variables as covariates to define risk groups and then extend an existing weighted logistic regression method for independent censoring to each risk group to accommodate potential dependent censoring. In a simulation study, we show that the proposed method results in both a gain in efficiency and reduction in bias for estimating the recurrence rate. We illustrate the methodology by analyzing a recurrent adenoma dataset from a colorectal polyp prevention trial. PMID:22065985

Screening for ketosis using multiple logistic regression based on milk yield and composition.

PubMed

Kayano, Mitsunori; Kataoka, Tomoko

2015-11-01

Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF - 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively.

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.

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.

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.

Outdoor Recreation Constraints: An Examination of Race, Gender, and Rural Dwelling

Treesearch

Cassandra Y. Johnson; J. Michael Bowker; H. Ken Cordell

2001-01-01

We assess whether traditionally marginalized groups in American society (African-Americans, women, rural dwellers) perceive more constraints to outdoor recreation participation than other groups. A series of logistic regressions are applied to a national recreation survey and used to model the probability that individuals perceive certain constraints to...

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.

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.

Laboratory test variables useful for distinguishing upper from lower gastrointestinal bleeding.

PubMed

Tomizawa, Minoru; Shinozaki, Fuminobu; Hasegawa, Rumiko; Shirai, Yoshinori; Motoyoshi, Yasufumi; Sugiyama, Takao; Yamamoto, Shigenori; Ishige, Naoki

2015-05-28

To distinguish upper from lower gastrointestinal (GI) bleeding. Patient records between April 2011 and March 2014 were analyzed retrospectively (3296 upper endoscopy, and 1520 colonoscopy). Seventy-six patients had upper GI bleeding (Upper group) and 65 had lower GI bleeding (Lower group). Variables were compared between the groups using one-way analysis of variance. Logistic regression was performed to identify variables significantly associated with the diagnosis of upper vs lower GI bleeding. Receiver-operator characteristic (ROC) analysis was performed to determine the threshold value that could distinguish upper from lower GI bleeding. Hemoglobin (P = 0.023), total protein (P = 0.0002), and lactate dehydrogenase (P = 0.009) were significantly lower in the Upper group than in the Lower group. Blood urea nitrogen (BUN) was higher in the Upper group than in the Lower group (P = 0.0065). Logistic regression analysis revealed that BUN was most strongly associated with the diagnosis of upper vs lower GI bleeding. ROC analysis revealed a threshold BUN value of 21.0 mg/dL, with a specificity of 93.0%. The threshold BUN value for distinguishing upper from lower GI bleeding was 21.0 mg/dL.

Laboratory test variables useful for distinguishing upper from lower gastrointestinal bleeding

PubMed Central

Tomizawa, Minoru; Shinozaki, Fuminobu; Hasegawa, Rumiko; Shirai, Yoshinori; Motoyoshi, Yasufumi; Sugiyama, Takao; Yamamoto, Shigenori; Ishige, Naoki

2015-01-01

AIM: To distinguish upper from lower gastrointestinal (GI) bleeding. METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively (3296 upper endoscopy, and 1520 colonoscopy). Seventy-six patients had upper GI bleeding (Upper group) and 65 had lower GI bleeding (Lower group). Variables were compared between the groups using one-way analysis of variance. Logistic regression was performed to identify variables significantly associated with the diagnosis of upper vs lower GI bleeding. Receiver-operator characteristic (ROC) analysis was performed to determine the threshold value that could distinguish upper from lower GI bleeding. RESULTS: Hemoglobin (P = 0.023), total protein (P = 0.0002), and lactate dehydrogenase (P = 0.009) were significantly lower in the Upper group than in the Lower group. Blood urea nitrogen (BUN) was higher in the Upper group than in the Lower group (P = 0.0065). Logistic regression analysis revealed that BUN was most strongly associated with the diagnosis of upper vs lower GI bleeding. ROC analysis revealed a threshold BUN value of 21.0 mg/dL, with a specificity of 93.0%. CONCLUSION: The threshold BUN value for distinguishing upper from lower GI bleeding was 21.0 mg/dL. PMID:26034359

Polymorphisms within the FANCA gene associate with premature ovarian failure in Korean women.

PubMed

Pyun, Jung-A; Kim, Sunshin; Cha, Dong Hyun; Kwack, KyuBum

2014-05-01

This study investigated whether polymorphisms within the Fanconi anemia complementation group A (FANCA) gene contribute to the increased risk of premature ovarian failure (POF) in Korean women. Ninety-eight women with POF and 218 controls participated in this study. Genomic DNA from peripheral blood was isolated, and GoldenGate genotyping assay was used to identify single nucleotide polymorphisms (SNPs) within the FANCA gene. Two significant SNPs (rs1006547 and rs2239359; P < 0.05) were identified by logistic regression analysis, but results were insignificant after Bonferroni correction. Six SNPs formed a linkage disequilibrium block, and three main haplotypes were found. Two of three haplotypes (AAAGAA and GGGAGG) distributed highly in the POF group, whereas the remaining haplotype (GGAAGG) distributed highly in the control group by logistic regression analysis (highest odds ratio, 2.515; 95% CI, 1.515-4.175; P = 0.00036). Our observations suggest that genetic variations in the FANCA gene may increase the risk for POF in Korean women.

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.

Mathematical models application for mapping soils spatial distribution on the example of the farm from the North of Udmurt Republic of Russia

NASA Astrophysics Data System (ADS)

Dokuchaev, P. M.; Meshalkina, J. L.; Yaroslavtsev, A. M.

2018-01-01

Comparative analysis of soils geospatial modeling using multinomial logistic regression, decision trees, random forest, regression trees and support vector machines algorithms was conducted. The visual interpretation of the digital maps obtained and their comparison with the existing map, as well as the quantitative assessment of the individual soil groups detection overall accuracy and of the models kappa showed that multiple logistic regression, support vector method, and random forest models application with spatial prediction of the conditional soil groups distribution can be reliably used for mapping of the study area. It has shown the most accurate detection for sod-podzolics soils (Phaeozems Albic) lightly eroded and moderately eroded soils. In second place, according to the mean overall accuracy of the prediction, there are sod-podzolics soils - non-eroded and warp one, as well as sod-gley soils (Umbrisols Gleyic) and alluvial soils (Fluvisols Dystric, Umbric). Heavy eroded sod-podzolics and gray forest soils (Phaeozems Albic) were detected by methods of automatic classification worst of all.

Does the EDI Measure School Readiness in the Same Way across Different Groups of Children?

ERIC Educational Resources Information Center

Guhn, Martin; Gadermann, Anne; Zumbo, Bruno D.

2007-01-01

The present study investigates whether the Early Development Instrument (Offord & Janus, 1999) measures school readiness similarly across different groups of children. We employ ordinal logistic regression to investigate differential item functioning, a method of examining measurement bias. For 40,000 children, our analysis compares groups…

Predicting site locations for biomass using facilities with Bayesian methods

Treesearch

Timothy M. Young; James H. Perdue; Xia Huang

2017-01-01

Logistic regression models combined with Bayesian inference were developed to predict locations and quantify factors that influence the siting of biomass-using facilities that use woody biomass in the Southeastern United States. Predictions were developed for two groups of mills, one representing larger capacity mills similar to pulp and paper mills (Group II...

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.

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.

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.

Screening for ketosis using multiple logistic regression based on milk yield and composition

PubMed Central

KAYANO, Mitsunori; KATAOKA, Tomoko

2015-01-01

Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF − 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively. PMID:26074408

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.

2014-01-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W

2015-02-01

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

A case-control study of determinants for high and low dental caries prevalence in Nevada youth

PubMed Central

2010-01-01

Background The main purpose of this study was to compare the 30% of Nevada Youth who presented with the highest Decayed Missing and Filled Teeth (DMFT) index to a cohort who were caries free and to national NHANES data. Secondly, to explore the factors associated with higher caries prevalence in those with the highest DMFT scores compared to the caries-free group. Methods Over 4000 adolescents between ages 12 and 19 (Case Group: N = 2124; Control Group: N = 2045) received oral health screenings conducted in public/private middle and high schools in Nevada in 2008/2009 academic year. Caries prevalence was computed (Untreated decay scores [D-Score] and DMFT scores) for the 30% of Nevada Youth who presented with the highest DMFT score (case group) and compared to the control group (caries-free) and to national averages. Bivariate and multivariate logistic regression was used to analyze the relationship between selected variables and caries prevalence. Results A majority of the sample was non-Hispanic (62%), non-smokers (80%), and had dental insurance (70%). With the exception of gender, significant differences in mean D-scores were found in seven of the eight variables. All variables produced significant differences between the case and control groups in mean DMFT Scores. With the exception of smoking status, there were significant differences in seven of the eight variables in the bivariate logistic regression. All of the independent variables remained in the multivariate logistic regression model contributing significantly to over 40% of the variation in the increased DMFT status. The strongest predictors for the high DMFT status were racial background, age, fluoridated community, and applied sealants respectively. Gender, second hand smoke, insurance status, and tobacco use were significant, but to a lesser extent. Conclusions Findings from this study will aid in creating educational programs and other primary and secondary interventions to help promote oral health for Nevada youth, especially focusing on the subgroup that presents with the highest mean DMFT scores. PMID:21067620

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.

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.

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

NASA Astrophysics Data System (ADS)

Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

2017-07-01

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

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.

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

PubMed

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

2017-05-01

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

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...

The Efficacy of Two Improvement-over-Chance Effect Sizes for Two-Group Univariate Comparisons under Variance Heterogeneity and Nonnormality.

ERIC Educational Resources Information Center

Hess, Brian; Olejnik, Stephen; Huberty, Carl J.

2001-01-01

Studied the efficacy of two improvement-over-chance or "I" effect sizes derived from predictive discriminant analysis and logistic regression analysis for two-group univariate mean comparisons through simulation. Discusses the ways in which the usefulness of each of the indices depends on the population characteristics. (SLD)

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

[Comparison of arterial stiffness in non-hypertensive and hypertensive population of various age groups].

PubMed

Zhang, Y J; Wu, S L; Li, H Y; Zhao, Q H; Ning, C H; Zhang, R Y; Yu, J X; Li, W; Chen, S H; Gao, J S

2018-01-24

Objective: To investigate the impact of blood pressure and age on arterial stiffness in general population. Methods: Participants who took part in 2010, 2012 and 2014 Kailuan health examination were included. Data of brachial ankle pulse wave velocity (baPWV) examination were analyzed. According to the WHO criteria of age, participants were divided into 3 age groups: 18-44 years group ( n= 11 608), 45-59 years group ( n= 12 757), above 60 years group ( n= 5 002). Participants were further divided into hypertension group and non-hypertension group according to the diagnostic criteria for hypertension (2010 Chinese guidelines for the managemengt of hypertension). Multiple linear regression analysis was used to analyze the association between systolic blood pressure (SBP) with baPWV in the total participants and then stratified by age groups. Multivariate logistic regression model was used to analyze the influence of blood pressure on arterial stiffness (baPWV≥1 400 cm/s) of various groups. Results: (1)The baseline characteristics of all participants: 35 350 participants completed 2010, 2012 and 2014 Kailuan examinations and took part in baPWV examination. 2 237 participants without blood pressure measurement values were excluded, 1 569 participants with history of peripheral artery disease were excluded, we also excluded 1 016 participants with history of cardiac-cerebral vascular disease. Data from 29 367 participants were analyzed. The age was (48.0±12.4) years old, 21 305 were males (72.5%). (2) Distribution of baPWV in various age groups: baPWV increased with aging. In non-hypertension population, baPWV in 18-44 years group, 45-59 years group, above 60 years group were as follows: 1 299.3, 1 428.7 and 1 704.6 cm/s, respectively. For hypertension participants, the respective values of baPWV were: 1 498.4, 1 640.7 and 1 921.4 cm/s. BaPWV was significantly higher in hypertension group than non-hypertension group of respective age groups ( P< 0.05). (3) Multiple linear regression analysis defined risk factors of baPWV: Multivariate linear regression analysis showed that baPWV was positively correlated with SBP( t= 39.30, P< 0.001), and same results were found in the sub-age groups ( t -value was 37.72, 27.30, 9.15, all P< 0.001, respectively) after adjustment for other confounding factors, including age, sex, pulse pressure(PP), body mass index (BMI), fasting blood glucose (FBG), total cholesterol (TC), smoking, drinking, physical exercise, antihypertensive medications, lipid-lowering medication. (4) Multivariate logistic regression analysis of baPWV-related factors: After adjustment for other confounding factors, including age, sex, PP, BMI, FBG, TC, smoking, drinking, physical exercise, antihypertensive medication, lipid-lowering medication, multivariate logistic regression analysis showed that risks for increased arterial stiffness in hypertension group were higher than those in non-hypertension group, the OR in participants with hypertension was 2.54 (2.35-2.74) in the total participants, and same results were also found in sub-age groups, the OR s were 3.22(2.86-3.63), 2.48(2.23-2.76), and 1.91(1.42-2.56), respectively, in each sub-age group. Conclusion: SBP is positively related to arterial stiffness in different age groups, and hypertension is a risk factor for increased arterial stiffness in different age groups. Clinical Trial Registry Chinese Clinical Trial Registry, ChiCTR-TNC-11001489.

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.

Predictors of Smokeless Tobacco Abstinence

ERIC Educational Resources Information Center

Ebbert, Jon O.; Glover, Elbert D.; Shinozaki, Eri; Schroeder, Darrell R.; Dale, Lowell C.

2008-01-01

Objectives: To investigate predictors of tobacco abstinence among smokeless tobacco (ST) users. Methods: Logistic regression analyses assessed characteristics associated with tobacco abstinence among ST users receiving bupropion SR. Results: Older age was associated with increased tobacco abstinence in both placebo and bupropion SR groups at end…

Beyond Reading Alone: The Relationship Between Aural Literacy And Asthma Management

PubMed Central

Rosenfeld, Lindsay; Rudd, Rima; Emmons, Karen M.; Acevedo-García, Dolores; Martin, Laurie; Buka, Stephen

2010-01-01

Objectives To examine the relationship between literacy and asthma management with a focus on the oral exchange. Methods Study participants, all of whom reported asthma, were drawn from the New England Family Study (NEFS), an examination of links between education and health. NEFS data included reading, oral (speaking), and aural (listening) literacy measures. An additional survey was conducted with this group of study participants related to asthma issues, particularly asthma management. Data analysis focused on bivariate and multivariable logistic regression. Results In bivariate logistic regression models exploring aural literacy, there was a statistically significant association between those participants with lower aural literacy skills and less successful asthma management (OR:4.37, 95%CI:1.11, 17.32). In multivariable logistic regression analyses, controlling for gender, income, and race in separate models (one-at-a-time), there remained a statistically significant association between those participants with lower aural literacy skills and less successful asthma management. Conclusion Lower aural literacy skills seem to complicate asthma management capabilities. Practice Implications Greater attention to the oral exchange, in particular the listening skills highlighted by aural literacy, as well as other related literacy skills may help us develop strategies for clear communication related to asthma management. PMID:20399060

Correlates of consistent condom use among men who have sex with men recruited through the Internet in Huzhou city: a cross-sectional survey.

PubMed

Jin, Meihua; Yang, Zhongrong; Dong, Zhengquan; Han, Jiankang

2013-12-01

There is growing evidence that men who have sex with men (MSM) are currently a group at high risk of HIV infection in China. Our study aims to know the factors affecting consistent condom use among MSM recruited through the internet in Huzhou city. An anonymous cross-sectional study was conducted by recruiting 410 MSM living in Huzhou city via the Internet. The socio-demographic profiles (age, education level, employment status, etc.) and sexual risk behaviors of the respondents were investigated. Bivariate logistic regression analyses were performed to compare the differences between consistent condom users and inconsistent condom users. Variables with significant bivariate between groups' differences were used as candidate variables in a stepwise multivariate logistic regression model. All statistical analyses were performed using SPSS for Windows 17.0, and a p value < 0.05 was considered to be statistically significant. According to their condom use, sixty-eight respondents were classified into two groups. One is consistent condom users, and the other is inconsistent condom users. Multivariate logistic regression showed that respondents who had a comprehensive knowledge of HIV (OR = 4.08, 95% CI: 1.85-8.99), who had sex with male sex workers (OR = 15.30, 95% CI: 5.89-39.75) and who had not drunk alcohol before sex (OR = 3.10, 95% CI: 1.38-6.95) were more likely to be consistent condom users. Consistent condom use among MSM was associated with comprehensive knowledge of HIV and a lack of alcohol use before sexual contact. As a result, reducing alcohol consumption and enhancing education regarding the risks of HIV among sexually active MSM would be effective in preventing of HIV transmission.

Logistic regression analysis of the risk factors of anastomotic fistula after radical resection of esophageal-cardiac cancer.

PubMed

Huang, Jinxi; Zhou, Yi; Wang, Chenghu; Yuan, Weiwei; Zhang, Zhandong; Chen, Beibei; Zhang, Xiefu

2017-11-01

This study was conducted to investigate the risk factors of anastomotic fistula after the radical resection of esophageal-cardiac cancer. Five hundred and forty-four esophageal-cardiac cancer patients who underwent surgery and had complete clinical data were included in the study. Fifty patients diagnosed with postoperative anastomotic fistula were considered the case group and the remaining 494 subjects who did not develop postoperative anastomotic fistula were considered the control. The potential risk factors for anastomotic fistula, such as age, gender, diabetes history, smoking history, were collected and compared between the groups. Statistically significant variables were substituted into logistic regression to further evaluate the independent risk factors for postoperative anastomotic fistulas in esophageal-cardiac cancer. The incidence of anastomotic fistulas was 9.2% (50/544). Logistic regression analysis revealed that female gender (P < 0.05), laparoscopic surgery (P < 0.05), decreased postoperative albumin (P < 0.05), and postoperative renal dysfunction (P < 0.05) were independent risk factors for anastomotic fistulas in patients who received surgery for esophageal-cardiac cancer. Of the 50 anastomotic fistulas, 16 cases were small fistulas, which were only discovered by conventional imaging examination and not presenting clinical symptoms. All of the anastomotic fistulas occurred within seven days after surgery. Five of the patients with anastomotic fistulas underwent a second surgery and three died. Female patients with esophageal-cardiac cancer treated with endoscopic surgery and suffering from postoperative hypoproteinemia and renal dysfunction were susceptible to postoperative anastomotic fistula. © 2017 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.

Nomogram for prediction of level 2 axillary lymph node metastasis in proven level 1 node-positive breast cancer patients.

PubMed

Jiang, Yanlin; Xu, Hong; Zhang, Hao; Ou, Xunyan; Xu, Zhen; Ai, Liping; Sun, Lisha; Liu, Caigang

2017-09-22

The current management of the axilla in level 1 node-positive breast cancer patients is axillary lymph node dissection regardless of the status of the level 2 axillary lymph nodes. The goal of this study was to develop a nomogram predicting the probability of level 2 axillary lymph node metastasis (L-2-ALNM) in patients with level 1 axillary node-positive breast cancer. We reviewed the records of 974 patients with pathology-confirmed level 1 node-positive breast cancer between 2010 and 2014 at the Liaoning Cancer Hospital and Institute. The patients were randomized 1:1 and divided into a modeling group and a validation group. Clinical and pathological features of the patients were assessed with uni- and multivariate logistic regression. A nomogram based on independent predictors for the L-2-ALNM identified by multivariate logistic regression was constructed. Independent predictors of L-2-ALNM by the multivariate logistic regression analysis included tumor size, Ki-67 status, histological grade, and number of positive level 1 axillary lymph nodes. The areas under the receiver operating characteristic curve of the modeling set and the validation set were 0.828 and 0.816, respectively. The false-negative rates of the L-2-ALNM nomogram were 1.82% and 7.41% for the predicted probability cut-off points of < 6% and < 10%, respectively, when applied to the validation group. Our nomogram could help predict L-2-ALNM in patients with level 1 axillary lymph node metastasis. Patients with a low probability of L-2-ALNM could be spared level 2 axillary lymph node dissection, thereby reducing postoperative morbidity.

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.

Contributions of sociodemographic factors to criminal behavior

PubMed Central

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

2016-01-01

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

Extension of the Peters–Belson method to estimate health disparities among multiple groups using logistic regression with survey data

PubMed Central

Li, Y.; Graubard, B. I.; Huang, P.; Gastwirth, J. L.

2015-01-01

Determining the extent of a disparity, if any, between groups of people, for example, race or gender, is of interest in many fields, including public health for medical treatment and prevention of disease. An observed difference in the mean outcome between an advantaged group (AG) and disadvantaged group (DG) can be due to differences in the distribution of relevant covariates. The Peters–Belson (PB) method fits a regression model with covariates to the AG to predict, for each DG member, their outcome measure as if they had been from the AG. The difference between the mean predicted and the mean observed outcomes of DG members is the (unexplained) disparity of interest. We focus on applying the PB method to estimate the disparity based on binary/multinomial/proportional odds logistic regression models using data collected from complex surveys with more than one DG. Estimators of the unexplained disparity, an analytic variance–covariance estimator that is based on the Taylor linearization variance–covariance estimation method, as well as a Wald test for testing a joint null hypothesis of zero for unexplained disparities between two or more minority groups and a majority group, are provided. Simulation studies with data selected from simple random sampling and cluster sampling, as well as the analyses of disparity in body mass index in the National Health and Nutrition Examination Survey 1999–2004, are conducted. Empirical results indicate that the Taylor linearization variance–covariance estimation is accurate and that the proposed Wald test maintains the nominal level. PMID:25382235

Does Group-Level Commitment Predict Employee Well-Being?: A Prospective Analysis.

PubMed

Clausen, Thomas; Christensen, Karl Bang; Nielsen, Karina

2015-11-01

To investigate the links between group-level affective organizational commitment (AOC) and individual-level psychological well-being, self-reported sickness absence, and sleep disturbances. A total of 5085 care workers from 301 workgroups in the Danish eldercare services participated in both waves of the study (T1 [2005] and T2 [2006]). The three outcomes were analyzed using linear multilevel regression analysis, multilevel Poisson regression analysis, and multilevel logistic regression analysis, respectively. Group-level AOC (T1) significantly predicted individual-level psychological well-being, self-reported sickness absence, and sleep disturbances (T2). The association between group-level AOC (T1) and psychological well-being (T2) was fully mediated by individual-level AOC (T1), and the associations between group-level AOC (T1) and self-reported sickness absence and sleep disturbances (T2) were partially mediated by individual-level AOC (T1). Group-level AOC is an important predictor of employee well-being in contemporary health care organizations.

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

PubMed

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

2018-04-01

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

Modeling health survey data with excessive zero and K responses.

PubMed

Lin, Ting Hsiang; Tsai, Min-Hsiao

2013-04-30

Zero-inflated Poisson regression is a popular tool used to analyze data with excessive zeros. Although much work has already been performed to fit zero-inflated data, most models heavily depend on special features of the individual data. To be specific, this means that there is a sizable group of respondents who endorse the same answers making the data have peaks. In this paper, we propose a new model with the flexibility to model excessive counts other than zero, and the model is a mixture of multinomial logistic and Poisson regression, in which the multinomial logistic component models the occurrence of excessive counts, including zeros, K (where K is a positive integer) and all other values. The Poisson regression component models the counts that are assumed to follow a Poisson distribution. Two examples are provided to illustrate our models when the data have counts containing many ones and sixes. As a result, the zero-inflated and K-inflated models exhibit a better fit than the zero-inflated Poisson and standard Poisson regressions. Copyright © 2012 John Wiley & Sons, Ltd.

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.

Neuropsychological tests for predicting cognitive decline in older adults

PubMed Central

Baerresen, Kimberly M; Miller, Karen J; Hanson, Eric R; Miller, Justin S; Dye, Richelin V; Hartman, Richard E; Vermeersch, David; Small, Gary W

2015-01-01

Summary Aim To determine neuropsychological tests likely to predict cognitive decline. Methods A sample of nonconverters (n = 106) was compared with those who declined in cognitive status (n = 24). Significant univariate logistic regression prediction models were used to create multivariate logistic regression models to predict decline based on initial neuropsychological testing. Results Rey–Osterrieth Complex Figure Test (RCFT) Retention predicted conversion to mild cognitive impairment (MCI) while baseline Buschke Delay predicted conversion to Alzheimer’s disease (AD). Due to group sample size differences, additional analyses were conducted using a subsample of demographically matched nonconverters. Analyses indicated RCFT Retention predicted conversion to MCI and AD, and Buschke Delay predicted conversion to AD. Conclusion Results suggest RCFT Retention and Buschke Delay may be useful in predicting cognitive decline. PMID:26107318

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.

Maximal bite force, facial morphology and sucking habits in young children with functional posterior crossbite.

PubMed

Castelo, Paula Midori; Gavião, Maria Beatriz Duarte; Pereira, Luciano José; Bonjardim, Leonardo Rigoldi

2010-01-01

The maintenance of normal conditions of the masticatory function is determinant for the correct growth and development of its structures. Thus, the aims of this study were to evaluate the influence of sucking habits on the presence of crossbite and its relationship with maximal bite force, facial morphology and body variables in 67 children of both genders (3.5-7 years) with primary or early mixed dentition. The children were divided in four groups: primary-normocclusion (PN, n=19), primary-crossbite (PC, n=19), mixed-normocclusion (MN, n=13), and mixed-crossbite (MC, n=16). Bite force was measured with a pressurized tube, and facial morphology was determined by standardized frontal photographs: AFH (anterior face height) and BFW (bizygomatic facial width). It was observed that MC group showed lower bite force than MN, and AFH/BFW was significantly smaller in PN than PC (t-test). Weight and height were only significantly correlated with bite force in PC group (Pearson's correlation test). In the primary dentition, AFH/BFW and breast-feeding (at least six months) were positive and negatively associated with crossbite, respectively (multiple logistic regression). In the mixed dentition, breast-feeding and bite force showed negative associations with crossbite (univariate regression), while nonnutritive sucking (up to 3 years) associated significantly with crossbite in all groups (multiple logistic regression). In the studied sample, sucking habits played an important role in the etiology of crossbite, which was associated with lower bite force and long-face tendency.

Intergroup Relations and Predictors of Immigrant Experience

ERIC Educational Resources Information Center

Danso, Kofi; Lum, Terry

2013-01-01

Using survey data from 1,036 participants, which included 4 immigrant groups, we examined the factors that influence immigrants' experiences as they interact with nonimmigrant Americans. Logistic and multinomial regression results indicate that non-European immigrants were more likely to report negative experiences with Americans. The odds of…

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.

Basic Diagnosis and Prediction of Persistent Contrail Occurrence using High-resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

NASA Technical Reports Server (NTRS)

Duda, David P.; Minnis, Patrick

2009-01-01

Previous studies have shown that probabilistic forecasting may be a useful method for predicting persistent contrail formation. A probabilistic forecast to accurately predict contrail formation over the contiguous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and from the Rapid Update Cycle (RUC) as well as GOES water vapor channel measurements, combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The mean accuracies for both the SURFACE and OUTBREAK models typically exceeded 75 percent when based on the RUC or ARPS analysis data, but decreased when the logistic models were derived from ARPS forecast data.

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.

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

PubMed Central

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

2017-01-01

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

Prevalence and risk factors of non-carious cervical lesions related to occupational exposure to acid mists.

PubMed

Bomfim, Rafael Aiello; Crosato, Edgard; Mazzilli, Luiz Eugênio Nigro; Frias, Antonio Carlos

2015-01-01

This study evaluates the prevalence and risk factors of non-carious cervical lesions (NCCLs) in a Brazilian population of workers exposed and non-exposed to acid mists and chemical products. One hundred workers (46 exposed and 54 non-exposed) were evaluated in a Centro de Referência em Saúde do Trabalhador - CEREST (Worker's Health Reference Center). The workers responded to questionnaires regarding their personal information and about alcohol consumption and tobacco use. A clinical examination was conducted to evaluate the presence of NCCLs, according to WHO parameters. Statistical analyses were performed by unconditional logistic regression and multiple linear regression, with the critical level of p < 0.05. NCCLs were significantly associated with age groups (18-34, 35-44, 45-68 years). The unconditional logistic regression showed that the presence of NCCLs was better explained by age group (OR = 4.04; CI 95% 1.77-9.22) and occupational exposure to acid mists and chemical products (OR = 3.84; CI 95% 1.10-13.49), whereas the linear multiple regression revealed that NCCLs were better explained by years of smoking (p = 0.01) and age group (p = 0.04). The prevalence of NCCLs in the study population was particularly high (76.84%), and the risk factors for NCCLs were age, exposure to acid mists and smoking habit. Controlling risk factors through preventive and educative measures, allied to the use of personal protective equipment to prevent the occupational exposure to acid mists, may contribute to minimizing the prevalence of NCCLs.

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.

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

[Effect of different anesthetic methods on postoperative outcomes in elderly patients undergoing hip fracture surgery].

PubMed

Wei, B; Zhang, H; Xu, M; Li, M; Wang, J; Zhang, L P; Guo, X Y; Zhao, Y M; Zhou, F

2017-12-18

To investigate the effect of general or regional anesthesia on postoperative cardiopulmonary complications and inpatient mortality after hip fracture surgery in elderly patients. A retrospective analysis was conducted according to the medical records of 572 elderly patients with hip fractures admitted to our hospital from January 1, 2005 to December 31, 2014. The age, gender, preoperative comorbidities, length of preoperative bedridden time, mechanism of injury, surgical types, anesthetic methods, major postoperative complications and inpatient mortality were recorded. Multivariate Logistic regression analysis was applied to analyze the impact of different anesthetic methods on inpatient mortality in these patients. Of the 572 patients, 392 (68.5%) received regional anesthesia. Inpatient death occurred in 8 (8/572, mortality: 1.4%), including 5 cases of RA group (5/392, mortality: 1.3%) and 3 cases of GA group (3/180, mortality: 1.7%). There was no statistically significant difference between the two groups in inpatient mortality (P>0.05). Multiple Logistic regression analysis showed that gender (odds ratio: 0.18, 95% CI: 0.03-1.05, P=0.057), age (odds ratio: 1.22, 95% CI: 1.07-1.38, P=0.002), preoperative pulmonary comorbidities (odds ratio: 12.09, 95% CI: 2.28-64.12, P=0.003) and surgical types (odds ratio: 9.36, 95% CI: 1.34-64.26, P=0.024) were risk factors for inpatient mortality. Postoperative cardiovascular complications occurred in 36 patients (36/572, morbidity: 6.3%), with 19 patients in RA group (19/392, morbidity: 4.8%),and 17 patients in GA group (17/180, morbidity: 9.4%). Multiple Logistic regression analysis showed that age (odds ratio: 1.13, 95% CI: 1.07-1.19, P<0.001), hypertension (odds ratio: 2.72, 95% CI: 1.24-5.96, P=0.012) and preoperative cerebral comorbidities (odds ratio: 2.11, 95% CI: 0.99-4.52, P=0.054) were risk factors for postoperative cardiovascular complications. Postoperative pulmonary complications occurred in 56 patients (56/572, morbidity: 9.8%), with 19 patients in RA group (19/392, morbidity: 4.8%), and 37 patients in GA group (37/180, morbidity: 20.6%). Multiple Logistic regression analysis showed that age (odds ratio: 1.13, 95% CI: 1.07-1.19, P<0.001), preoperative pulmonary comorbidities (odds ratio: 2.89, 95% CI: 1.28-7.05, P=0.020), length of preoperative bedridden time (odds ratio: 1.11, 95% CI: 1.04-1.18, P=0.003) and anesthetic methods (odds ratio: 5.86, 95% CI: 2.98-11.53, P<0.001) were risk factors for postoperative pulmonary complications. General anesthesia may not affect the inpatient mortality after hip fracture surgery in elderly patients. Regional anesthesia is associated with a lower risk of pulmonary complications after surgical procedure compared with general anesthesia.

Logistic regression analysis of the risk factors of anastomotic fistula after radical resection of esophageal‐cardiac cancer

PubMed Central

Huang, Jinxi; Wang, Chenghu; Yuan, Weiwei; Zhang, Zhandong; Chen, Beibei; Zhang, Xiefu

2017-01-01

Background This study was conducted to investigate the risk factors of anastomotic fistula after the radical resection of esophageal‐cardiac cancer. Methods Five hundred and forty‐four esophageal‐cardiac cancer patients who underwent surgery and had complete clinical data were included in the study. Fifty patients diagnosed with postoperative anastomotic fistula were considered the case group and the remaining 494 subjects who did not develop postoperative anastomotic fistula were considered the control. The potential risk factors for anastomotic fistula, such as age, gender, diabetes history, smoking history, were collected and compared between the groups. Statistically significant variables were substituted into logistic regression to further evaluate the independent risk factors for postoperative anastomotic fistulas in esophageal‐cardiac cancer. Results The incidence of anastomotic fistulas was 9.2% (50/544). Logistic regression analysis revealed that female gender (P < 0.05), laparoscopic surgery (P < 0.05), decreased postoperative albumin (P < 0.05), and postoperative renal dysfunction (P < 0.05) were independent risk factors for anastomotic fistulas in patients who received surgery for esophageal‐cardiac cancer. Of the 50 anastomotic fistulas, 16 cases were small fistulas, which were only discovered by conventional imaging examination and not presenting clinical symptoms. All of the anastomotic fistulas occurred within seven days after surgery. Five of the patients with anastomotic fistulas underwent a second surgery and three died. Conclusion Female patients with esophageal‐cardiac cancer treated with endoscopic surgery and suffering from postoperative hypoproteinemia and renal dysfunction were susceptible to postoperative anastomotic fistula. PMID:28940985

Differentiating major depressive disorder in youths with attention deficit hyperactivity disorder.

PubMed

Diler, Rasim Somer; Daviss, W Burleson; Lopez, Adriana; Axelson, David; Iyengar, Satish; Birmaher, Boris

2007-09-01

Youths with attention deficit hyperactivity disorders (ADHD) frequently have comorbid major depressive disorders (MDD) sharing overlapping symptoms. Our objective was to examine which depressive symptoms best discriminate MDD among youths with ADHD. One-hundred-eleven youths with ADHD (5.2-17.8 years old) and their parents completed interviews with the K-SADS-PL and respective versions of the child or the parent Mood and Feelings Questionnaire (MFQ-C, MFQ-P). Controlling for group differences, logistic regression was used to calculate odds ratios reflecting the accuracy with which various depressive symptoms on the MFQ-C or MFQ-P discriminated MDD. Stepwise logistic regression then identified depressive symptoms that best discriminated the groups with and without MDD, using cross-validated misclassification rate as the criterion. Symptoms that discriminated youths with MDD (n=18) from those without MDD (n=93) were 4 of 6 mood/anhedonia symptoms, all 14 depressed cognition symptoms, and only 3 of 11 physical/vegetative symptoms. Mild irritability, miserable/unhappy moods, and symptoms related to sleep, appetite, energy levels and concentration did not discriminate MDD. A stepwise logistic regression correctly classified 89% of the comorbid MDD subjects, with only age, anhedonia at school, thoughts about killing self, thoughts that bad things would happen, and talking more slowly remaining in the final model. Results of this study may not generalize to community samples because subjects were drawn largely from a university-based outpatient psychiatric clinic. These findings stress the importance of social withdrawal, anhedonia, depressive cognitions, suicidal thoughts, and psychomotor retardation when trying to identify MDD among ADHD youths.

Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

PubMed

Sun, Hokeun; Wang, Shuang

2013-05-30

The matched case-control designs are commonly used to control for potential confounding factors in genetic epidemiology studies especially epigenetic studies with DNA methylation. Compared with unmatched case-control studies with high-dimensional genomic or epigenetic data, there have been few variable selection methods for matched sets. In an earlier paper, we proposed the penalized logistic regression model for the analysis of unmatched DNA methylation data using a network-based penalty. However, for popularly applied matched designs in epigenetic studies that compare DNA methylation between tumor and adjacent non-tumor tissues or between pre-treatment and post-treatment conditions, applying ordinary logistic regression ignoring matching is known to bring serious bias in estimation. In this paper, we developed a penalized conditional logistic model using the network-based penalty that encourages a grouping effect of (1) linked Cytosine-phosphate-Guanine (CpG) sites within a gene or (2) linked genes within a genetic pathway for analysis of matched DNA methylation data. In our simulation studies, we demonstrated the superiority of using conditional logistic model over unconditional logistic model in high-dimensional variable selection problems for matched case-control data. We further investigated the benefits of utilizing biological group or graph information for matched case-control data. We applied the proposed method to a genome-wide DNA methylation study on hepatocellular carcinoma (HCC) where we investigated the DNA methylation levels of tumor and adjacent non-tumor tissues from HCC patients by using the Illumina Infinium HumanMethylation27 Beadchip. Several new CpG sites and genes known to be related to HCC were identified but were missed by the standard method in the original paper. Copyright © 2012 John Wiley & Sons, Ltd.

Comparing Revictimization in Two Groups of Marginalized Women

ERIC Educational Resources Information Center

Tusher, Chantal Poister; Cook, Sarah L.

2010-01-01

This study examines physical and sexual revictimization in a random sample of incarcerated and poor, urban, nonincarcerated women using multiple measures of physical and sexual child abuse. Researchers used hierarchical logistic regression to compare rates of revictimization and the strength of the association between child abuse and adult…

Vocational Rehabilitation Service Patterns and Outcomes for Individuals with Autism of Different Ages

ERIC Educational Resources Information Center

Chen, June L.; Sung, Connie; Pi, Sukyeong

2015-01-01

Young adults with autism spectrum disorders (ASD) often experience employment difficulties. Using Rehabilitation Service Administration data (RSA-911), this study investigated the service patterns and factors related to the employment outcomes of individuals with ASD in different age groups. Hierarchical logistic regression analyses were conducted…

Predictors of Employment Outcomes for State-Federal Vocational Rehabilitation Consumers with HIV/AIDS

ERIC Educational Resources Information Center

Jung, Youngoh; Schaller, James; Bellini, James

2010-01-01

In this study, the authors investigated the effects of demographic, medical, and vocational rehabilitation service variables on employment outcomes of persons living with HIV/AIDS. Binary logistic regression analyses were conducted to determine predictors of employment outcomes using two groups drawn from Rehabilitation Services Administration…

Evidence for Specificity of Motor Impairments in Catching and Balance in Children with Autism

ERIC Educational Resources Information Center

Ament, Katarina; Mejia, Amanda; Buhlman, Rebecca; Erklin, Shannon; Caffo, Brian; Mostofsky, Stewart; Wodka, Ericka

2015-01-01

To evaluate evidence for motor impairment specificity in autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Children completed performance-based assessment of motor functioning (Movement Assessment Battery for Children: MABC-2). Logistic regression models were used to predict group membership. In the models…

The Effect of Religiosity and Campus Alcohol Culture on Collegiate Alcohol Consumption

ERIC Educational Resources Information Center

Wells, Gayle M.

2010-01-01

Religiosity and campus culture were examined in relationship to alcohol consumption among college students using reference group theory. Participants and Methods: College students (N = 530) at a religious college and at a state university complete questionnaires on alcohol use and religiosity. Statistical tests and logistic regression were…

An Examination of Perceived Constraints to Outdoor Recreation

Treesearch

G.T. Green; J.M. Bowker; X. Wang; H.K. Cordell; Cassandra Y. Johnson

2009-01-01

This study examines whether different social and marginalized groups in American society (minorities, women, rural dwellers, immigrants, low income, less educated) perceive more constraints or barriers to outdoor recreation participation than White middle-class males. Logistic regressions were applied to data from the National Survey on Recreation and the Environment...

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.

Logistic regression analysis of risk factors for postoperative recurrence of spinal tumors and analysis of prognostic factors.

PubMed

Zhang, Shanyong; Yang, Lili; Peng, Chuangang; Wu, Minfei

2018-02-01

The aim of the present study was to investigate the risk factors for postoperative recurrence of spinal tumors by logistic regression analysis and analysis of prognostic factors. In total, 77 male and 48 female patients with spinal tumor were selected in our hospital from January, 2010 to December, 2015 and divided into the benign (n=76) and malignant groups (n=49). All the patients underwent microsurgical resection of spinal tumors and were reviewed regularly 3 months after operation. The McCormick grading system was used to evaluate the postoperative spinal cord function. Data were subjected to statistical analysis. Of the 125 cases, 63 cases showed improvement after operation, 50 cases were stable, and deterioration was found in 12 cases. The improvement rate of patients with cervical spine tumor, which reached 56.3%, was the highest. Fifty-two cases of sensory disturbance, 34 cases of pain, 30 cases of inability to exercise, 26 cases of ataxia, and 12 cases of sphincter disorders were found after operation. Seventy-two cases (57.6%) underwent total resection, 18 cases (14.4%) received subtotal resection, 23 cases (18.4%) received partial resection, and 12 cases (9.6%) were only treated with biopsy/decompression. Postoperative recurrence was found in 57 cases (45.6%). The mean recurrence time of patients in the malignant group was 27.49±6.09 months, and the mean recurrence time of patients in the benign group was 40.62±4.34. The results were significantly different (P<0.001). Recurrence was found in 18 cases of the benign group and 39 cases of the malignant group, and results were significantly different (P<0.001). Tumor recurrence was shorter in patients with a higher McCormick grade (P<0.001). Recurrence was found in 13 patients with resection and all the patients with partial resection or biopsy/decompression. The results were significantly different (P<0.001). Logistic regression analysis of total resection-related factors showed that total resection should be the preferred treatment for patients with benign tumors, thoracic and lumbosacral tumors, and lower McCormick grade, as well as patients without syringomyelia and intramedullary tumors. Logistic regression analysis of recurrence-related factors revealed that the recurrence rate was relatively higher in patients with malignant, cervical, thoracic and lumbosacral, intramedullary tumors, and higher McCormick grade and patient received partial resection or biopsy. Tumor property, tumor location, McCormick grade, tumor resection, and intramedullary tumors are risk factors for the recurrence of spinal tumors. Clinical assessment of these risk factors may be helpful in selecting appropriate treatment strategies.

Logistic regression analysis of risk factors for postoperative recurrence of spinal tumors and analysis of prognostic factors

PubMed Central

Zhang, Shanyong; Yang, Lili; Peng, Chuangang; Wu, Minfei

2018-01-01

The aim of the present study was to investigate the risk factors for postoperative recurrence of spinal tumors by logistic regression analysis and analysis of prognostic factors. In total, 77 male and 48 female patients with spinal tumor were selected in our hospital from January, 2010 to December, 2015 and divided into the benign (n=76) and malignant groups (n=49). All the patients underwent microsurgical resection of spinal tumors and were reviewed regularly 3 months after operation. The McCormick grading system was used to evaluate the postoperative spinal cord function. Data were subjected to statistical analysis. Of the 125 cases, 63 cases showed improvement after operation, 50 cases were stable, and deterioration was found in 12 cases. The improvement rate of patients with cervical spine tumor, which reached 56.3%, was the highest. Fifty-two cases of sensory disturbance, 34 cases of pain, 30 cases of inability to exercise, 26 cases of ataxia, and 12 cases of sphincter disorders were found after operation. Seventy-two cases (57.6%) underwent total resection, 18 cases (14.4%) received subtotal resection, 23 cases (18.4%) received partial resection, and 12 cases (9.6%) were only treated with biopsy/decompression. Postoperative recurrence was found in 57 cases (45.6%). The mean recurrence time of patients in the malignant group was 27.49±6.09 months, and the mean recurrence time of patients in the benign group was 40.62±4.34. The results were significantly different (P<0.001). Recurrence was found in 18 cases of the benign group and 39 cases of the malignant group, and results were significantly different (P<0.001). Tumor recurrence was shorter in patients with a higher McCormick grade (P<0.001). Recurrence was found in 13 patients with resection and all the patients with partial resection or biopsy/decompression. The results were significantly different (P<0.001). Logistic regression analysis of total resection-related factors showed that total resection should be the preferred treatment for patients with benign tumors, thoracic and lumbosacral tumors, and lower McCormick grade, as well as patients without syringomyelia and intramedullary tumors. Logistic regression analysis of recurrence-related factors revealed that the recurrence rate was relatively higher in patients with malignant, cervical, thoracic and lumbosacral, intramedullary tumors, and higher McCormick grade and patient received partial resection or biopsy. Tumor property, tumor location, McCormick grade, tumor resection, and intramedullary tumors are risk factors for the recurrence of spinal tumors. Clinical assessment of these risk factors may be helpful in selecting appropriate treatment strategies. PMID:29434866

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.

Factors determining the smooth flow and the non-operative time in a one-induction room to one-operating room setting

PubMed Central

Mulier, Jan P; De Boeck, Liesje; Meulders, Michel; Beliën, Jeroen; Colpaert, Jan; Sels, Annabel

2015-01-01

Rationale, aims and objectives What factors determine the use of an anaesthesia preparation room and shorten non-operative time? Methods A logistic regression is applied to 18 751 surgery records from AZ Sint-Jan Brugge AV, Belgium, where each operating room has its own anaesthesia preparation room. Surgeries, in which the patient's induction has already started when the preceding patient's surgery has ended, belong to a first group where the preparation room is used as an induction room. Surgeries not fulfilling this property belong to a second group. A logistic regression model tries to predict the probability that a surgery will be classified into a specific group. Non-operative time is calculated as the time between end of the previous surgery and incision of the next surgery. A log-linear regression of this non-operative time is performed. Results It was found that switches in surgeons, being a non-elective surgery as well as the previous surgery being non-elective, increase the probability of being classified into the second group. Only a few surgery types, anaesthesiologists and operating rooms can be found exclusively in one of the two groups. Analysis of variance demonstrates that the first group has significantly lower non-operative times. Switches in surgeons, anaesthesiologists and longer scheduled durations of the previous surgery increases the non-operative time. A switch in both surgeon and anaesthesiologist strengthens this negative effect. Only a few operating rooms and surgery types influence the non-operative time. Conclusion The use of the anaesthesia preparation room shortens the non-operative time and is determined by several human and structural factors. PMID:25496600

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.

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

PubMed

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

2016-11-02

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

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.

ABO blood groups and susceptibility to brucellosis.

PubMed

Mohsenpour, Behzad; Hajibagheri, Katayon; Afrasiabian, Shahla; Ghaderi, Ebrahim; Ghasembegloo, Saeideh

2015-01-01

The relationship between blood groups and some infections such as norovirus, cholera, and malaria has been reported. Despite the importance of brucellosis, there is a lack of data on the relationship between blood groups and brucellosis. Thus, in this study, we examined the relationship between blood groups and brucellosis. In this case-control study, the blood groups of 100 patients with brucellosis and 200 healthy individuals were studied. Exclusion criteria for the control group consisted of a positive Coombs Wright test or a history of brucellosis. The chi-square test was used to compare qualitative variables between the two groups. The variables that met inclusion criteria for the regression model were entered into the logistic regression model. A total of 43% patients were female and 57% male; 27% were urban and 73% rural. Regression analysis showed that the likelihood of brucellosis infection was 6.26 times more in people with blood group AB than in those with blood group O (P<0.001). However, Rh type was not associated with brucellosis infection. Thus, there is a relationship between blood group and brucellosis. People with blood group AB were susceptible to brucellosis, but no difference was observed for brucellosis infection in terms of blood Rh type.

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.

Sociodemographic Differences in the Association Between Obesity and Stress: A Propensity Score-Matched Analysis from the Korean National Health and Nutrition Examination Survey (KNHANES).

PubMed

Mak, Kwok-Kei; Kim, Dae-Hwan; Leigh, J Paul

2015-01-01

Few population-based studies have used an econometric approach to understand the association between two cancer risk factors, obesity and stress. This study investigated sociodemographic differences in the association between obesity and stress among Korean adults (6,546 men and 8,473 women). Data were drawn from the Korean National Health and Nutrition Examination Survey for 2008, 2009, and 2010. Ordered logistic regression models and propensity score matching methods were used to examine the associations between obesity and stress, stratified by gender and age groups. In women, the stress level of the obese group was found to be 27.6% higher than the nonobese group in the ordered logistic regression; the obesity effect on stress was statistically significant in the propensity score-matched analysis. Corresponding evidence for the effect of obesity on stress was lacking among men. Participants who were young, well-educated, and working were more likely to report stress. In Korea, obesity causes stress in women but not in men. Young women are susceptible to a disproportionate level of stress. More cancer prevention programs targeting young and obese women are encouraged in developed Asian countries.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss.

PubMed

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. © 2016 S. Karger GmbH, Freiburg.

Predictors of Dropout by Female Obese Patients Treated with a Group Cognitive Behavioral Therapy to Promote Weight Loss

PubMed Central

Sawamoto, Ryoko; Nozaki, Takehiro; Furukawa, Tomokazu; Tanahashi, Tokusei; Morita, Chihiro; Hata, Tomokazu; Komaki, Gen; Sudo, Nobuyuki

2016-01-01

Objective To investigate predictors of dropout from a group cognitive behavioral therapy (CBT) intervention for overweight or obese women. Methods 119 overweight and obese Japanese women aged 25-65 years who attended an outpatient weight loss intervention were followed throughout the 7-month weight loss phase. Somatic characteristics, socioeconomic status, obesity-related diseases, diet and exercise habits, and psychological variables (depression, anxiety, self-esteem, alexithymia, parenting style, perfectionism, and eating attitude) were assessed at baseline. Significant variables, extracted by univariate statistical analysis, were then used as independent variables in a stepwise multiple logistic regression analysis with dropout as the dependent variable. Results 90 participants completed the weight loss phase, giving a dropout rate of 24.4%. The multiple logistic regression analysis demonstrated that compared to completers the dropouts had significantly stronger body shape concern, tended to not have jobs, perceived their mothers to be less caring, and were more disorganized in temperament. Of all these factors, the best predictor of dropout was shape concern. Conclusion Shape concern, job condition, parenting care, and organization predicted dropout from the group CBT weight loss intervention for overweight or obese Japanese women. PMID:26745715

Initial depressive episodes affect the risk of suicide attempts in Korean patients with bipolar disorder.

PubMed

Ryu, Vin; Jon, Duk-In; Cho, Hyun Sang; Kim, Se Joo; Lee, Eun; Kim, Eun Joo; Seok, Jeong-Ho

2010-09-01

Suicide is a major concern for increasing mortality in bipolar patients, but risk factors for suicide in bipolar disorder remain complex, including Korean patients. Medical records of bipolar patients were retrospectively reviewed to detect significant clinical characteristics associated with suicide attempts. A total of 579 medical records were retrospectively reviewed. Bipolar patients were divided into two groups with the presence of a history of suicide attempts. We compared demographic characteristics and clinical features between the two groups using an analysis of covariance and chi-square tests. Finally, logistic regression was performed to evaluate significant risk factors associated with suicide attempts in bipolar disorder. The prevalence of suicide attempt was 13.1% in our patient group. The presence of a depressive first episode was significantly different between attempters and nonattempters. Logistic regression analysis revealed that depressive first episodes and bipolar II disorder were significantly associated with suicide attempts in those patients. Clinicians should consider the polarity of the first mood episode when evaluating suicide risk in bipolar patients. This study has some limitations as a retrospective study and further studies with a prospective design are needed to replicate and evaluate risk factors for suicide in patients with bipolar disorder.

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.

[Prediction of histological liver damage in asymptomatic alcoholic patients by means of clinical and laboratory data].

PubMed

Iturriaga, H; Hirsch, S; Bunout, D; Díaz, M; Kelly, M; Silva, G; de la Maza, M P; Petermann, M; Ugarte, G

1993-04-01

Looking for a noninvasive method to predict liver histologic alterations in alcoholic patients without clinical signs of liver failure, we studied 187 chronic alcoholics recently abstinent, divided in 2 series. In the model series (n = 94) several clinical variables and results of common laboratory tests were confronted to the findings of liver biopsies. These were classified in 3 groups: 1. Normal liver; 2. Moderate alterations; 3. Marked alterations, including alcoholic hepatitis and cirrhosis. Multivariate methods used were logistic regression analysis and a classification and regression tree (CART). Both methods entered gamma-glutamyltransferase (GGT), aspartate-aminotransferase (AST), weight and age as significant and independent variables. Univariate analysis with GGT and AST at different cutoffs were also performed. To predict the presence of any kind of damage (Groups 2 and 3), CART and AST > 30 IU showed the higher sensitivity, specificity and correct prediction, both in the model and validation series. For prediction of marked liver damage, a score based on logistic regression and GGT > 110 IU had the higher efficiencies. It is concluded that GGT and AST are good markers of alcoholic liver damage and that, using sample cutoffs, histologic diagnosis can be correctly predicted in 80% of recently abstinent asymptomatic alcoholics.

Association between coagulation function and patients with primary angle closure glaucoma: a 5-year retrospective case-control study.

PubMed

Li, Shengjie; Gao, Yanting; Shao, Mingxi; Tang, Binghua; Cao, Wenjun; Sun, Xinghuai

2017-11-04

To evaluate the association between coagulation function and patients with primary angle closure glaucoma (PACG). A retrospective, hospital-based, case-control study. Shanghai, China. A total of 1778 subjects were recruited from the Eye & ENT Hospital of Fudan University from January 2010 to December 2015, including patients with PACG (male=296; female=569) and control subjects (male=290; female=623). Sociodemographic data and clinical data were collected. The one-way analysis of variance test was used to compare the levels of laboratory parameters among the mild, moderate and severe PACG groups. Multivariate logistic regression analyses were performed to identify the independent risk factors for PACG. The nomogram was constructed based on the logistic regression model using the R project for statistical computing (R V.3.3.2). The activated partial thromboplastin time (APTT) of the PACG group was approximately 4% shorter (p<0.001) than that of the control group. The prothrombin time (PT) was approximately 2.40% shorter (p<0.001) in patients with PACG compared with the control group. The thrombin time was also approximately 2.14% shorter (p<0.001) in patients with PACG compared with the control group. The level of D-dimer was significantly higher (p=0.042) in patients with PACG. Moreover, the mean platelet volume (MPV) of the PACG group was significantly higher (p=0.013) than that of the control group. A similar trend was observed when coagulation parameters were compared between the PACG and control groups with respect to gender and/or age. Multiple logistic regression analyses revealed that APTT (OR=1.032, 95% CI 1.000 to 1.026), PT (OR=1.249, 95% CI 1.071 to 1.457) and MPV (OR=1.185, 95% CI 1.081 to 1.299) were independently associated with PACG. Patients with PACG had a shorter coagulation time. Our results suggest that coagulation function is significantly associated with patients with PACG and may play an important role in the onset and development of PACG. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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

PubMed

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

2008-01-01

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

Clinical multifactorial analysis of early postoperative seizures in elderly patients following meningioma resection

PubMed Central

ZHANG, BO; WANG, DAN; GUO, YUNBAO; YU, JINLU

2015-01-01

The aim of the present study was to identify the major factors correlated with early postoperative seizures in elderly patients who had undergone a meningioma resection, and subsequently, to develop a logistic regression equation for assessing the seizures risk. Fourteen factors possibly correlated with early postoperative seizures in a cohort of 209 elderly patients who had undergone meningioma resection, as analyzed by multifactorial stepwise logistic regression. Phenobarbital sodium (0.1 g, intramuscularly) was administered to all 209 patients 30 min prior to undergoing surgery. All the patients had no previous history of seizures. The correlation of the 14 clinical factors (gender, tumor site, dyskinesia, peritumoral brain edema (PTBE), tumor diameter, pre- and postoperative prophylaxes, surgery time, tumor adhesion, circumscription, blood supply, intraoperative transfusion, original site of the tumor and dysphasia) was assessed in association with the risk for post-operative seizures. Tumor diameter, postoperative prophylactic antiepileptic drug (PPAD) administration, PTBE and tumor site were entered as risk factors into a mathematical regression model. The odds ratio (OR) of the tumor diameter was >1, and PPAD administration showed an OR >1, relative to a non-prophylactic group. A logistic regression equation was obtained and the sensitivity, specificity and misdiagnosis rates were 91.4, 74.3 and 25.7%, respectively. Tumor diameter, PPAD administration, PTBE and tumor site were closely correlated with early postoperative seizures; PTBE and PPAD administration were risk and protective factors, respectively. PMID:26137257

[Risk factors for elevated serum total bile acid in preterm infants].

PubMed

Song, Yan-Ting; Wang, Yong-Qin; Zhao, Yue-Hua; Zhu, Hai-Ling; Liu, Qian; Zhang, Xiao; Gao, Yi-Wen; Zhang, Wei-Ye; Sang, Yu-Tong

2018-03-01

To study the risk factors for elevated serum total bile acid (TBA) in preterm infants. A retrospective analysis was performed for the clinical data of 216 preterm infants who were admitted to the neonatal intensive care unit. According to the presence or absence of elevated TBA (TBA >24.8 μmol/L), the preterm infants were divided into elevated TBA group with 53 infants and non-elevated TBA group with 163 infants. A univariate analysis and an unconditional multivariate logistic regression analysis were used to investigate the risk factors for elevated TBA. The univariate analysis showed that there were significant differences between the elevated TBA group and the non-elevated TBA group in gestational age at birth, birth weight, proportion of small-for-gestational-age infants, proportion of infants undergoing ventilator-assisted ventilation, fasting time, parenteral nutrition time, and incidence of neonatal respiratory failure and sepsis (P<0.05). The unconditional multivariate logistic regression analysis showed that low birth weight (OR=3.84, 95%CI: 1.53-9.64) and neonatal sepsis (OR=2.56, 95%CI: 1.01-6.47) were independent risk factors for elevated TBA in preterm infants. Low birth weight and neonatal sepsis may lead to elevated TBA in preterm infants.

Embedded measures of performance validity using verbal fluency tests in a clinical sample.

PubMed

Sugarman, Michael A; Axelrod, Bradley N

2015-01-01

The objective of this study was to determine to what extent verbal fluency measures can be used as performance validity indicators during neuropsychological evaluation. Participants were clinically referred for neuropsychological evaluation in an urban-based Veteran's Affairs hospital. Participants were placed into 2 groups based on their objectively evaluated effort on performance validity tests (PVTs). Individuals who exhibited credible performance (n = 431) failed 0 PVTs, and those with poor effort (n = 192) failed 2 or more PVTs. All participants completed the Controlled Oral Word Association Test (COWAT) and Animals verbal fluency measures. We evaluated how well verbal fluency scores could discriminate between the 2 groups. Raw scores and T scores for Animals discriminated between the credible performance and poor-effort groups with 90% specificity and greater than 40% sensitivity. COWAT scores had lower sensitivity for detecting poor effort. A combination of FAS and Animals scores into logistic regression models yielded acceptable group classification, with 90% specificity and greater than 44% sensitivity. Verbal fluency measures can yield adequate detection of poor effort during neuropsychological evaluation. We provide suggested cut points and logistic regression models for predicting the probability of poor effort in our clinical setting and offer suggested cutoff scores to optimize sensitivity and specificity.

Association between oral health behavior and periodontal disease among Korean adults

PubMed Central

Han, Kyungdo; Park, Jun-Beom

2017-01-01

Abstract This study was performed to assess the association between oral health behavior and periodontal disease using nationally representative data. This study involved a cross-sectional analysis and multivariable logistic regression analysis models using the data from the Korean National Health and Nutrition Examination Survey. A community periodontal index greater than or equal to code 3 was used to define periodontal disease. Adjusted odds ratios and their 95% confidence intervals of periodontitis for the toothbrushing after lunch group and the toothbrushing before bedtime group were 0.842 (0.758, 0.936) and 0.814 (0.728, 0.911), respectively, after adjustments for age, sex, body mass index, drinking, exercise, education, income, white blood cell count, and metabolic syndrome. Adjusted odds ratios and their 95% confidence intervals of periodontitis for the floss group and the powered toothbrush group after adjustment were 0.678 (0.588, 0.781) and 0.771 (0.610, 0.974), respectively. The association between oral health behavior and periodontitis was proven by multiple logistic regression analyses after adjusting for confounding factors among Korean adults. Brushing after lunch and before bedtime as well as the use of floss and a powered toothbrush may be considered independent risk indicators of periodontal disease among Korean adults. PMID:28207558

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…

Soccer and sexual health education: a promising approach for reducing adolescent births in Haiti.

PubMed

Kaplan, Kathryn C; Lewis, Judy; Gebrian, Bette; Theall, Katherine

2015-05-01

To explore the effect of an innovative, integrative program in female sexual reproductive health (SRH) and soccer (or fútbol, in Haitian Creole) in rural Haiti by measuring the rate of births among program participants 15-19 years old and their nonparticipant peers. A retrospective cohort study using 2006-2009 data from the computerized data-tracking system of the Haitian Health Foundation (HHF), a U.S.-based nongovernmental organization serving urban and rural populations in Haiti, was used to assess births among girls 15-19 years old who participated in HHF's GenNext program, a combination education-soccer program for youth, based on SRH classes HHF nurses and community workers had been conducting in Haiti for mothers, fathers, and youth; girl-centered health screenings; and an all-female summer soccer league, during 2006-2009 (n = 4 251). Bivariate and multiple logistic regression analyses were carried out to assess differences in the rate of births among program participants according to their level of participation (SRH component only ("EDU") versus both the SRH and soccer components ("SO") compared to their village peers who did not participate. Hazard ratios (HRs) of birth rates were estimated using Cox regression analysis of childbearing data for the three different groups. In the multiple logistic regression analysis, only the girls in the "EDU" group had significantly fewer births than the nonparticipants after adjusting for confounders (odds ratio = 0.535; 95% confidence interval (CI) = 0.304, 0.940). The Cox regression analysis demonstrated that those in the EDU group (HR = 0.893; 95% CI = 0.802, 0.994) and to a greater degree those in the SO group (HR = 0.631; 95% CI = 0.558, 0.714) were significantly protected against childbearing between the ages of 15 and 19 years. HHF's GenNext program demonstrates the effectiveness of utilizing nurse educators, community mobilization, and youth participation in sports, education, and structured youth groups to promote and sustain health for adolescent girls and young women.

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

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.

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.

Maximal bite force, facial morphology and sucking habits in young children with functional posterior crossbite

PubMed Central

CASTELO, Paula Midori; GAVIÃO, Maria Beatriz Duarte; PEREIRA, Luciano José; BONJARDIM, Leonardo Rigoldi

2010-01-01

Objective The maintenance of normal conditions of the masticatory function is determinant for the correct growth and development of its structures. Thus, the aims of this study were to evaluate the influence of sucking habits on the presence of crossbite and its relationship with maximal bite force, facial morphology and body variables in 67 children of both genders (3.5-7 years) with primary or early mixed dentition. Material and methods The children were divided in four groups: primary-normocclusion (PN, n=19), primary-crossbite (PC, n=19), mixed-normocclusion (MN, n=13), and mixed-crossbite (MC, n=16). Bite force was measured with a pressurized tube, and facial morphology was determined by standardized frontal photographs: AFH (anterior face height) and BFW (bizygomatic facial width). Results It was observed that MC group showed lower bite force than MN, and AFH/ BFW was significantly smaller in PN than PC (t-test). Weight and height were only significantly correlated with bite force in PC group (Pearson’s correlation test). In the primary dentition, AFH/BFW and breast-feeding (at least six months) were positive and negatively associated with crossbite, respectively (multiple logistic regression). In the mixed dentition, breastfeeding and bite force showed negative associations with crossbite (univariate regression), while nonnutritive sucking (up to 3 years) associated significantly with crossbite in all groups (multiple logistic regression). Conclusions In the studied sample, sucking habits played an important role in the etiology of crossbite, which was associated with lower bite force and long-face tendency. PMID:20485925

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.

Foreign Diploma versus Immigrant Background: Determinants of Labour Market Success or Failure?

ERIC Educational Resources Information Center

Storen, Liv Anne; Wiers-Jenssen, Jannecke

2010-01-01

This article compares the labour market situation of graduates with different types of international background. The authors look at four groups of graduates: immigrants and ethnic Norwegians graduated in Norway and immigrants and ethnic Norwegians graduated abroad. By employing multinomial logistic regression analyses the authors find that ethnic…

An Examination of Relationships between Precollege Outreach Programs and College Attendance Patterns among Minority Participants

ERIC Educational Resources Information Center

Alhaddab, Taghreed A.; Aquino, Katherine C.

2017-01-01

This study is an examination of the relationship between participation in precollege outreach programs and students' college access patterns (i.e., enrollment patterns and timing in postsecondary institutions), comparing different racial/ ethnic groups. The study included a series of logistic regression models to investigate relationships between…

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

ERIC Educational Resources Information Center

Breidenbach, Daniel H.; French, Brian F.

2011-01-01

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

Parental Youth Assets and Sexual Activity: Differences by Race/Ethnicity

ERIC Educational Resources Information Center

Tolma, Eleni L.; Oman, Roy F.; Vesely, Sara K.; Aspy, Cheryl B.; Beebe, Laura; Fluhr, Janene

2011-01-01

Objectives: To examine how the relationship between parental-related youth assets and youth sexual activity differed by race/ethnicity. Methods: A random sample of 976 youth and their parents living in a Midwestern city participated in the study. Multivariate logistic regression analyses were conducted for 3 major ethnic groups controlling for the…

Differences in Health Determinants between International and Domestic Students at a German.

ERIC Educational Resources Information Center

Kramer, Alexander; Prufer-Kramer, Luise; Stock, Christiane; Tshiananga, Jacques Tshiang

2004-01-01

The authors used a standardized questionnaire to survey 201 international and 193 German students at the University of Bielefeld, Germany, to determine differences in health practices between the 2 groups and to identify targets for health-promoting interventions. Multivariate logistic regression models revealed that long-term female international…

A Question of Justice: Disparities in Employees' Access to Flexible Schedule Arrangements

ERIC Educational Resources Information Center

Swanberg, Jennifer E.; Pitt-Catsouphes, Marcie; Drescher-Burke, Krista

2005-01-01

Within an organizational justice framework, this article investigates which group of employees are less likely to have access to flexible schedule options. Using data from the 1997 National Study of the Changing Workforce, logistical regressions were conducted to identify the employee, job, and workplace factors associated with limited access to…

Self-Reported Weight Perceptions, Dieting Behavior, and Breakfast Eating among High School Adolescents

ERIC Educational Resources Information Center

Zullig, Keith; Ubbes, Valerie A.; Pyle, Jennifer; Valois, Robert F.

2006-01-01

This study explored the relationships among weight perceptions, dieting behavior, and breakfast eating in 4597 public high school adolescents using the Centers for Disease Control and Prevention Youth Risk Behavior Survey. Adjusted multiple logistic regression models were constructed separately for race and gender groups via SUDAAN (Survey Data…

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

PubMed

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

2014-06-01

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

Seroprevalence of human hydatidosis using ELISA method in qom province, central iran.

PubMed

Rakhshanpour, A; Harandi, M Fasihi; Moazezi, Ss; Rahimi, Mt; Mohebali, M; Mowlavi, Ghh; Babaei, Z; Ariaeipour, M; Heidari, Z; Rokni, Mb

2012-01-01

The objective of this study was to determine the prevalence of cystic echinococcosis (CE) in Qom Province, central Iran using ELISA test. Overall, 1564 serum samples (800 males and 764 females) were collected from selected subjects by randomized cluster sampling in 2011-2012. Sera were analyzed by ELISA test using AgB. Before sampling, a questionnaire was filled out for each case. Data were analyzed using Chi-square test and multivariate logistic regression for risk factors analysis. Seropositivity was 1.6% (25 cases). Males (2.2%) showed significantly more positivity than females (0.9%) (P= 0.03). There was no significant association between CE seropositivity and age group, occupation, and region. Age group of 30-60 years encompassed the highest rate of positivity. The seropositivity of CE was 2.1% and 1.2% for urban and rural cases respectively. Binary logistic regression showed that males were 2.5 times at higher risk for infection than females. Although seroprevalence of CE is relatively low in Qom Province, yet due to the importance of the disease, all preventive measures should be taken into consideration.

Characterization of Musa sp. fruits and plantain banana ripening stages according to their physicochemical attributes.

PubMed

Valérie Passo Tsamo, Claudine; Andre, Christelle M; Ritter, Christian; Tomekpe, Kodjo; Ngoh Newilah, Gérard; Rogez, Hervé; Larondelle, Yvan

2014-08-27

This study aimed at understanding the contribution of the fruit physicochemical parameters to Musa sp. diversity and plantain ripening stages. A discriminant analysis was first performed on a collection of 35 Musa sp. cultivars, organized in six groups based on the consumption mode (dessert or cooking banana) and the genomic constitution. A principal component analysis reinforced by a logistic regression on plantain cultivars was proposed as an analytical approach to describe the plantain ripening stages. The results of the discriminant analysis showed that edible fraction, peel pH, pulp water content, and pulp total phenolics were among the most contributing attributes for the discrimination of the cultivar groups. With mean values ranging from 65.4 to 247.3 mg of gallic acid equivalents/100 g of fresh weight, the pulp total phenolics strongly differed between interspecific and monospecific cultivars within dessert and nonplantain cooking bananas. The results of the logistic regression revealed that the best models according to fitting parameters involved more than one physicochemical attribute. Interestingly, pulp and peel total phenolic contents contributed in the building up of these models.

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.

Effect of expressions of tumor necrosis factor α and interleukin 1B on peritoneal metastasis of gastric cancer.

PubMed

Guo, Lin; Ou, Jin-Lei; Zhang, Tong; Ma, Liang; Qu, Long-Fei

2015-11-01

Our study aimed to investigate effect of expressions of tumor necrosis factor α (TNF-α) and interleukin 1B (IL-1B) on peritoneal metastasis of gastric cancer (GC). From June 2012 to June 2014, a total of 60 patients with advanced peritoneal metastasis from GC were collected from Department of Gastrointestinal and Nutriology Surgery at Shengjing Hospital of China Medical University. Furthermore, 60 GC patients without peritoneal metastasis were enrolled as controls. Immunohistochemistry was performed to test TNF-α and IL-1B expression, and logistic regression analysis was employed for evaluating risk factors for peritoneal metastasis of GC. Our results showed that TNF-α expression in metastatic group and non-metastatic group was significantly different (P = 0.043), but no significant difference was found in IL-1B expression between two groups (P = 0.261). In addition, TNF-α expression in metastatic group and non-metastatic group was associated with tumor size, depth of invasion, the degree of differentiation (all P < 0.05). Logistic regression analysis indicated that tumor size, depth of invasion, the degree of differentiation and TNF-α expression were risk factors for peritoneal metastasis of GC (all P < 0.05). Our study found that TNF-α expression may play a vital role in peritoneal metastasis of GC, while IL-1B expression might not be correlated with peritoneal metastasis.

Using the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict 1-year mortality in population-based cohorts of patients with diabetes in Ontario, Canada.

PubMed

Austin, P C; Shah, B R; Newman, A; Anderson, G M

2012-09-01

There are limited validated methods to ascertain comorbidities for risk adjustment in ambulatory populations of patients with diabetes using administrative health-care databases. The objective was to examine the ability of the Johns Hopkins' Aggregated Diagnosis Groups to predict mortality in population-based ambulatory samples of both incident and prevalent subjects with diabetes. Retrospective cohorts constructed using population-based administrative data. The incident cohort consisted of all 346,297 subjects diagnosed with diabetes between 1 April 2004 and 31 March 2008. The prevalent cohort consisted of all 879,849 subjects with pre-existing diabetes on 1 January, 2007. The outcome was death within 1 year of the subject's index date. A logistic regression model consisting of age, sex and indicator variables for 22 of the 32 Johns Hopkins' Aggregated Diagnosis Group categories had excellent discrimination for predicting mortality in incident diabetes patients: the c-statistic was 0.87 in an independent validation sample. A similar model had excellent discrimination for predicting mortality in prevalent diabetes patients: the c-statistic was 0.84 in an independent validation sample. Both models demonstrated very good calibration, denoting good agreement between observed and predicted mortality across the range of predicted mortality in which the large majority of subjects lay. For comparative purposes, regression models incorporating the Charlson comorbidity index, age and sex, age and sex, and age alone had poorer discrimination than the model that incorporated the Johns Hopkins' Aggregated Diagnosis Groups. Logistical regression models using age, sex and the John Hopkins' Aggregated Diagnosis Groups were able to accurately predict 1-year mortality in population-based samples of patients with diabetes. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.

The mortality risk score and the ADG score: two points-based scoring systems for the Johns Hopkins aggregated diagnosis groups to predict mortality in a general adult population cohort in Ontario, Canada.

PubMed

Austin, Peter C; Walraven, Carl van

2011-10-01

Logistic regression models that incorporated age, sex, and indicator variables for the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) categories have been shown to accurately predict all-cause mortality in adults. To develop 2 different point-scoring systems using the ADGs. The Mortality Risk Score (MRS) collapses age, sex, and the ADGs to a single summary score that predicts the annual risk of all-cause death in adults. The ADG Score derives weights for the individual ADG diagnosis groups. : Retrospective cohort constructed using population-based administrative data. All 10,498,413 residents of Ontario, Canada, between the age of 20 and 100 years who were alive on their birthday in 2007, participated in this study. Participants were randomly divided into derivation and validation samples. : Death within 1 year. In the derivation cohort, the MRS ranged from -21 to 139 (median value 29, IQR 17 to 44). In the validation group, a logistic regression model with the MRS as the sole predictor significantly predicted the risk of 1-year mortality with a c-statistic of 0.917. A regression model with age, sex, and the ADG Score has similar performance. Both methods accurately predicted the risk of 1-year mortality across the 20 vigintiles of risk. The MRS combined values for a person's age, sex, and the John Hopkins ADGs to accurately predict 1-year mortality in adults. The ADG Score is a weighted score representing the presence or absence of the 32 ADG diagnosis groups. These scores will facilitate health services researchers conducting risk adjustment using administrative health care databases.

Risk factors for retinal breaks in patients with symptom of floaters.

PubMed

Singalavanija, Apichart; Amornrattanapan, Chutiwan; Nitiruangjarus, Kanjanee; Tongsai, Sasima

2010-06-01

To identify the risk factors of retinal breaks in patients with the symptom of floaters, and to determine the association between those risk factors and retinal breaks. A retrospective analytic study of 184 patients (55 males and 129 females) that included 220 eyes was conducted. Patient information such as age, symptoms (multiple floaters, flashing), duration of symptom, refractive error, history of cataract surgery, family history of retinal detachment, and complete eye examination were recorded. The patients were divided into two groups, the first group (control group) had symptoms of floaters and no retinal breaks, the second group (retinal breaks group) had symptoms of floaters with retinal breaks. Chi-square test, and the multiple logistic regression were used for statistical analysis. Two hundred twenty eyes, 175 eyes of the control group and 45 eyes of the retinal breaks group were examined and included in this study. The multiple logistic regression analysis revealed that patients with multiple floaters, and floaters and flashing increased the risk of retinal breaks to 5.8 and 4.3 times, respectively, when compared to patients with single floater or floaters alone. Lattice degeneration increased the risk of retinal breaks to 5.9 times when compared to eyes that did not have lattice degeneration. Multiple floaters, flashing and lattice degeneration are risk factors of retinal breaks in patients with symptoms of floaters. Therefore, it is important for the ophthalmologists to be aware of these risk factors and the patients at risk should have follow-up examinations.

[Risk factor analysis of the patients with solitary pulmonary nodules and establishment of a prediction model for the probability of malignancy].

PubMed

Wang, X; Xu, Y H; Du, Z Y; Qian, Y J; Xu, Z H; Chen, R; Shi, M H

2018-02-23

Objective: This study aims to analyze the relationship among the clinical features, radiologic characteristics and pathological diagnosis in patients with solitary pulmonary nodules, and establish a prediction model for the probability of malignancy. Methods: Clinical data of 372 patients with solitary pulmonary nodules who underwent surgical resection with definite postoperative pathological diagnosis were retrospectively analyzed. In these cases, we collected clinical and radiologic features including gender, age, smoking history, history of tumor, family history of cancer, the location of lesion, ground-glass opacity, maximum diameter, calcification, vessel convergence sign, vacuole sign, pleural indentation, speculation and lobulation. The cases were divided to modeling group (268 cases) and validation group (104 cases). A new prediction model was established by logistic regression analying the data from modeling group. Then the data of validation group was planned to validate the efficiency of the new model, and was compared with three classical models(Mayo model, VA model and LiYun model). With the calculated probability values for each model from validation group, SPSS 22.0 was used to draw the receiver operating characteristic curve, to assess the predictive value of this new model. Results: 112 benign SPNs and 156 malignant SPNs were included in modeling group. Multivariable logistic regression analysis showed that gender, age, history of tumor, ground -glass opacity, maximum diameter, and speculation were independent predictors of malignancy in patients with SPN( P <0.05). We calculated a prediction model for the probability of malignancy as follow: p =e(x)/(1+ e(x)), x=-4.8029-0.743×gender+ 0.057×age+ 1.306×history of tumor+ 1.305×ground-glass opacity+ 0.051×maximum diameter+ 1.043×speculation. When the data of validation group was added to the four-mathematical prediction model, The area under the curve of our mathematical prediction model was 0.742, which is greater than other models (Mayo 0.696, VA 0.634, LiYun 0.681), while the differences between any two of the four models were not significant ( P >0.05). Conclusions: Age of patient, gender, history of tumor, ground-glass opacity, maximum diameter and speculation are independent predictors of malignancy in patients with solitary pulmonary nodule. This logistic regression prediction mathematic model is not inferior to those classical models in estimating the prognosis of SPNs.

Identification of patients with gout: elaboration of a questionnaire for epidemiological studies.

PubMed

Richette, P; Clerson, P; Bouée, S; Chalès, G; Doherty, M; Flipo, R M; Lambert, C; Lioté, F; Poiraud, T; Schaeverbeke, T; Bardin, T

2015-09-01

In France, the prevalence of gout is currently unknown. We aimed to design a questionnaire to detect gout that would be suitable for use in a telephone survey by non-physicians and assessed its performance. We designed a 62-item questionnaire covering comorbidities, clinical features and treatment of gout. In a case-control study, we enrolled patients with a history of arthritis who had undergone arthrocentesis for synovial fluid analysis and crystal detection. Cases were patients with crystal-proven gout and controls were patients who had arthritis and effusion with no monosodium urate crystals in synovial fluid. The questionnaire was administered by phone to cases and controls by non-physicians who were unaware of the patient diagnosis. Logistic regression analysis and classification and regression trees were used to select items discriminating cases and controls. We interviewed 246 patients (102 cases and 142 controls). Two logistic regression models (sensitivity 88.0% and 87.5%; specificity 93.0% and 89.8%, respectively) and one classification and regression tree model (sensitivity 81.4%, specificity 93.7%) revealed 11 informative items that allowed for classifying 90.0%, 88.8% and 88.5% of patients, respectively. We developed a questionnaire to detect gout containing 11 items that is fast and suitable for use in a telephone survey by non-physicians. The questionnaire demonstrated good properties for discriminating patients with and without gout. It will be administered in a large sample of the general population to estimate the prevalence of gout in France. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

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

PubMed

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

2017-07-20

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

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…

A Logistic Regression and Markov Chain Model for the Prediction of Nation-state Violent Conflicts and Transitions

DTIC Science & Technology

2016-03-24

McCarthy, Blood Meridian 1.1 General Issue Violent conflict between competing groups has been a pervasive and driving force for all of human history...It has evolved from small skirmishes between unarmed groups , wielding rudimentary weapons, to industrialized global conflagrations. Global...methodology is presented in Figure 2. Figure 2: Study Methodology 5 1.6 Study Assumptions and Limitations Assumptions Four underlying assumptions were

Dysglycemia, Glycemic Variability, and Outcome After Cardiac Arrest and Temperature Management at 33°C and 36°C.

PubMed

Borgquist, Ola; Wise, Matt P; Nielsen, Niklas; Al-Subaie, Nawaf; Cranshaw, Julius; Cronberg, Tobias; Glover, Guy; Hassager, Christian; Kjaergaard, Jesper; Kuiper, Michael; Smid, Ondrej; Walden, Andrew; Friberg, Hans

2017-08-01

Dysglycemia and glycemic variability are associated with poor outcomes in critically ill patients. Targeted temperature management alters blood glucose homeostasis. We investigated the association between blood glucose concentrations and glycemic variability and the neurologic outcomes of patients randomized to targeted temperature management at 33°C or 36°C after cardiac arrest. Post hoc analysis of the multicenter TTM-trial. Primary outcome of this analysis was neurologic outcome after 6 months, referred to as "Cerebral Performance Category." Thirty-six sites in Europe and Australia. All 939 patients with out-of-hospital cardiac arrest of presumed cardiac cause that had been included in the TTM-trial. Targeted temperature management at 33°C or 36°C. Nonparametric tests as well as multiple logistic regression and mixed effects logistic regression models were used. Median glucose concentrations on hospital admission differed significantly between Cerebral Performance Category outcomes (p < 0.0001). Hyper- and hypoglycemia were associated with poor neurologic outcome (p = 0.001 and p = 0.054). In the multiple logistic regression models, the median glycemic level was an independent predictor of poor Cerebral Performance Category (Cerebral Performance Category, 3-5) with an odds ratio (OR) of 1.13 in the adjusted model (p = 0.008; 95% CI, 1.03-1.24). It was also a predictor in the mixed model, which served as a sensitivity analysis to adjust for the multiple time points. The proportion of hyperglycemia was higher in the 33°C group compared with the 36°C group. Higher blood glucose levels at admission and during the first 36 hours, and higher glycemic variability, were associated with poor neurologic outcome and death. More patients in the 33°C treatment arm had hyperglycemia.

Impact of wearing fixed orthodontic appliances on quality of life among adolescents: Case-control study.

PubMed

Costa, Andréa A; Serra-Negra, Júnia M; Bendo, Cristiane B; Pordeus, Isabela A; Paiva, Saul M

2016-01-01

To investigate the impact of wearing a fixed orthodontic appliance on oral health-related quality of life (OHRQoL) among adolescents. A case-control study (1 ∶ 2) was carried out with a population-based randomized sample of 327 adolescents aged 11 to 14 years enrolled at public and private schools in the City of Brumadinho, southeast of Brazil. The case group (n  =  109) was made up of adolescents with a high negative impact on OHRQoL, and the control group (n  =  218) was made up of adolescents with a low negative impact. The outcome variable was the impact on OHRQoL measured by the Brazilian version of the Child Perceptions Questionnaire (CPQ 11-14) - Impact Short Form (ISF:16). The main independent variable was wearing fixed orthodontic appliances. Malocclusion and the type of school were identified as possible confounding variables. Bivariate and multiple conditional logistic regressions were employed in the statistical analysis. A multiple conditional logistic regression model demonstrated that adolescents wearing fixed orthodontic appliances had a 4.88-fold greater chance of presenting high negative impact on OHRQoL (95% CI: 2.93-8.13; P < .001) than those who did not wear fixed orthodontic appliances. A bivariate conditional logistic regression demonstrated that malocclusion was significantly associated with OHRQoL (P  =  .017), whereas no statistically significant association was found between the type of school and OHRQoL (P  =  .108). Adolescents who wore fixed orthodontic appliances had a greater chance of reporting a negative impact on OHRQoL than those who did not wear such appliances.

Relationship between chemical structure and the occupational asthma hazard of low molecular weight organic compounds

PubMed Central

Jarvis, J; Seed, M; Elton, R; Sawyer, L; Agius, R

2005-01-01

Aims: To investigate quantitatively, relationships between chemical structure and reported occupational asthma hazard for low molecular weight (LMW) organic compounds; to develop and validate a model linking asthma hazard with chemical substructure; and to generate mechanistic hypotheses that might explain the relationships. Methods: A learning dataset used 78 LMW chemical asthmagens reported in the literature before 1995, and 301 control compounds with recognised occupational exposures and hazards other than respiratory sensitisation. The chemical structures of the asthmagens and control compounds were characterised by the presence of chemical substructure fragments. Odds ratios were calculated for these fragments to determine which were associated with a likelihood of being reported as an occupational asthmagen. Logistic regression modelling was used to identify the independent contribution of these substructures. A post-1995 set of 21 asthmagens and 77 controls were selected to externally validate the model. Results: Nitrogen or oxygen containing functional groups such as isocyanate, amine, acid anhydride, and carbonyl were associated with an occupational asthma hazard, particularly when the functional group was present twice or more in the same molecule. A logistic regression model using only statistically significant independent variables for occupational asthma hazard correctly assigned 90% of the model development set. The external validation showed a sensitivity of 86% and specificity of 99%. Conclusions: Although a wide variety of chemical structures are associated with occupational asthma, bifunctional reactivity is strongly associated with occupational asthma hazard across a range of chemical substructures. This suggests that chemical cross-linking is an important molecular mechanism leading to the development of occupational asthma. The logistic regression model is freely available on the internet and may offer a useful but inexpensive adjunct to the prediction of occupational asthma hazard. PMID:15778257

Association Between Duration of Breast Feeding and Metabolic Syndrome: The Korean National Health and Nutrition Examination Surveys.

PubMed

Choi, Se Rin; Kim, Yong Min; Cho, Min Su; Kim, So Hyun; Shim, Young Suk

2017-04-01

This study aimed to evaluate the association of the lifelong duration of breast feeding with metabolic syndrome (MetS) and its components in Korean parous women aged 19-50 years. A total of 4724 participants from the Korean National Health and Nutritional Survey were included. Subjects were divided into four groups according to the duration of breast feeding: ≤5, 6-11, 12-23, or ≥24 months groups. The adjusted odds ratios (ORs) of MetS and its components were assessed according to the duration of breast feeding. Women who breastfed for 6-11 months had an OR of 0.67 (95% confidence interval [CI], 0.54-0.86) for elevated blood pressure (BP) compared with those who breastfed for ≤5 months after adjustment for possible confounders in a multivariable logistic regression analyses. Women who breastfed for 12-23 months were associated with an OR of 0.68 (95% CI, 0.54-0.86) for elevated BP, an OR of 0.78 (95% CI, 0.62-0.97) for elevated glucose, and an OR of 0.73 (95% CI, 0.56-0.95) for MetS compared with those who breastfed for ≤5 months in a multivariable logistic regression analyses. Women who breastfed for ≥24 months had an OR of 0.62 (95% CI, 0.52-0.84) for elevated glucose, an OR of 0.76 (95% CI, 0.60-0.96) for elevated triglycerides, and an OR of 0.70 (95% CI, 0.53-0.92) for MetS compared with those who breastfed for ≤5 months in a multivariable logistic regression analyses. Our results suggest that lifelong breast feeding for ≥12 months may be associated with lower risk for MetS.

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…

Modeling data for pancreatitis in presence of a duodenal diverticula using logistic regression

NASA Astrophysics Data System (ADS)

Dineva, S.; Prodanova, K.; Mlachkova, D.

2013-12-01

The presence of a periampullary duodenal diverticulum (PDD) is often observed during upper digestive tract barium meal studies and endoscopic retrograde cholangiopancreatography (ERCP). A few papers reported that the diverticulum had something to do with the incidence of pancreatitis. The aim of this study is to investigate if the presence of duodenal diverticula predisposes to the development of a pancreatic disease. A total 3966 patients who had undergone ERCP were studied retrospectively. They were divided into 2 groups-with and without PDD. Patients with a duodenal diverticula had a higher rate of acute pancreatitis. The duodenal diverticula is a risk factor for acute idiopathic pancreatitis. A multiple logistic regression to obtain adjusted estimate of odds and to identify if a PDD is a predictor of acute or chronic pancreatitis was performed. The software package STATISTICA 10.0 was used for analyzing the real data.

Association between breastfeeding duration, non-nutritive sucking habits and dental arch dimensions in deciduous dentition: a cross-sectional study.

PubMed

Agarwal, Shiv Shankar; Nehra, Karan; Sharma, Mohit; Jayan, Balakrishna; Poonia, Anish; Bhattal, Hiteshwar

2014-10-31

This cross-sectional retrospective study was conducted to determine association between breastfeeding duration, non-nutritive sucking habits, dental arch transverse diameters, posterior crossbite and anterior open bite in deciduous dentition. 415 children (228 males and 187 females), 4 to 6 years old, from a mixed Indian population were clinically examined. Based on written questionnaire answered by parents, children were divided into two groups: group 1 (breastfed for <6 months (n = 158)) and group 2 (breastfed for ≥6 months (n = 257)). The associations were analysed using chi-square test (P < 0.05 taken as statistically significant). Odds ratio (OR) was calculated to determine the strength of associations tested. Multivariate logistic regression analysis was done for obtaining independent predictors of posterior crossbite and maxillary and mandibular IMD (Inter-molar distance) and ICD (Inter-canine distance). Non-nutritive sucking (NNS) was present in 15.18% children (20.3% in group 1 as compared to 12.1% in group 2 (P = 0.024)). The average ICD and IMD in maxilla and average IMD in mandible were significantly higher among group 2 as compared to group 1 (P < 0.01). In mandible, average ICD did not differ significantly between the two groups (P = 0.342). The distribution of anterior open bite did not differ significantly between the two groups (P = 0.865). The distribution of posterior crossbite was significantly different between the two groups (P = 0.001). OR assessment (OR = 1.852) revealed that group 1 had almost twofold higher prevalence of NNS habits than group 2. Multivariate logistic regression analysis revealed that the first group had independently fourfold increased risk of developing crossbite compared to the second group (OR = 4.3). Multivariate linear regression analysis also revealed that age and breastfeeding duration were the most significant determinants of ICD and IMD. An increased prevalence of NNS in the first group suggests that NNS is a dominant variable in the association between breastfeeding duration and reduced intra-arch transverse diameters which leads to increased prevalence of posterior crossbites as seen in our study. Mandibular inter-canine width is however unaffected due to a lowered tongue posture seen in these children.

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.

Prevalence of the Metabolic Syndrome among Korean Workers by Occupational Group: Fifth Korean National Health and Nutrition Examination Survey (KNHANES) 2010

PubMed Central

2013-01-01

Objectives The prevalence of the metabolic syndrome has increased rapidly in South Korea over the past 10 years. However, the occurrence of the metabolic syndrome in workers grouped according to the specific type of work is not well understood in Korea. In this study, we assessed the differences in the prevalence of the metabolic syndrome by occupational group and evaluated the risk of the metabolic syndrome among occupational groups. Methods From the Fifth Korean National Health and Nutrition Examination Survey (2010), 3,303 employed participants were included in this study. The unadjusted and age-adjusted prevalences of the metabolic syndrome were estimated and multiple logistic regression analysis was conducted using the presence of the metabolic syndrome as a dependent variable, and adjusting for age, education level, household income, drinking behavior, smoking status, physical activity, work hours, and work scheduling pattern. Results Among male workers, non-manual workers had the greatest age-adjusted prevalence (26.4%, 95% CI: 22.3-30.5%) among the occupational groups. In a logistic regression analysis, male manual workers had a significantly lower odds ratio for the metabolic syndrome relative to non-manual workers (0.59, 95% CI: 0.41-0.85). Conclusion Our study demonstrated differences in the prevalence of the metabolic syndrome by occupational group and identified the greatest risk for the metabolic syndrome in male non-manual workers. PMID:24472422

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

Risk Factors for Venous Thromboembolism in Chronic Obstructive Pulmonary Disease

PubMed Central

Kim, Victor; Goel, Nishant; Gangar, Jinal; Zhao, Huaqing; Ciccolella, David E.; Silverman, Edwin K.; Crapo, James D.; Criner, Gerard J.

2014-01-01

Background: COPD patients are at increased risk for venous thromboembolism (VTE). VTE however remains under-diagnosed in this population and the clinical profile of VTE in COPD is unclear. Methods: Global initiative for chronic Obstructive Lung Disease (GOLD) stages II-IV participants in the COPD Genetic Epidemiology (COPDGene) study were divided into 2 groups: VTE+, those who reported a history of VTE by questionnaire, and VTE-, those who did not. We compared variables in these 2 groups with either t-test or chi-squared test for continuous and categorical variables, respectively. We performed a univariate logistic regression for VTE, and then a multivariate logistic regression using the significant predictors of interest in the univariate analysis to ascertain the determinants of VTE. Results: The VTE+ group was older, more likely to be Caucasian, had a higher body mass index (BMI), smoking history, used oxygen, had a lower 6-minute walk distance, worse quality of life scores, and more dyspnea and respiratory exacerbations than the VTE- group. Lung function was not different between groups. A greater percentage of the VTE+ group described multiple medical comorbidities. On multivariate analysis, BMI, 6-minute walk distance, pneumothorax, peripheral vascular disease, and congestive heart failure significantly increased the odds for VTE by history. Conclusions: BMI, exercise capacity, and medical comorbidities were significantly associated with VTE in moderate to severe COPD. Clinicians should suspect VTE in patients who present with dyspnea and should consider possibilities other than infection as causes of COPD exacerbation. PMID:25844397

A Predictive Model for Readmissions Among Medicare Patients in a California Hospital.

PubMed

Duncan, Ian; Huynh, Nhan

2017-11-17

Predictive models for hospital readmission rates are in high demand because of the Centers for Medicare & Medicaid Services (CMS) Hospital Readmission Reduction Program (HRRP). The LACE index is one of the most popular predictive tools among hospitals in the United States. The LACE index is a simple tool with 4 parameters: Length of stay, Acuity of admission, Comorbidity, and Emergency visits in the previous 6 months. The authors applied logistic regression to develop a predictive model for a medium-sized not-for-profit community hospital in California using patient-level data with more specific patient information (including 13 explanatory variables). Specifically, the logistic regression is applied to 2 populations: a general population including all patients and the specific group of patients targeted by the CMS penalty (characterized as ages 65 or older with select conditions). The 2 resulting logistic regression models have a higher sensitivity rate compared to the sensitivity of the LACE index. The C statistic values of the model applied to both populations demonstrate moderate levels of predictive power. The authors also build an economic model to demonstrate the potential financial impact of the use of the model for targeting high-risk patients in a sample hospital and demonstrate that, on balance, whether the hospital gains or loses from reducing readmissions depends on its margin and the extent of its readmission penalties.

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.

Distribution of cavity trees in midwestern old-growth and second-growth forests

Treesearch

Zhaofei Fan; Stephen R. Shifley; Martin A. Spetich; Frank R. Thompson; David R. Larsen

2003-01-01

We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in...

Distribution of cavity trees in midwesternold-growth and second-growth forests

Treesearch

Zhaofei Fan; Stephen R. Shifley; Martin A. Spetich; Frank R., III Thompson; David R. Larsen

2003-01-01

We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in...

Method for estimating potential tree-grade distributions for northeastern forest species

Treesearch

Daniel A. Yaussy; Daniel A. Yaussy

1993-01-01

Generalized logistic regression was used to distribute trees into four potential tree grades for 20 northeastern species groups. The potential tree grade is defined as the tree grade based on the length and amount of clear cuttings and defects only, disregarding minimum grading diameter. The algorithms described use site index and tree diameter as the predictive...

Service Needs across the Lifespan for Individuals with Autism

ERIC Educational Resources Information Center

Turcotte, Paul; Mathew, Mary; Shea, Lindsay L.; Brusilovskiy, Eugene; Nonnemacher, Stacy L.

2016-01-01

The goal of this research was to examine reported service needs among individuals with autism spectrum disorder (ASD) of all ages. Data were generated from a state survey that queried the needs of children, adolescents and adults with ASD. Logistic regression was used to compare service use and need among these age groups. Adults with ASD were…

A Randomized Controlled Study of the Virginia Student Threat Assessment Guidelines in Kindergarten through Grade 12

ERIC Educational Resources Information Center

Cornell, Dewey G.; Allen, Korrie; Fan, Xitao

2012-01-01

This randomized controlled study examined disciplinary outcomes for 201 students who made threats of violence at school. The students attended 40 schools randomly assigned to use the Virginia Student Threat Assessment Guidelines or follow a business-as-usual disciplinary approach in a control group. Logistic regression analyses found, after…

Determining the Factors of Social Phobia Levels of University Students: A Logistic Regression Analysis

ERIC Educational Resources Information Center

Ozen, Hamit

2016-01-01

Experiencing social phobia is an important factor which can hinder academic success during university years. In this study, research of social phobia with several variables is conducted among university students. The research group of the study consists of total 736 students studying at various departments at universities in Turkey. Students are…

Development and Validation of an Instrument for Early Assessment of Management Potential in a Mid-Size Chemical Company

ERIC Educational Resources Information Center

Zehner, Robert L.; Holton, Elwood F., III

2004-01-01

This study reports on development and concurrent validation of a competency instrument to identify potential leaders in a mid-size chemical company. Four competencies were identified: courageous problem solving, perceived energy, networking, and perceived motivation. Four different comparison groups were examined in logistic regression analyses.…

Income, Language, and Citizenship Status: Factors Affecting the Health Care Access and Utilization of Chinese Americans.

ERIC Educational Resources Information Center

Jang, Michael; Lee, Evelyn; Woo, Kent

1998-01-01

The effects of income, language, and citizenship on the use of health-care services by Chinese Americans is examined (N=1808). Focus groups, a telephone survey, and key informant interviews were conducted. Data analysis included an acculturation index, demographic profile, and logistical regression. Health insurance and social factors are…

Characteristics of Student Loan Defaulters among Different Racial and Ethnic Groups. AIR 1995 Annual Forum Paper.

ERIC Educational Resources Information Center

Volkwein, J. Fredericks; And Others

This study examined the characteristics of students who default on their student loans and compared default among Whites, Asians, African Americans, Hispanics, and Native Americans. Four logistic regression models were developed using information from the National Post-Secondary Student Aid Study which contains an array of pre-college, college,…

Preoperative Fasting C-Peptide Predicts Type 2 Diabetes Mellitus Remission in Low-BMI Chinese Patients After Roux-en-Y Gastric Bypass.

PubMed

Zhao, Lei; Li, Weizheng; Su, Zhihong; Liu, Yong; Zhu, Liyong; Zhu, Shaihong

2018-05-29

This study investigated the role of preoperative fasting C-peptide (FCP) levels in predicting diabetic outcomes in low-BMI Chinese patients following Roux-en-Y gastric bypass (RYGB) by comparing the metabolic outcomes of patients with FCP > 1 ng/ml versus FCP ≤ 1 ng/ml. The study sample included 78 type 2 diabetes mellitus patients with an average BMI < 30 kg/m 2 at baseline. Patients' parameters were analyzed before and after surgery, with a 2-year follow-up. A univariate logistic regression analysis and multivariate analysis of variance between the remission and improvement group were performed to determine factors that were associated with type 2 diabetes remission after RYGB. Linear correlation analyses between FCP and metabolic parameters were performed. Patients were divided into two groups: FCP > 1 ng/ml and FCP ≤ 1 ng/ml, with measured parameters compared between the groups. Patients' fasting plasma glucose, 2-h postprandial plasma glucose, FCP, and HbA1c improved significantly after surgery (p < 0.05). Factors associated with type 2 diabetes remission were BMI, 2hINS, and FCP at the univariate logistic regression analysis (p < 0.05). Multivariate logistic regression analysis was performed then showed the results were more related to FCP (OR = 2.39). FCP showed a significant linear correlation with fasting insulin and BMI (p < 0.05). There was a significant difference in remission rate between the FCP > 1 ng/ml and FCP ≤ 1 ng/ml groups (p = 0.01). The parameters of patients with FCP > 1 ng/ml, including BMI, plasma glucose, HbA1c, and plasma insulin, decreased markedly after surgery (p < 0.05). FCP level is a significant predictor of diabetes outcomes after RYGB in low-BMI Chinese patients. An FCP level of 1 ng/ml may be a useful threshold for predicting surgical prognosis, with FCP > 1 ng/ml predicting better clinical outcomes following RYGB.

[Analysis of the prevalence and risk factors of preoperative angiography confirmed coronary artery stenosis in patients with degenerative valvular heart disease].

PubMed

Xu, Z J; Pan, J; Zhou, Q; Wang, D J

2017-10-24

Objective: To estimate the prevalence and the risk factors of preoperative coronary angiography (CAG) confirmed coronary stenosis in patients with degenerative valvular heart disease. Methods: A total of 491 patients who underwent screening CAG before valvular surgery due to degenerative valvular heart disease were enrolled from January 2011 to September 2014 in our hospital, and clinical data were analyzed. According to CAG results, patients were divided into positive CAG result (PCAG) group or negative CAG (NCAG) group. Positive CAG result was defined as stenosis ≥50% of the diameter of the left main coronary artery or stenosis ≥70% of the diameter of left anterior descending, left circumflex artery, and right coronary artery.Risk factors of positive CAG result were analyzed by multivariable logistic regression analysis, and Bootstrap method was used to verify the results. Results: There were 47(9.57%)degenerative valvular heart disease patients with PCAG. Patients were older ((68.0±7.6)years vs.(62.6±7.1)years, P <0.001) and the prevalence of typical angina was significantly higher (14.89%(7/47)vs. 2.03%(9/444), P <0.001)in PCAG group than in NCAG group. Multivariable logistic regression analysis showed that age ( OR =1.118, 95% CI 1.067-1.172, P <0.001), typical angina ( OR =8.970, 95% CI 2.963-27.154, P <0.001), and serum concentration of apolipoprotein B ( OR =20.311, 95% CI 4.774-86.416, P <0.001) were the independent risk factors of PCAG in degenerative valvular heart disease patients. Bootstrap method revealed satisfactory repeatability of multivariable logistic regression analysis results (age: OR =1.118, 95% CI 1.068-1.178, P =0.001; typical angina: OR =8.970, 95% CI 2.338-35.891, P =0.001; serum concentration of apolipoprotein B: OR =20.311, 95% CI 4.639-91.977, P =0.001). Conclusions: A low prevalence of PCAG before valvular surgery is observed in degenerative valvular heart disease patients in this patient cohort. Age, typical angina, and serum concentration of apolipoprotein B are independent risk factors of PCAG in this patient cohort.

Anisodamine accelerates spontaneous passage of single symptomatic bile duct stones ≤ 10 mm

PubMed Central

Gao, Jun; Ding, Xue-Mei; Ke, Shan; Zhou, Yi-Ming; Qian, Xiao-Jun; Ma, Rui-Liang; Ning, Chun-Min; Xin, Zong-Hai; Sun, Wen-Bing

2013-01-01

AIM: To investigate the rate of spontaneous passage of single and symptomatic common bile duct (CBD) stones ≤ 10 mm in diameter in 4 wk with or without a 2-wk course of anisodamine. METHODS: A multicenter, randomized, placebo-controlled trial was undertaken. A total of 197 patients who met the inclusion criteria were enrolled. Ninety-seven patients were assigned randomly to the control group and the other 100 to the anisodamine group. The anisodamine group received intravenous infusions of anisodamine (10 mg every 8 h) for 2 wk. The control group received the same volume of 0.9% isotonic saline for 2 wk. Patients underwent imaging studies and liver-function tests every week for 4 wk. The rate of spontaneous passage of CBD stones was analyzed. RESULTS: The rate of spontaneous passage of CBD stones was significantly higher in the anisodamine group than that in the control group (47.0% vs 22.7%). Most (87.2%, 41/47) stone passages in the anisodamine group occurred in the first 2 wk, and passages in the control group occurred at a comparable rate each week. Factors significantly increasing the possibility of spontaneous passage by univariate logistic regression analyses were stone diameter (< 5 mm vs ≥ 5 mm and ≤ 10 mm) and anisodamine therapy. Multivariate logistic regression analyses revealed that these two factors were significantly associated with spontaneous passage. CONCLUSION: Two weeks of anisodamine administration can safely accelerate spontaneous passage of single and symptomatic CBD stones ≤ 10 mm in diameter, especially for stones < 5 mm. PMID:24151390

Relationship between serum bilirubin concentrations and diabetic nephropathy in Shanghai Han's patients with type 1 diabetes mellitus.

PubMed

Li, Xu; Zhang, Lei; Chen, Haibing; Guo, Kaifeng; Yu, Haoyong; Zhou, Jian; Li, Ming; Li, Qing; Li, Lianxi; Yin, Jun; Liu, Fang; Bao, Yuqian; Han, Junfeng; Jia, Weiping

2017-03-31

Recent studies highlight a negative association between total bilirubin concentrations and albuminuria in patients with type 2 diabetes mellitus. Our study evaluated the relationship between bilirubin concentrations and the prevalence of diabetic nephropathy (DN) in Chinese patients with type 1 diabetes mellitus (T1DM). A total of 258 patients with T1DM were recruited and bilirubin concentrations were compared between patients with or without diabetic nephropathy. Multiple stepwise regression analysis was used to examine the relationship between bilirubin concentrations and 24 h urinary microalbumin. Binary logistic regression analysis was performed to assess independent risk factors for diabetic nephropathy. Participants were divided into four groups according to the quartile of total bilirubin concentrations (Q1, 0.20-0.60; Q2, 0.60-0.80; Q3, 0.80-1.00; Q4, 1.00-1.90 mg/dL) and the chi-square test was used to compare the prevalence of DN in patients with T1DM. The median bilirubin level was 0.56 (interquartile: 0.43-0.68 mg/dL) in the DN group, significantly lower than in the non-DN group (0.70 [interquartile: 0.58-0.89 mg/dL], P < 0.001). Spearman's correlational analysis showed bilirubin concentrations were inversely correlated with 24 h urinary microalbumin (r = -0.13, P < 0.05) and multiple stepwise regression analysis showed bilirubin concentrations were independently associated with 24 h urinary microalbumin. In logistic regression analysis, bilirubin concentrations were significantly inversely associated with nephropathy. In addition, in stratified analysis, from the first to the fourth quartile group, increased bilirubin concentrations were associated with decreased prevalence of DN from 21.90% to 2.00%. High bilirubin concentrations are independently and negatively associated with albuminuria and the prevalence of DN in patients with T1DM.

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

Recurrence of superficial vein thrombosis in patients with varicose veins.

PubMed

Karathanos, Christos; Spanos, Konstantinos; Saleptsis, Vassileios; Tsezou, Aspasia; Kyriakou, Despina; Giannoukas, Athanasios D

2016-08-01

To investigate which factors other than history of superficial vein thrombosis (SVT) are associated with recurrent spontaneous SVT episodes in patients with varicose veins (VVs). Patients with a history of spontaneous SVT and VVs were followed up for a mean period of 55 months. Demographics, comorbidities, and thrombophilia screening test were analyzed. Patients were grouped according to the clinical-etiology-anatomy-pathophysiology classification. A multiple logistic regression analysis with the forward likelihood ratio method was undertaken. Thirteen patients out of 97 had a recurrence SVT episode during the follow-up period. All those patients were identified to have a thrombophilia defect. Protein C and S, antithrombin, and plasminogen deficiencies were more frequently present in patients without recurrence. Gene mutations were present in 38% in the nonrecurrence group and 77% in the recurrence group. After logistic regression analysis, patients with dislipidemia and mutation in prothrombin G20210A (FII) had an increased risk for recurrence by 5.4-fold and 4.6-fold, respectively. No deep vein thrombosis or pulmonary embolism occurred. Dislipidemia and gene mutations of F II are associated with SVT recurrence in patients with VVs. A selection of patients may benefit from anticoagulation in the short term and from VVs intervention in the long term. © The Author(s) 2015.

Increasing socioeconomic inequality in childhood undernutrition in urban India: trends between 1992-93, 1998-99 and 2005-06.

PubMed

Kumar, Abhishek; Kumari, Divya; Singh, Aditya

2015-10-01

This article examines the trends and pattern in socioeconomic inequality in stunting, underweight and wasting among children aged <3 years in urban India over a 14-year period. We use three successive rounds of the National Family Health Survey data conducted during 1992-93, 1998-99 and 2005-06. The selected socioeconomic predictors are household wealth and mother's education level. We use principal component analysis to compute a separate wealth index for urban India for all three rounds of the survey. We have used descriptive statistics, concentration index and pooled logistic regression to analyse the data. The results show that between 1992-93 and 2005-06, the prevalence of childhood undernutrition has declined across household wealth quintiles and educational level of mothers. However, the pace of decline is much higher among the better-off socioeconomic groups than among the least-affluent groups. The result of pooled logistic regression analysis shows that the socioeconomic inequality in childhood undernutrition in urban India has increased over the study period. The salient findings of this study call for separate programmes targeting the children of lower socioeconomic groups in urban population of India. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine © The Author 2014; all rights reserved.

Lower limb and associated injuries in frontal-impact road traffic collisions.

PubMed

Ammori, Mohannad B; Eid, Hani O; Abu-Zidan, Fikri M

2016-03-01

To study the relationship between severity of injury of the lower limb and severity of injury of the head, thoracic, and abdominal regions in frontal-impact road traffic collisions. Consecutive hospitalised trauma patients who were involved in a frontal road traffic collision were prospectively studied over 18 months. Patients with at least one Abbreviated Injury Scale (AIS) ≥3 or AIS 2 injuries within two AIS body regions were included. Patients were divided into two groups depending on the severity of injury to the head, chest or abdomen. Low severity group had an AIS < 2 and high severity group had an AIS ≥ 2. Backward likelihood logistic regression models were used to define significant factors affecting the severity of head, chest or abdominal injuries. Eighty-five patients were studied. The backward likelihood logistic regression model defining independent factors affecting severity of head injuries was highly significant (p =0.01, nagelkerke r square = 0.1) severity of lower limb injuries was the only significant factor (p=0.013) having a negative correlation with head injury (Odds ratio of 0.64 (95% CI: 0.45-0.91). Occupants who sustain a greater severity of injury to the lower limb in a frontal-impact collision are likely to be spared from a greater severity of head injury.

[Risk factors for anorexia in children].

PubMed

Liu, Wei-Xiao; Lang, Jun-Feng; Zhang, Qin-Feng

2016-11-01

To investigate the risk factors for anorexia in children, and to reduce the prevalence of anorexia in children. A questionnaire survey and a case-control study were used to collect the general information of 150 children with anorexia (case group) and 150 normal children (control group). Univariate analysis and multivariate logistic stepwise regression analysis were performed to identify the risk factors for anorexia in children. The results of the univariate analysis showed significant differences between the case and control groups in the age in months when supplementary food were added, feeding pattern, whether they liked meat, vegetables and salty food, whether they often took snacks and beverages, whether they liked to play while eating, and whether their parents asked them to eat food on time (P<0.05). The results of the multivariate logistic regression analysis showed that late addition of supplementary food (OR=5.408), high frequency of taking snacks and/or drinks (OR=11.813), and eating while playing (OR=6.654) were major risk factors for anorexia in children. Liking of meat (OR=0.093) and vegetables (OR=0.272) and eating on time required by parents (OR=0.079) were protective factors against anorexia in children. Timely addition of supplementary food, a proper diet, and development of children's proper eating and living habits can reduce the incidence of anorexia in children.

A case-control study evaluating relative risk factors for decompression sickness: a research report.

PubMed

Suzuki, Naoko; Yagishita, Kazuyosi; Togawa, Seiichiro; Okazaki, Fumihiro; Shibayama, Masaharu; Yamamoto, Kazuo; Mano, Yoshihiro

2014-01-01

Factors contributing to the pathogenesis of decompression sickness (DCS) in divers have been described in many studies. However, relative importance of these factors has not been reported. In this case-control study, we compared the diving profiles of divers experiencing DCS with those of a control group. The DCS group comprised 35 recreational scuba divers who were diagnosed by physicians as having DCS. The control group consisted of 324 apparently healthy recreational divers. All divers conducted their dives from 2009 to 2011. The questionnaire consisted of 33 items about an individual's diving profile, physical condition and activities before, during and just after the dive. To simplify dive parameters, the dive site was limited to Izu Osezaki. Odds ratios and multiple logistic regression were used for the analysis. Odds ratios revealed several items as dive and health factors associated with DCS. The major items were as follows: shortness of breath after heavy exercise during the dive (OR = 12.12), dehydration (OR = 10.63), and maximum dive depth > 30 msw (OR = 7.18). Results of logistic regression were similar to those by odds ratio analysis. We assessed the relative weights of the surveyed dive and health factors associated with DCS. Because results of several factors conflict with previous studies, future studies are needed.

Military Versus Civilian Murder-Suicide.

PubMed

Patton, Christina L; McNally, Matthew R; Fremouw, William J

2015-07-03

Previous studies have implicated significant differences between military members and civilians with regard to violent behavior, including suicide, domestic violence, and harm to others, but none have examined military murder-suicide. This study sought to determine whether there were meaningful differences between military and civilian murder-suicide perpetrators. Using data from the Center for Disease Control's (CDC) National Violent Death Reporting System (NVDRS), military (n = 259) and civilian (n = 259) murder-suicide perpetrators were compared on a number of demographic, psychological, and contextual factors using chi-square analyses. Logistic regression was used to determine which variables predicted membership to the military or civilian perpetrator groups. Military murder-suicide perpetrators were more likely to be older, have physical health problems, be currently or formerly married, less likely to abuse substances, and to exhibit significantly different motives than civilian perpetrators. Logistic regression revealed that membership to the military, rather than the civilian, perpetrator group was predicted by age, physical health problems, and declining heath motive-reflecting the significance of a more than 15-year difference in mean age between the two groups. Findings point to the need to tailor suicide risk assessments to include questions specific to murder-suicide, to assess attitudes toward murder-suicide, and to the importance of assessing suicide and violence risk in older adult military populations. © The Author(s) 2015.

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

PubMed

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

2016-01-01

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

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 ...

Radiation dose rates of differentiated thyroid cancer patients after 131I therapy.

PubMed

Jin, Pingyan; Feng, Huijuan; Ouyang, Wei; Wu, Juqing; Chen, Pan; Wang, Jing; Sun, Yungang; Xian, Jialang; Huang, Liuhua

2018-05-01

Postoperative 131 I treatment for differentiated thyroid cancer (DTC) can create a radiation hazard for nearby persons. The present prospective study aimed to investigate radiation dose rates in 131 I-treated DTC patients to provide references for radiation protection. A total of 141 131 I-treated DTC patients were enrolled, and grouped into a singular treatment (ST) group and a repeated treatment (RT) group. The radiation dose rate of 131 I-treated patients was measured. The rate of achieving discharge compliance and restricted contact time were analyzed based on Chinese regulations. Multivariate logistic regression analysis was used to analyze the independent factors associated with the clearance of radioiodine. The rate of achieving discharge compliance ( 131 I retention < 400 MBq) was 79.8 and 93.7% at day 2 (D2) for the ST and RT groups, respectively, and reached 100% at D7 and D4, respectively. The restricted contact time with 131 I-treated patients at 0.5 m for medical staff, caregivers, family members, and the general public ranged from 4 to 7 days. Multivariate logistic regression analysis showed that the 24-h iodine uptake rate was the only significant factor associated with radioiodine clearance. For the radiation safety of 131 I-treated DTC patients, the present results can provide radiometric data for radiation protection.

Incidence and risk factors of axial symptoms after cervical disc arthroplasty: a minimum 5-year follow-up study.

PubMed

Chen, Jing; Li, Jia; Qiu, Gang; Wei, Jingchao; Qiu, Yanfen; An, Yonghui; Shen, Yong

2016-09-20

The purpose of this study was to investigate whether uncovertebral joint ossification was a risk factor for axial symptoms (AS) after cervical disc arthroplasty (CDA). This retrospective study included 52 consecutive patients who underwent CDA for single-level cervical disc disease. To examine possible risk factors for AS after CDA, univariate and multivariate logistic regression analyses were conducted to compare data from the patients with and without AS (the AS and no-AS groups, respectively). Among the 52 patients examined, AS were observed in 24 patients (46.2 %), including a stiff neck (n = 11), neck pain and dullness (n = 10), and shoulder pain (n = 3). Uncovertebral joint ossification was detected in 22 (42.3 %) patients, including 17 patients in the AS group and 5 patients in the no-AS group. Clinical outcome improved during the follow-up period for the AS group. According to multivariate logistic regression analysis, uncovertebral joint ossification, cervical kyphosis, and range of motion (ROM) at the index level were identified as significant risk factors for AS after CDA. Satisfactory clinical outcomes were observed following CDA for the treatment of single-level cervical disc disease in the present cohort. In addition, uncovertebral joint ossification, cervical kyphosis, and ROM at the index level were found to affect the incidence of AS after CDA.

The Effect of Sitagliptin on the Regression of Carotid Intima-Media Thickening in Patients with Type 2 Diabetes Mellitus: A Post Hoc Analysis of the Sitagliptin Preventive Study of Intima-Media Thickness Evaluation.

PubMed

Mita, Tomoya; Katakami, Naoto; Shiraiwa, Toshihiko; Yoshii, Hidenori; Gosho, Masahiko; Shimomura, Iichiro; Watada, Hirotaka

2017-01-01

Background. The effect of dipeptidyl peptidase-4 (DPP-4) inhibitors on the regression of carotid IMT remains largely unknown. The present study aimed to clarify whether sitagliptin, DPP-4 inhibitor, could regress carotid intima-media thickness (IMT) in insulin-treated patients with type 2 diabetes mellitus (T2DM). Methods . This is an exploratory analysis of a randomized trial in which we investigated the effect of sitagliptin on the progression of carotid IMT in insulin-treated patients with T2DM. Here, we compared the efficacy of sitagliptin treatment on the number of patients who showed regression of carotid IMT of ≥0.10 mm in a post hoc analysis. Results . The percentages of the number of the patients who showed regression of mean-IMT-CCA (28.9% in the sitagliptin group versus 16.4% in the conventional group, P  = 0.022) and left max-IMT-CCA (43.0% in the sitagliptin group versus 26.2% in the conventional group, P  = 0.007), but not right max-IMT-CCA, were higher in the sitagliptin treatment group compared with those in the non-DPP-4 inhibitor treatment group. In multiple logistic regression analysis, sitagliptin treatment significantly achieved higher target attainment of mean-IMT-CCA ≥0.10 mm and right and left max-IMT-CCA ≥0.10 mm compared to conventional treatment. Conclusions . Our data suggested that DPP-4 inhibitors were associated with the regression of carotid atherosclerosis in insulin-treated T2DM patients. This study has been registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN000007396).

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

PubMed Central

Dipnall, Joanna F.

2016-01-01

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

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

PubMed

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

2016-01-01

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

Community Savings Groups, Financial Security, and HIV Risk Among Female Sex Workers in Iringa, Tanzania.

PubMed

Mantsios, Andrea; Galai, Noya; Mbwambo, Jessie; Likindikoki, Samuel; Shembilu, Catherine; Mwampashi, Ard; Beckham, S W; Leddy, Anna; Davis, Wendy; Sherman, Susan; Kennedy, Caitlin; Kerrigan, Deanna

2018-02-24

This study assessed the association between community savings group participation and consistent condom use (CCU) among female sex workers (FSW) in Iringa, Tanzania. Using cross-sectional data from a survey of venue-based FSW (n = 496), logistic regression was used to examine the associations between financial indicators including community savings group participation and CCU. Over one-third (35%) of the women participated in a savings group. Multivariable regression results indicated that participating in a savings group was significantly associated with nearly two times greater odds of CCU with new clients in the last 30 days (aOR = 1.77, 95% CI 1.10-2.86). Exploratory mediation analysis indicated that the relationship between savings group participation and CCU was partially mediated by financial security, as measured by monthly income. Findings indicate that community savings groups may play an important role in reducing sexual risk behaviors of FSW and hold promise as part of comprehensive, community-led HIV prevention strategies among FSW.

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.

Solo and Multi-Offenders Who Commit Stranger Kidnapping: An Assessment of Factors That Correlate With Violent Events.

PubMed

Cunningham, Shannon N; Vandiver, Donna M

2016-03-06

Research has demonstrated that co-offending dyads and groups often use more violence than individual offenders. Despite the attention given to co-offending by the research community, kidnapping remains understudied. Stranger kidnappings are more likely than non-stranger kidnappings to involve the use of a weapon. Public fear of stranger kidnapping warrants further examination of this specific crime, including differences between those committed by solo and multi-offender groups. The current study uses National Incident-Based Reporting System (NIBRS) data to assess differences in use of violence among 4,912 stranger kidnappings by solo offenders and multi-offender groups using cross-tabulations, ordinal regression, and logistic regression. The results indicate that violent factors are significantly more common in multi-offender incidents, and that multi-offender groups have fewer arrests than solo offenders. The implications of these findings are discussed. © The Author(s) 2016.

Are low wages risk factors for hypertension?

PubMed Central

Du, Juan

2012-01-01

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

Are low wages risk factors for hypertension?

PubMed

Leigh, J Paul; Du, Juan

2012-12-01

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

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.

The association between the RAGE G82S polymorphism, sRAGE and chronic periodontitis in Taiwanese individuals with and without diabetes.

PubMed

Wu, T-L; Tsai, C-C; Wang, Y-Y; Ho, K-Y; Wu, Y-M; Hung, H-C; Lin, Y-C

2015-12-01

The present study investigated the association between the RAGE G82S polymorphism, the plasma levels of sRAGE and chronic periodontitis in subjects with and without diabetes mellitus (DM). A total of 230 patients with DM and 264 non-DM participants were recruited for this study. Genotyping of the RAGE G82S polymorphism was accomplished using polymerase chain reaction-restriction fragment length polymorphism, and associations were analyzed with the chi-squared test and logistic regression analysis. In the non-DM group, the chi-squared test showed that the frequency distributions of the G82S polymorphism were significantly different between chronic periodontitis and non-chronic periodontitis subjects (χ(2) = 8.39, p = 0.02). A multivariate logistic regression model showed that the (G82S + S82S) genotypes were associated with a significantly increased risk of chronic periodontitis development compared to the G82G genotype (adjusted odds ratio = 2.06, 95% confidence interval: 1.08-4.07). In the DM group, there was no association between the G82S polymorphism and chronic periodontitis development when a multivariate logistic regression was performed. Plasma levels of sRAGE were significantly higher in subjects with the G82G genotype compared to those with the (G82S + S82S) genotypes in both the non-DM (856.6 ± 332.0 vs. 720.4 ± 311.4 pg/mL, p = 0.003) and DM groups (915.3 ± 497.1 vs. 603.5 ± 298.3 pg/mL, p < 0.0001). However, there was no difference in plasma sRAGE levels between chronic periodontitis and non-chronic periodontitis subjects in both the DM and non-DM groups. Moreover, when the subjects were further sub-divided by the G82S polymorphism, the difference in plasma levels of sRAGE between chronic periodontitis and non-chronic periodontitis subjects in the DM and non-DM groups remained statistically insignificant. The present study revealed that the RAGE G82S polymorphism was associated with chronic periodontitis in the non-DM group but not in the DM group. Our results also showed that the plasma levels of sRAGE were significantly higher in subjects with the RAGE G82G genotype, and this correlation was not affected by the presence of chronic periodontitis in the DM and non-DM groups. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

[Influence of coke oven emissions on workers' blood pressure and electrocardiographic findings].

PubMed

Liang, J J; Yi, G L; Mao, G S; Wang, D M; Dai, X Y

2016-09-20

Objective: To investigate the influence of coke oven emissions on workers' blood pressure and electrocardiographic findings, and to provide a basis for the prevention and treatment of cardiovascular diseases. Methods: The concentration of coke oven emissions at the bottom, side, and top of coke ovens was determined in a coking plant. A total of 406 coke oven workers were enrolled as exposure group and 201 office staff members were enrolled as control group. Blood pressure and electrocardiographic findings were compared between the two groups, and the multivariate logistic regression analysis was performed to analyze the influencing factors for hypertension and abnormal electrocardiographic findings. Results: The concentration of coke oven emissions was the highest at the top of coke ovens, followed by the side and bottom of coke ovens, and there was a significant difference between the exposure group and the control group ( P <0.01). The exposure group had significantly higher detection rates of hypertension, abnormal electrocardiographic findings, and abnormal chest X-ray findings than the control group ( P <0.05). The logistic regression analysis showed that high concentration of coke oven emission and age were risk factors for hypertension and abnormal electrocardiographic findings ( P <0.05). The workers exposed to high-concentration coke oven emissions were more likely to experience hypertension and abnormal electrocardiographic findings than those exposed to low-concentration coke oven emissions ( OR =1.7 and 1.9). Conclusion: Besides lung injury, coke oven emissions also have adverse effects on the cardiovascular system. Therefore, more effective measures are needed to protect the health of coke oven workers.

Thoracic Inlet Parameters for Degenerative Cervical Spondylolisthesis Imaging Measurement.

PubMed

Wang, Quanbing; Wang, Xiao-Tao; Zhu, Lei; Wei, Yu-Xi

2018-04-05

BACKGROUND The aim of this study was to explore the diagnostic value of sagittal measurement of thoracic inlet parameters for degenerative cervical spondylolisthesis (DCS). MATERIAL AND METHODS We initially included 65 patients with DCS and the same number of health people as the control group by using cervical radiograph evaluations. We analyzed the x-ray and computer tomographic (CT) data in prone and standing position at the same time. Measurement of cervical sagittal parameters was carried out in a standardized supine position. Multivariate logistic regression analysis was performed to evaluate these parameters as a diagnostic index for DCS. RESULTS There were 60 cases enrolled in the DCS group, and 62 cases included in the control group. The T1 slope and thoracic inlet angle (TIA) were significantly greater for the DCS group compared to the control group (24.33±2.85º versus 19.59±2.04º, p=0.00; 76.11±9.82º versus 72.86±7.31º, p=0.03, respectively). We observed no significant difference for the results of the neck tilt (NT), C2-C7 angle in the control and the DSC group (p>0.05). Logistic regression analysis and receiver operating characteristic (ROC) curve revealed that preoperative T1 slope of more than 22.0º showed significantly diagnostic value for the DCS group (p<0.05). CONCLUSIONS Patients with preoperative sagittal imbalance of thoracic inlet have a statistically significant increased risk of DCS. T1 slope of more than 22.0º showed significantly diagnostic value for the incidence of DCS.

Parental, peer and school experiences as predictors of alcohol drinking among first and second generation immigrant adolescents in Israel.

PubMed

Walsh, Sophie D; Djalovski, Amir; Boniel-Nissim, Meyran; Harel-Fisch, Yossi

2014-05-01

Ecological perspectives stress the importance of environmental predictors of adolescent alcohol use, yet little research has examined such predictors among immigrant adolescents. This study examines parental, peer and school predictors of alcohol drinking (casual drinking, binge drinking and drunkenness) among Israeli-born adolescents and first and second generation adolescent immigrants from the Former Soviet Union (FSU) and Ethiopia in Israel. The study uses data from the 2010 to 2011 Israeli Health Behaviors of School age Children (HBSC) survey and includes a representative sample of 3059 adolescents, aged 11-17. Differences between the groups for drinking were examined using Pearson's chi square. Logistic regression models were used to examine group specific predictors of drinking. First generation FSU and both Ethiopian groups reported higher levels of binge drinking and drunkenness than Israeli-born adolescents. All immigrant groups reported lower levels of parental monitoring than native born adolescents; both first generation groups reported difficulties talking to parents; and first generation FSU and second generation Ethiopian adolescents reported greater time with friends. Group specific logistic regression models suggest that while parent, peer and school variables all predicted alcohol use among Israeli adolescents, only time spent with peers consistently predicted immigrant alcohol use. Findings highlight specific vulnerability of first generation FSU and second generation Ethiopian adolescents to high levels of drinking and the salience of time spent with peers as predicting immigrant adolescent drinking patterns. They suggest that drinking patterns must be understood in relation to country of origin and immigration experience of a particular group. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Impact of hypertension with or without diabetes on left ventricular remodeling in rural Chinese population: a cross-sectional study.

PubMed

Li, Tan; Chen, Shuang; Guo, Xiaofan; Yang, Jun; Sun, Yingxian

2017-07-27

The aim of this study was to assess the impact of hypertension with or without diabetes on left ventricular (LV) remodeling in rural Chinese population. A total of 10,270 participants were classified into control group, hypertension without diabetes (HT) group, and hypertension with diabetes (HT + DM) group. We compared clinical characteristics and echocardiographic parameters, and used multivariable logistic regression analysis to assess the associations of interest. HT + DM group had higher interventricular septal thickness (IVSd), posterior wall thickness (PWTd), left ventricular mass (LVM), LVM index (LVMI), relative wall thickness (RWT), left atrial diameter (LAD), A wave and lower E wave than HT group (all P < 0.05). The prevalence rates of left ventricular hypertrophy (LVH) and abnormal geometry were statistically different among three groups (P < 0.001) and eccentric hypertrophy was the highest proportion of geometry abnormality. Logistic regression analysis suggested that subjects in HT and HT + DM groups had odds ratio (OR) values of 2.81, 4.41, 2.24 and 3.94, 7.20, 2.38 for LVH, concentric hypertrophy and eccentric hypertrophy in the total population, respectively, compared to control group. When compared with HT group, those in HT + DM group had approximately 1.40-, 1.61- and 1.38-, 1.71-fold increased risk for LVH and concentric hypertrophy in the total and female population separately, but no association of HT + DM with LVH and abnormal geometrical patterns was found in men. This study demonstrated that, to varying degrees, hypertension was associated with LV remodeling in rural Chinese population, and this risk association was obviously increased for LVH and concentric hypertrophy when accompanied by diabetes, especially for women.

The Association between Androgenic Hormone Levels and the Risk of Developing Coronary Artery Disease (CAD).

PubMed

Allameh, Farzad; Pourmand, Gholamreza; Bozorgi, Ali; Nekuie, Sepideh; Namdari, Farshad

2016-01-01

The aim of the study was to evaluate the relationship between the serum levels of androgens and Coronary Artery Disease (CAD) in an Iranian population. Male individuals admitted to Tehran Heart Center and Sina Hospital, Tehran, Iran from 2011-2012 were categorized into CAD and control groups based on selective coronary angiography. Baseline demographic data, including age, BMI, diabetes, and a history of hypertension were recorded. Patients were also assessed for their serum levels of total testosterone, free testosterone, estradiol, dehydroepi and rosterone sulfate (DHEA-S), and Sex Hormone Binding Globulin (SHBG). Data analysis was carried out chi-square and ANOVA tests as well as logistic regression analysis. Two hundred patients were in the CAD group and 135 individuals in control group. In the CAD group, 69 had single-vessel disease, 49 had two-vessel diseases, and 82 had three-vessel diseases. Statistically significant differences were observed between the individuals in the two groups with respect to age (P<0.0001), diabetes (P<0.0001), and a history of hypertension (P=0.018). The serum levels of free testosterone (P=0.048) and DHEA-S (P<0.0001) were significantly higher in the control group than in the CAD group; however, the serum level of SHBG was higher in the CAD group than in the control group (P=0.007). Results of the logistic regression analysis indicated that only age (P=0.042) and diabetes (P=0.003) had significant relationships with CAD. Although the serum levels of some of the androgens were significantly different between the two groups, no association was found between androgenic hormone levels and the risk of CAD, due mainly to the effect of age and diabetes.

Poor dental hygiene and periodontal health in nursing home residents with dementia: an observational study.

PubMed

Zenthöfer, Andreas; Baumgart, Dominik; Cabrera, Tomas; Rammelsberg, Peter; Schröder, Johannes; Corcodel, Nicoleta; Hassel, Alexander Jochen

2017-04-01

Poor oral health conditions are well documented in the institutionalized elderly, but the literature is lacking research on relationships between dementia and periodontal health in nursing home residents. The purpose of this cohort study, therefore, was to assess whether dementia is associated with poor oral health/denture hygiene and an increased risk of periodontal disease in the institutionalized elderly. A total of 219 participants were assessed using the Mini Mental State Examination (MMSE) to determine cognitive state. According to the MMSE outcome, participants scoring ≤20 were assigned to dementia group (D) and those scoring >20 to the non-dementia group (ND), respectively. For each of the groups D and ND, Gingival Bleeding Index (GBI) and Denture Hygiene Index (DHI) linear regression models were used with the confounders age, gender, dementia, number of comorbidities and number of permanent medications. To assess the risk factors for severe periodontitis as measured by the Community Index of Periodontal Treatment Needs, a logistic regression analysis was performed. Statistical analysis revealed no significant differences of GBI as well of DHI for demented and healthy subjects (p > 0.05). Severe periodontitis was detected in 66 % of participants with dementia. The logistic regression showed a 2.9 times increased risk among demented participants (p = 0.006). Oral hygiene, denture hygiene and periodontal health are poor in nursing home residents. The severity of oral problems, primarily periodontitis, seems to be enhanced in subjects suffering from dementia. Longitudinal observations are needed to clarify the cause-reaction relationship.

Effects of edaravone on early outcomes in acute ischemic stroke patients treated with recombinant tissue plasminogen activator.

PubMed

Wada, Tomoki; Yasunaga, Hideo; Inokuchi, Ryota; Horiguchi, Hiromasa; Fushimi, Kiyohide; Matsubara, Takehiro; Nakajima, Susumu; Yahagi, Naoki

2014-10-15

We investigated whether edaravone could improve early outcomes in acute ischemic stroke patients treated with recombinant tissue plasminogen activator (rtPA). We conducted a retrospective cohort study using the Japanese Diagnosis Procedure Combination database. We identified patients admitted with a primary diagnosis of ischemic stroke from 1 July 2010 to 31 March 2012 and treated with rtPA on the same day of stroke onset or the following day. Thereafter, we selected those who received edaravone on the same day of rtPA administration (edaravone group), and those who received rtPA without edaravone (control group). The primary outcomes were modified Rankin Scale (mRS) scores at discharge. One-to-one propensity-score matching was performed between the edaravone and control groups. An ordinal logistic regression analysis for mRS scores at discharge was performed with adjustment for possible variables as well as clustering of patients within hospitals using a generalized estimating equation. We identified 6336 eligible patients for inclusion in the edaravone group (n=5979; 94%) and the control group (n=357; 6%) as the total population. In 356 pairs of the propensity-matched population, the ordinal logistic regression analysis showed that edaravone was significantly associated with lower mRS scores of patients at discharge (adjusted odds ratio: 0.74; 95% confidence interval: 0.57-0.96). Edaravone may improve early outcomes in acute ischemic stroke patients treated with rtPA. Copyright © 2014 Elsevier B.V. All rights reserved.

Child sex tourism - prevalence of and risk factors for its use in a German community sample.

PubMed

Koops, Thula; Turner, Daniel; Neutze, Janina; Briken, Peer

2017-04-20

To investigate the prevalence of child sex tourism (CST) in a large German community sample, and to compare those who made use of CST with other child sexual abusers regarding established characteristics and risk factors for child sexual abuse. Adult German men were recruited through a German market research panel and questioned by means of an anonymous online survey. Group assignment was accomplished based on information on previous sexual contacts with children and previous use of CST. Characteristics and risk factors were compared between the groups using t- and Chi-square tests. Binary logistic regression analysis was performed to predict CST. Data collection was conducted in 2013, data analysis in January 2015. Out of 8718 men, 36 (0.4%) reported CST use. The CST group differed from the nonCST group (n = 96; 1.1%) with regard to pedophilic sexual and antisocial behaviors as well as own experiences of sexual abuse. Social difficulties, pedophilic sexual interests, and hypersexuality were not distinct features in the CST group. Own experiences of sexual abuse, child prostitution use, and previous conviction for a violent offense predicted CST in a logistic regression model. This study is a first step to gain insight into the prevalence and characteristics of men using CST. Findings could help to augment prevention strategies against commercial forms of sexual abuse in developed as well as in developing countries by fostering the knowledge about the characteristics of perpetrators.

Rotational movements of mandibular two-implant overdentures.

PubMed

Kimoto, Suguru; Pan, Shaoxia; Drolet, Nicolas; Feine, Jocelyne S

2009-08-01

Clinicians have reported that their patients complain that their mandibular two-implant overdentures (IOD) rotate. Therefore, we studied the frequency and severity of rotation of IODs with two-ball attachments, how rotation may influence perceived satisfaction ratings of chewing ability, and the factors that are involved in the rotation of IODs. Seventy-nine participants were recruited and asked to rate their general satisfaction of their IODs, as well as their ability to chew foods, the existence of any mandibular denture rotation, and to what degree denture rotation bothered them. Data on participant sociodemographic, anatomical, and prosthesis characteristics were also collected. Student's t-test and logistic regression analyses were performed to analyze the differences between participants who did (R group) and did not report (NR group) denture rotation. Thirty-seven of 79 participants were aware of rotational movement in their IODs. These patients were significantly less satisfied with their chewing ability than those who felt no rotation (69.1 mm R group vs. 82.9 mm), and discomfort caused by the rotation bothered them moderately (39/100 mm). The multivariate logistic regression analysis revealed that the arrangement of the anterior teeth and the length of the denture are significantly associated with awareness of denture rotation. Thirty-eight percent in the R group and 31% in the NR group had non-scheduled visits. Rotational movement with a mandibular two-IOD has a negative effect on perceived chewing ability and is associated with anterior tooth arrangement and denture length.

Fall-related self-efficacy, not balance and mobility performance, is related to accidental falls in chronic stroke survivors with low bone mineral density

PubMed Central

Pang, Marco Y.C.; Eng, Janice J.

2011-01-01

Introduction Chronic stroke survivors with low bone mineral density (BMD) are particularly prone to fragility fractures. The purpose of this study was to identify the determinants of balance, mobility and falls in this sub-group of stroke patients. Methods Thirty nine chronic stroke survivors with low hip BMD (T-score <-1.0) were studied. Each subject was evaluated for: balance, mobility, leg muscle strength, spasticity, and falls-related self-efficacy. Any falls in the past 12 months were also recorded. Multiple regression analysis was used to identify the determinants of balance and mobility performance whereas logistic regression was used to identify the determinants of falls. Results Multiple regression analysis revealed that after adjusting for basic demographics, falls-related self-efficacy remained independently associated with balance/mobility performance (R2=0.494, P<0.001). Logistic regression showed that falls-related self-efficacy, but not balance and mobility performance, was a significant determinant of falls (odds ratio: 0.18, P=0.04). Conclusions Falls-related self-efficacy, but not mobility and balance performance, was the most important determinant of accidental falls. This psychological factor should not be overlooked in the prevention of fragility fractures among chronic stroke survivors with low hip BMD. PMID:18097709

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.

Application of Logistic Regression and Survival Analysis to the Study of CEP, Manpower Performance and Attrition

DTIC Science & Technology

1993-09-01

compared to the male counterparts, the study does not discriminate between the two sexes . Out of the total of about 17000 records, about 30% of them are...few naval officers and pilots. Almost all the officers are in the Army. Hence, for the support vocations and sevice groups effects the study does not

The Effect of College Selection Factors on Persistence: An Examination of Black and Latino Males in the Community College

ERIC Educational Resources Information Center

Wood, J. Luke; Harris, Frank, III

2015-01-01

The purpose of this study was to understand the relationship (if any) between college selection factors and persistence for Black and Latino males in the community college. Using data derived from the Educational Longitudinal Study, backwards stepwise logistic regression models were developed for both groups. Findings are contextualized in light…

Analysis of the Effects of the Commander’s Battle Positioning on Unit Combat Performance

DTIC Science & Technology

1991-03-01

Analysis ......... .. 58 Logistic Regression Analysis ......... .. 61 Canonical Correlation Analysis ........ .. 62 Descriminant Analysis...entails classifying objects into two or more distinct groups, or responses. Dillon defines descriminant analysis as "deriving linear combinations of the...object given it’s predictor variables. The second objective is, through analysis of the parameters of the descriminant functions, determine those

Can Statistical Modeling Increase Annual Fund Performance? An Experiment at the University of Maryland, College Park.

ERIC Educational Resources Information Center

Porter, Stephen R.

Annual funds face pressures to contact all alumni to maximize participation, but these efforts are costly. This paper uses a logistic regression model to predict likely donors among alumni from the College of Arts & Humanities at the University of Maryland, College Park. Alumni were grouped according to their predicted probability of donating…

The Association Between Long Working Hours and High-Sensitivity C-Reactive Protein in Older Aged Individuals: The Korea National Health and Nutrition Examination Survey (KNHANES) 2015.

PubMed

Lee, Jongin; Kim, Hyoung-Ryoul

2018-05-22

To show the association of hs-CRP level with working hours in different age groups. We used data from Korean National Health and Nutrition Survey. The odds ratios (ORs) and 95% confidence intervals (CIs) of variables for elevated hs-CRP (> 3.0 mg/L) were generated with logistic regression models. Significant variables were verified with an adjusted multivariate logistic model after stratification of age groups. Working for more than 55 hours per week was associated with elevated hs-CRP level in the old-ages group (≥ 60 years old: OR 2.18, 95% CI 1.07-4.45). Working for 40-55 hours per week was associated with decreased hs-CRP in the young-ages group (OR 0.58, 95% CI 0.37-0.93). Working hours appear to influence the levels of hs-CRP in individuals aged older than 60 years.

[Calf circumference and its association with gait speed in elderly participants at Peruvian Naval Medical Center].

PubMed

Díaz Villegas, Gregory Mishell; Runzer Colmenares, Fernando

2015-01-01

To evaluate the association between calf circumference and gait speed in elderly patients 65 years or older at Geriatric day clinic at Peruvian Centro Médico Naval. Cross-sectional, retrospective study. We assessed 139 participants, 65 years or older at Peruvian Centro Médico Naval including calf circumference, gait speed and Short Physical Performance Battery. With bivariate analyses and logistic regression model we search for association between variables. The age mean was 79.37 years old (SD: 8.71). 59.71% were male, the 30.97% had a slow walking speed and the mean calf circumference was 33.42cm (SD: 5.61). After a bivariate analysis, we found a calf circumference mean of 30.35cm (SD: 3.74) in the slow speed group and, in normal gait group, a mean of 33.51cm (SD: 3.26) with significantly differences. We used logistic regression to analyze association with slow gait speed, founding statistically significant results adjusting model by disability and age. Low calf circumference is associated with slow speed walk in population over 65 years old. Copyright © 2014. Published by Elsevier Espana.

Internal exposure levels of typical POPs and their associations with childhood asthma in Shanghai, China.

PubMed

Meng, Ge; Feng, Yan; Nie, Zhiqing; Wu, Xiaomeng; Wei, Hongying; Wu, Shaowei; Yin, Yong; Wang, Yan

2016-04-01

Polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) are common persistent organic pollutants (POPs) that may be associated with childhood asthma. The concentrations of PBDEs, PCBs and OCPs were analyzed in pooled serum samples from both asthmatic and non-asthmatic children. The differences in the internal exposure levels between the case and control groups were tested (p value <0.0012). The associations between the internal exposure concentrations of the POPs and childhood asthma were estimated based on the odds ratios (ORs) calculated using logistic regression models. There were significant differences in three PBDEs, 26 PCBs and seven OCPs between the two groups, with significantly higher levels in the cases. The multiple logistic regression models demonstrated that the internal exposure concentrations of a number of the POPs (23 PCBs, p,p'-DDE and α-HCH) were positively associated with childhood asthma. Some synergistic effects were observed when the children were co-exposed to the chemicals. BDE-209 was positively associated with asthma aggravation. This study indicates the potential relationships between the internal exposure concentrations of particular POPs and the development of childhood asthma. Copyright © 2015 Elsevier Inc. All rights reserved.

Bayesian logistic regression approaches to predict incorrect DRG assignment.

PubMed

Suleiman, Mani; Demirhan, Haydar; Boyd, Leanne; Girosi, Federico; Aksakalli, Vural

2018-05-07

Episodes of care involving similar diagnoses and treatments and requiring similar levels of resource utilisation are grouped to the same Diagnosis-Related Group (DRG). In jurisdictions which implement DRG based payment systems, DRGs are a major determinant of funding for inpatient care. Hence, service providers often dedicate auditing staff to the task of checking that episodes have been coded to the correct DRG. The use of statistical models to estimate an episode's probability of DRG error can significantly improve the efficiency of clinical coding audits. This study implements Bayesian logistic regression models with weakly informative prior distributions to estimate the likelihood that episodes require a DRG revision, comparing these models with each other and to classical maximum likelihood estimates. All Bayesian approaches had more stable model parameters than maximum likelihood. The best performing Bayesian model improved overall classification per- formance by 6% compared to maximum likelihood, with a 34% gain compared to random classification, respectively. We found that the original DRG, coder and the day of coding all have a significant effect on the likelihood of DRG error. Use of Bayesian approaches has improved model parameter stability and classification accuracy. This method has already lead to improved audit efficiency in an operational capacity.

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

PubMed

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

2016-02-01

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

[A survey of correlation between serum 25-hydroxyvitamin D levels and dyslipidemia rlsk among middle-aged individuals in Beijing].

PubMed

Zhang, L L; Lu, Y H; Cheng, X L; Liu, M Y; Sun, B R; Li, C L

2016-08-01

To evaluate vitamin D status in middle-aged subjects in Beijing and explore the correlation between serum 25-hydroxyvitamin D[25(OH)D] levels and dyslipidemia. A total of 448 individuals over 40 years old were enrolled in the cross-sectional survey. The general information, blood biochemical and lipid profiles and serum 25(OH)D levels were collected. The subjects were either divided into two groups (the dyslipidemia group and the non-dyslipidemia group) based on the lipid levels, or four groups according to quartiles of 25(OH)D levels. The association between 25(OH)D levels and dyslipidemia risk was analyzed by a logistic regression analysis. A total of 234 cases were in dyslipidemia group, which accounted for 52.23% of the subjects. The serum 25(OH)D levels were significantly lower in the dyslipidemia group than in the non-dyslipidemia group both in men and in women (all P<0.05). The median serum 25(OH)D level in the total subjects was 15.7 (12.2, 20.1)μg/L with 91.1% subjects of serum 25(OH)D level<30 μg/L. The proportion of subjects with dyslipidemia (high TC, high TG, high LDL-C, or low HDL-C) increased with the decrease of 25(OH)D level quartiles (P<0.05). After adjustment of confounding factors, the logistic regression analysis showed that subjects in the lowest 25(OH) D quartile group had 143% higher risks for dyslipidemia than those in the highest quartile group. These findings indicate that 25(OH)D insufficiency is highly prevalent among middle-aged individuals and it may be associated with the risk of dyslipidemia.

Advertising for Demand Creation for Voluntary Medical Male Circumcision.

PubMed

Wilson, Nicholas; Frade, Sasha; Rech, Dino; Friedman, Willa

2016-08-15

To measure the effects of information, a challenge, and a conditional cash transfer on take-up of voluntary medical male circumcision (VMMC). A randomized, controlled experiment with 4000 postcard recipients in Soweto (Johannesburg), South Africa. We examined differences in take-up of several decisions in the VMMC cascade between the control arm and each of several intervention arms using logistic regression. Logistic regression analysis indicated that the group offered US $10 as compensation and the group challenged with "Are you tough enough?" had significantly higher take-up of the VMMC procedure than did the control group [odds ratios, respectively, 5.30 (CI: 2.20 to 12.76) and 2.70 (CI: 1.05 to 6.91)]. Similarly, the compensation group had significantly higher take-up of the VMMC counseling session than did the control group [odds ratio 3.76 (CI: 1.79 to 7.89)]. The analysis did not reveal significantly different take-up of either the VMMC counseling session or the procedure in the partner preference information group compared with the control group [odds ratios, respectively, 1.23 (CI: 0.51 to 2.97) and 1.67 (CI: 0.61 to 4.62)]. The analysis did not reveal significantly higher take-up of the VMMC nurse hotline in any intervention group compared with the control group [odds ratios for US $10, information, and challenge, respectively, 1.17 (CI: 0.67 to 2.07), 0.69 (CI: 0.36 to 1.32), and 0.60 (0.31 to 1.18)]. Among adult males in Soweto, South Africa, compensation of US $10 provided conditional on completing the VMMC counseling session compared with no compensation offer and a postcard with a challenge, "Are you tough enough?" compared with no challenge, resulted in moderate increases in take-up of circumcision.

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.

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

PubMed

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

2016-06-01

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

Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

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

2013-01-01

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

Characteristics of insufficiently active participants that benefit from health-enhancing physical activity (HEPA) promotion programs implemented in the sports club setting.

PubMed

Ooms, Linda; Leemrijse, Chantal; Collard, Dorine; Schipper-van Veldhoven, Nicolette; Veenhof, Cindy

2018-06-01

Health-enhancing physical activity (HEPA) promotion programs are implemented in sports clubs. The purpose of this study was to examine the characteristics of the insufficiently active participants that benefit from these programs. Data of three sporting programs, developed for insufficiently active adults, were used for this study. These sporting programs were implemented in different sports clubs in the Netherlands. Participants completed an online questionnaire at baseline and after six months (n = 458). Of this sample, 35.1% (n = 161) was insufficiently active (i.e. not meeting HEPA levels) at baseline. Accordingly, two groups were compared: participants who were insufficiently active at baseline, but increased their physical activity to HEPA levels after six months (activated group, n = 86) versus participants who were insufficiently active both at baseline and after six months (non-activated group, n = 75). Potential associated characteristics (demographic, social, sport history, physical activity) were included as independent variables in bivariate and multivariate logistic regression analyses. The percentage of active participants increased significantly from baseline to six months (from 64.9 to 76.9%, p < 0.05). The bivariate logistic regression analyses showed that participants in the activated group were more likely to receive support from family members with regard to their sport participation (62.8% vs. 42.7%, p = 0.02) and spent more time in moderate-intensity physical activity (128 ± 191 min/week vs. 70 ± 106 min/week, p = 0.02) at baseline compared with participants in the non-activated group. These results were confirmed in the multivariate logistic regression analyses: when receiving support from most family members, there is a 216% increase in the odds of being in the activated group (OR = 2.155; 95% CI: 1.118-4.154, p = 0.02) and for each additional 1 min/week spent in moderate-intensity physical activity, the odds increases with 0.3% (OR = 1.003; 95% CI: 1.001-1.006, p = 0.02). The results suggest that HEPA sporting programs can be used to increase HEPA levels of insufficiently active people, but it seems a challenge to reach the least active ones. It is important that promotional strategies and channels are tailored to the target group. Furthermore, strategies that promote family support may enhance the impact of the programs.

Mediastinal micro-vessels clipping during lymph node dissection may contribute to reduce postoperative pleural drainage

PubMed Central

Yan, Shi; Wang, Xing; Lv, Chao; Phan, Kevin; Wang, Yuzhao; Wang, Jia; Yang, Yue

2016-01-01

Background Postoperative pleural drainage markedly influences the length of postoperative stay and financial costs of medical care. The aim of this study is to retrospectively investigate potentially predisposing factors related to pleural drainage after curative thoracic surgery and to explore the impact of mediastinal micro-vessels clipping on pleural drainage control after lymph node dissection. Methods From February 2012 to November 2013, 322 consecutive cases of operable non-small cell lung cancers (NSCLC) undergoing lobectomy and mediastinal lymph node dissection with or without application of clipping were collected. Total and daily postoperative pleural drainage were recorded. Propensity score matching (1:2) was applied to balance variables potentially impacting pleural drainage between group clip and group control. Analyses were performed to compare drainage volume, duration of chest tube and postoperative hospital stay between the two groups. Variables linked with pleural drainage in whole cohort were assessed using multivariable logistic regression analysis. Results Propensity score matching resulted in 197 patients (matched cohort). Baseline patient characteristics were matched between two groups. Group clip showed less cumulative drainage volume (P=0.020), shorter duration of chest tube (P=0.031) and postoperative hospital stay (P=0.022) compared with group control. Risk factors significantly associated with high-output drainage in multivariable logistic regression analysis were being male, age >60 years, bilobectomy/sleeve lobectomy, pleural adhesion, the application of clip applier, duration of operation ≥220 minutes and chylothorax (P<0.05). Conclusions This study suggests that mediastinal micro-vessels clipping during lymph node dissection may reduce postoperative pleural drainage and thus shorten hospital stay. PMID:27076936

Effectiveness of a training programme to improve hand hygiene compliance in primary healthcare

PubMed Central

2009-01-01

Background Hand hygiene is the most effective measure for preventing infections related to healthcare, and its impact on the reduction of these infections is estimated at 50%. Non-compliance has been highlighted in several studies in hospitals, although none have been carried out in primary healthcare. Main objective To evaluated the effect of a "Hand Hygiene for the reduction of healthcare-associated infections" training program for primary healthcare workers, measured by variation from correct hand hygiene compliance, according to regulatory and specific criteria, 6 months after the baseline, in the intervention group (group receiving a training program) and in the control group (a usual clinical practice). Secondary objectives -To describe knowledges, attitudes and behaviors as regards hand hygiene among the professionals, and their possible association with "professional burnout", stratifying the results by type of group (intervention and usual clinical practice). -To estimate the logistic regression model that best explains hand hygiene compliance. Methods/Design Experimental study of parallel groups, with a control group, and random assignment by Health Center. Area of study.- Health centers in north-eastern Madrid (Spain). Sample studied.- Healthcare workers (physicians, odontostomatologists, pediatricians, nurses, dental hygienists, midwife and nursing auxiliaries). Intervention.- A hand hygiene training program, including a theoretical-practical workshop, provision of alcohol-based solutions and a reminder strategy in the workplace. Other variables: sociodemographic and professional knowledges, attitudes, and behaviors with regard to hand hygiene. Statistical Analysis: descriptive and inferential, using multivariate methods (covariance analysis and logistic regression). Discussion This study will provide valuable information on the prevalence of hand hygiene non-compliance, and improve healthcare. PMID:20015368

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.

Risk Factors for Suicidal Ideation in People at Risk for Huntington's Disease.

PubMed

Anderson, Karen E; Eberly, Shirley; Groves, Mark; Kayson, Elise; Marder, Karen; Young, Anne B; Shoulson, Ira

2016-12-15

Suicidal ideation (SI) and attempts are increased in Huntington's disease (HD), making risk factor assessment a priority. To determine whether, hopelessness, irritability, aggression, anxiety, CAG expansion status, depression, and motor signs/symptoms were associated with Suicidal Ideation (SI) in those at risk for HD. Behavioral and neurological data were collected from subjects in an observational study. Subject characteristics were calculated by CAG status and SI. Logistic regression models were adjusted for demographics. Separate logistic regressions were used to compare SI and non-SI subjects. A combined logistic regression model, including 4 pre-specified predictors, (hopelessness, irritability, aggression, anxiety) was used to assess the relationship of SI to these predictors. 801 subjects were assessed, 40 were classified as having SI, 6.3% of CAG mutation expansion carriers had SI, compared with 4.3% of non- CAG mutation expansion carriers (p = 0.2275). SI subjects had significantly increased depression (p < 0.0001), hopelessness (p < 0.0001), irritability (p < 0.0001), aggression (p = 0.0089), and anxiety (p < 0.0001), and an elevated motor score (p = 0.0098). Impulsivity, assessed in a subgroup of subjects, was also associated with SI (p = 0.0267). Hopelessness and anxiety remained significant in combined model (p < 0.001; p < 0.0198, respectively) even when motor score was included. Behavioral symptoms were significantly higher in those reporting SI. Hopelessness and anxiety showed a particularly strong association with SI. Risk identification could assist in assessment of suicidality in this group.

Organizational Justice and Physiological Coronary Heart Disease Risk Factors in Japanese Employees: a Cross-Sectional Study.

PubMed

Inoue, Akiomi; Kawakami, Norito; Eguchi, Hisashi; Miyaki, Koichi; Tsutsumi, Akizumi

2015-12-01

Growing evidence has shown that lack of organizational justice (i.e., procedural justice and interactional justice) is associated with coronary heart disease (CHD) while biological mechanisms underlying this association have not yet been fully clarified. The purpose of the present study was to investigate the cross-sectional association of organizational justice with physiological CHD risk factors (i.e., blood pressure, high-density lipoprotein [HDL] cholesterol, low-density lipoprotein [LDL] cholesterol, and triglyceride) in Japanese employees. Overall, 3598 male and 901 female employees from two manufacturing companies in Japan completed self-administered questionnaires measuring organizational justice, demographic characteristics, and lifestyle factors. They completed health checkup, which included blood pressure and serum lipid measurements. Multiple logistic regression analyses and trend tests were conducted. Among male employees, multiple logistic regression analyses and trend tests showed significant associations of low procedural justice and low interactional justice with high triglyceride (defined as 150 mg/dL or greater) after adjusting for demographic characteristics and lifestyle factors. Among female employees, trend tests showed significant dose-response relationship between low interactional justice and high LDL cholesterol (defined as 140 mg/dL or greater) while multiple logistic regression analysis showed only marginally significant or insignificant odds ratio of high LDL cholesterol among the low interactional justice group. Neither procedural justice nor interactional justice was associated with blood pressure or HDL cholesterol. Organizational justice may be an important psychosocial factor associated with increased triglyceride at least among Japanese male employees.

Host Polymorphisms in TLR9 and IL10 Are Associated With the Outcomes of Experimental Haemophilus ducreyi Infection in Human Volunteers.

PubMed

Singer, Martin; Li, Wei; Morré, Servaas A; Ouburg, Sander; Spinola, Stanley M

2016-08-01

In humans inoculated with Haemophilus ducreyi, there are host effects on the possible clinical outcomes-pustule formation versus spontaneous resolution of infection. However, the immunogenetic factors that influence these outcomes are unknown. Here we examined the role of 14 single-nucleotide polymorphisms (SNPs) in 7 selected pathogen-recognition pathways and cytokine genes on the gradated outcomes of experimental infection. DNAs from 105 volunteers infected with H. ducreyi at 3 sites were genotyped for SNPs, using real-time polymerase chain reaction. The participants were classified into 2 cohorts, by race, and into 4 groups, based on whether they formed 0, 1, 2, or 3 pustules. χ(2) tests for trend and logistic regression analyses were performed on the data. In European Americans, the most significant findings were a protective association of the TLR9 +2848 GG genotype and a risk-enhancing association of the TLR9 TA haplotype with pustule formation; logistic regression showed a trend toward protection for the TLR9 +2848 GG genotype. In African Americans, logistic regression showed a protective effect for the IL10 -2849 AA genotype and a risk-enhancing effect for the IL10 AAC haplotype. Variations in TLR9 and IL10 are associated with the outcome of H. ducreyi infection. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Measurements of the talus in the assessment of population affinity.

PubMed

Bidmos, Mubarak A; Dayal, Manisha R; Adegboye, Oyelola A

2018-06-01

As part of their routine work, forensic anthropologists are expected to report population affinity as part of the biological profile of an individual. The skull is the most widely used bone for the estimation of population affinity but it is not always present in a forensic case. Thus, other bones that preserve well have been shown to give a good indication of either the sex or population affinity of an individual. In this study, the potential of measurements of the talus was investigated for the purpose of estimating population affinity in South Africans. Nine measurements from two hundred and twenty tali of South African Africans (SAA) and South African Whites (SAW) from the Raymond A. Dart Collection of Human Skeletons were used. Direct and step-wise discriminant function and logistic regression analyses were carried out using SPSS and SAS. Talar length was the best single variable for discriminating between these two groups for males while in females the head height was the best single predictor. Average accuracies for correct population affinity classification using logistic regression analysis were higher than those obtained from discriminant function analysis. This study was the first of its type to employ discriminant function analyses and logistic regression analyses to estimate the population affinity of an individual from the talus. Thus these equations can now be used by South African anthropologists when estimating the population affinity of dismembered or damaged or incomplete skeletal remains of SAA and SAW. Copyright © 2018 Elsevier B.V. All rights reserved.

[A case-control study on the risk factors of work-related acute pesticide poisoning among farmers from Jiangsu province].

PubMed

Tu, Zhi-bin; Cui, Meng-jing; Yao, Hong-yan; Hu, Guo-qing; Xiang, Hui-yun; Stallones, Lorann; Zhang, Xu-jun

2012-04-01

To explore the risk factors on cases regarding work-related acute pesticide poisoning among farmers of Jiangsu province. A population-based, 1:2 matched case-control study was carried out, with 121 patients as case-group paired by 242 persons with same gender, district and age less then difference of 3 years, as controls. Cases were the ones who had suffered from work-related acute pesticide poisoning. A unified questionnaire was used. Data base was established by EpiData 3.1, and SPSS 16.0 was used for both data single factor and multi-conditional logistics regression analysis. Results from the single factor logistic regression analysis showed that the related risk factors were: lack of safety guidance, lack of readable labels before praying pesticides, no regression during application, using hand to wipe sweat, using leaking knapsack, body contaminated during application and continuing to work when feeling ill after the contact of pesticides. Results from multi-conditional logistic regression analysis indicated that the lack of safety guidance (OR=2.25, 95%CI: 1.35-3.74), no readable labels before praying pesticides (OR=1.95, 95%CI: 1.19-3.18), wiping the sweat by hand during application (OR=1.97, 95%CI: 1.20-3.24) and using leaking knapsack during application (OR=1.82, 95%CI:1.10-3.01) were risk factors for the occurrence of work-related acute pesticide poisoning. The lack of safety guidance, no readable labels before praying pesticides, wiping the sweat by hand or using leaking knapsack during application were correlated to the occurrence of work-related acute pesticide poisoning.

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…

The association of benign prostatic hyperplasia with lower urinary tract stones in adult men: A retrospective multicenter study.

PubMed

Jung, Jae Hung; Park, Jinsung; Kim, Won Tae; Kim, Hong Wook; Kim, Hyung Joon; Hong, Sungwoo; Yang, Hee Jo; Chung, Hong

2018-04-01

To examine the relationship between benign prostatic hyperplasia (BPH) and the presence of lower urinary tract stones. We retrospectively reviewed the records of men with lower urinary tract stones who presented to three clinical centers in Korea over a 4-year period. We divided the patients into two groups based on the location of urinary stones: Group 1 (bladder calculi) and Group 2 (urethral calculi). We compared the characteristics of both groups and performed univariate and multivariate analyses with a logistic regression model to investigate the relationship between BPH and lower urinary tract stones. Of 221 patients, 194 (87.8%) had bladder calculi and 27 (12.2%) had urethral calculi. The mean age of Group 1 was higher than that of Group 2 (68.96 ± 12.11 years vs. 55.74 ± 14.20 years, p  < 0.001). The mean prostate volume of Group 1 was higher than that of Group 2 (44.47 ± 27.14 mL vs. 24.70 ± 6.41 mL, respectively, p  < 0.001). Multivariate logistic regression showed that age (OR = 1.075, 95%CI: 1.023-1.129) and prostate volume (OR = 1.069, 95%CI: 1.017-1.123) were independently associated with increased risk for bladder calculi. Upper urinary tract stones and/or hydronephrosis conferred a 3-fold risk for urethral calculi (OR = 3.468, 95%CI: 1.093-10.999). Age and prostate volume are independent risk factors for bladder calculi. In addition, men with upper urinary tract disease are at greater risk for urethral calculi, which may migrate from the upper urinary tract rather than from the bladder.

[Risk factors for lower extremity amputation in patients with diabetic foot].

PubMed

Xu, B; Yang, C Z; Wu, S B; Zhang, D; Wang, L N; Xiao, L; Chen, Y; Wang, C R; Tong, A; Zhou, X F; Li, X H; Guan, X H

2017-01-01

Objective: To explore the risk factors for lower extremity amputation in patients with diabetic foot. Methods: The clinical data of 1 771 patients with diabetic foot at the Air Force General Hospital of PLA from November 2001 to April 2015 were retrospectively analyzed. The patients were divided into the non-amputation and amputation groups. Within the amputation group, subjects were further divided into the minor and major amputation subgroups. Binary logistic regression analyses were used to assess the association between risk factors and lower extremity amputation. Results: Among 1 771 patients with diabetic foot, 323 of them (18.24%) were in the amputation group (major amputation: 41; minor amputation: 282) and 1 448 (81.76%) in the non-amputation group. Compared with non-amputation patients, those in the amputation group had a longer hospital stay and higher estimated glomerular filtration rate(eGFR)levels. Fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), C-reaction protein (CRP), ESR, ferritin, fibrinogen and WBC levels of the amputation group were higher, while hemoglobin albumin, transferrin, TC, TG, HDL-C and LDL-C were lower than those of the non-amputation group (all P <0.05). The proportion of hypertension(52.48% vs 59.98%), peripheral vascular disease (PAD)(68.11% vs 25.04%), and coronary heart disease(21.33% vs 28.71%)were different between the amputation and non-amputation groups (all P <0.05). Multivariable logistic regression analyses showed that Wagner's grade, PAD and CRP were the independent risk factors associated with lower extremity amputation in hospitalized patients with diabetic foot. Conclusion: Wagner's grade, ischemia of lower limbs and infection are closely associated with amputation of diabetic foot patients.

Risk factors affecting human traumatic tympanic membrane perforation regeneration therapy using fibroblast growth factor-2.

PubMed

Lou, Zhengcai; Yang, Jian; Tang, Yongmei; Xiao, Jian

2015-01-01

The use of growth factors to achieve closure of human traumatic tympanic membrane perforations (TMPs) has recently been demonstrated. However, pretreatment factors affecting healing outcomes have seldom been discussed. The objective of this study was to evaluate pretreatment factors contributing to the success or failure of healing of TMPs using fibroblast growth factor-2 (FGF-2). A retrospective cohort study of 99 patients (43 males, 56 females) with traumatic TMPs who were observed for at least 6 months after FGF-2 treatment between March 2011 and December 2012. Eleven factors considered likely to affect the outcome of perforation closure were evaluated statistically using univariate and multivariate logistic regression analysis. Each traumatic TMP was treated by direct application of FGF-2. Complete closure versus failure to close. In total, 99 patients were analyzed. The total closure rate was 92/99 (92.9%) at 6 months; the mean closure time was 10.59 ±  6.81 days. The closure rate did not significantly differ between perforations with or without inverted edges (100.0% vs. 91.4%, p = 0.087), among different size groups (p = 0.768), or among different periods of exposure to injury (p = 0.051). However, the closure rate was significantly different between the high- and low-dose FGF-2 groups (85.0% vs. 98.3%, p = 0.010) and between perforations where the umbo or malleus was or was not involved in perforation (85.4% vs. 98.3%, p = 0.012). Additionally, univariate logistic regression analysis tests showed that it was difficult to achieve healing of these perforations with a history of chronic otitis media or residual TM calcification (p = 0.006), the umbo or malleus was involved in perforation (p = 0.038), and with a high dose of FGF-2 (p = 0.035) compared with control groups. Multivariate logistic regression analysis showed that only a history of chronic otitis media and residual TM calcification and perforation close to the umbo or malleus were associated with non-healing of the TM perforation (p = 0.03 and p = 0.017, respectively) with relative risk factors. Direct application of FGF-2 can be used in all traumatic TMPs, the size of the perforation and inverted edges did not affect the closure rate, and the most beneficial dose was sufficient to keep the residual eardrum environment moist, but without adding liquid. Additionally, multivariate logistic regression analysis revealed that a large perforation was not a major risk factor for nonhealing of TM perforations. However, a history of chronic otitis media, residual TM calcification and involvement of the umbo or malleus in perforation were significant risk factors.

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.

Employment outcomes among African Americans and Whites with mental illness.

PubMed

Lukyanova, Valentina V; Balcazar, Fabricio E; Oberoi, Ashmeet K; Suarez-Balcazar, Yolanda

2014-01-01

People with mental illness often experience major difficulties in finding and maintaining sustainable employment. African Americans with mental illness have additional challenges to secure a job, as reflected in their significantly lower employment rates compared to Whites. To examine the factors that contribute to racial disparities in employment outcomes for African-American and White Vocational Rehabilitation (VR) consumers with mental illness. This study used VR data from a Midwestern state that included 2,122 African American and 4,284 White participants who reported mental illness in their VR records. Logistic regression analyses were conducted. African Americans had significantly more closures after referral and were closed as non-rehabilitated more often than Whites. Logistic regressions indicated that African Americans are less likely to be employed compared to Whites. The regression also found differences by gender (females more likely to find jobs than males) and age (middle age consumers [36 to 50] were more likely to find jobs than younger consumers [18 to 35]). Case expenditures between $1,000 and $4,999 were significantly lower for African Americans. VR agencies need to remain vigilant of potential discrepancies in service delivery among consumers from various ethnic groups and work hard to assure as much equality as possible.

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.

Cultural beliefs and clinical breast examination in Hmong American women: the crucial role of modesty.

PubMed

Lee, Hee Yun; Vang, Suzanne

2015-06-01

Despite grave cancer disparities in Hmong American women, investigation of the group's breast cancer screening behavior is sparse. This study examined how cultural factors are associated with breast cancer screening utilization, specifically clinical breast exam (CBE), in this population. One hundred and sixty-four Hmong American women between ages 18 and 67 were recruited from a large Midwestern metropolitan area with a median age of 28.0 years. Logistic regression was used to assess the association of cultural variables with receipt of CBE. Roughly 73% of Hmong American women reported ever having had a CBE. Logistic regression revealed that endorsing more modest views was the greatest barrier to ever having had a CBE. Age and language preference were also found to be significant predictors of past CBE use. Cultural factors should be considered in developing interventions aimed at promoting breast cancer screening in this population. In particular, Hmong American women who have less English proficiency and are relatively younger should be targeted in breast cancer screening efforts.

A modified approach to estimating sample size for simple logistic regression with one continuous covariate.

PubMed

Novikov, I; Fund, N; Freedman, L S

2010-01-15

Different methods for the calculation of sample size for simple logistic regression (LR) with one normally distributed continuous covariate give different results. Sometimes the difference can be large. Furthermore, some methods require the user to specify the prevalence of cases when the covariate equals its population mean, rather than the more natural population prevalence. We focus on two commonly used methods and show through simulations that the power for a given sample size may differ substantially from the nominal value for one method, especially when the covariate effect is large, while the other method performs poorly if the user provides the population prevalence instead of the required parameter. We propose a modification of the method of Hsieh et al. that requires specification of the population prevalence and that employs Schouten's sample size formula for a t-test with unequal variances and group sizes. This approach appears to increase the accuracy of the sample size estimates for LR with one continuous covariate.

Predictors of adherence with self-care guidelines among persons with type 2 diabetes: results from a logistic regression tree analysis.

PubMed

Yamashita, Takashi; Kart, Cary S; Noe, Douglas A

2012-12-01

Type 2 diabetes is known to contribute to health disparities in the U.S. and failure to adhere to recommended self-care behaviors is a contributing factor. Intervention programs face difficulties as a result of patient diversity and limited resources. With data from the 2005 Behavioral Risk Factor Surveillance System, this study employs a logistic regression tree algorithm to identify characteristics of sub-populations with type 2 diabetes according to their reported frequency of adherence to four recommended diabetes self-care behaviors including blood glucose monitoring, foot examination, eye examination and HbA1c testing. Using Andersen's health behavior model, need factors appear to dominate the definition of which sub-groups were at greatest risk for low as well as high adherence. Findings demonstrate the utility of easily interpreted tree diagrams to design specific culturally appropriate intervention programs targeting sub-populations of diabetes patients who need to improve their self-care behaviors. Limitations and contributions of the study are discussed.

The effects of sleep quality, physical activity, and environmental quality on the risk of falls in dementia.

PubMed

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

2013-06-01

This study aimed to identify the effects of sleep quality, physical activity, environmental quality, age, ethnicity, sex differences, marital status, and educational level on the risk of falls in the elderly individuals with dementia. Data were derived from a group of 1210 Malaysian elderly individuals who were noninstitutionalized and demented. The multiple logistic regression model was applied to estimate the risk of falls in respondents. Approximately the prevalence of falls was 17% among the individuals. The results of multiple logistic regression analysis revealed that age (odds ratio [OR] = 1.03), ethnicity (OR = 1.76), sleep quality (OR = 1.46), and environmental quality (OR = 0.62) significantly affected the risk of falls in individuals (P < .05). Furthermore, sex differences, marital status, educational level, and physical activity were not significant predictors of falls in samples (P > .05). It was found that age, ethnic non-Malay, and sleep disruption increased the risk of falls in respondents, but high environmental quality reduced the risk of falls.

Gene-environment interaction between adiponectin gene polymorphisms and environmental factors on the risk of diabetic retinopathy.

PubMed

Li, Yuan; Wu, Qun Hong; Jiao, Ming Li; Fan, Xiao Hong; Hu, Quan; Hao, Yan Hua; Liu, Ruo Hong; Zhang, Wei; Cui, Yu; Han, Li Yuan

2015-01-01

To evaluate whether the adiponectin gene is associated with diabetic retinopathy (DR) risk and interaction with environmental factors modifies the DR risk, and to investigate the relationship between serum adiponectin levels and DR. Four adiponectin polymorphisms were evaluated in 372 DR cases and 145 controls. Differences in environmental factors between cases and controls were evaluated by unconditional logistic regression analysis. The model-free multifactor dimensionality reduction method and traditional multiple regression models were applied to explore interactions between the polymorphisms and environmental factors. Using the Bonferroni method, we found no significant associations between four adiponectin polymorphisms and DR susceptibility. Multivariate logistic regression found that physical activity played a protective role in the progress of DR, whereas family history of diabetes (odds ratio 1.75) and insulin therapy (odds ratio 1.78) were associated with an increased risk for DR. The interaction between the C-11377 G (rs266729) polymorphism and insulin therapy might be associated with DR risk. Family history of diabetes combined with insulin therapy also increased the risk of DR. No adiponectin gene polymorphisms influenced the serum adiponectin levels. Serum adiponectin levels did not differ between the DR group and non-DR group. No significant association was identified between four adiponectin polymorphisms and DR susceptibility after stringent Bonferroni correction. The interaction between C-11377G (rs266729) polymorphism and insulin therapy, as well as the interaction between family history of diabetes and insulin therapy, might be associated with DR susceptibility.

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

PubMed

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

2017-05-01

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

[The role of uric acid in the insulin resistance in children and adolescents with obesity].

PubMed

de Miranda, Josiane Aparecida; Almeida, Guilherme Gomide; Martins, Raissa Isabelle Leão; Cunha, Mariana Botrel; Belo, Vanessa Almeida; dos Santos, José Eduardo Tanus; Mourão-Júnior, Carlos Alberto; Lanna, Carla Márcia Moreira

2015-12-01

To investigate the association between serum uric acid levels and insulin resistance in children and adolescents with obesity. Cross-sectional study with 245 children and adolescents (134 obese and 111 controls), aged 8 to 18 years. The anthropometric variables (weight, height and waist circumference), blood pressure and biochemical parameters were collected. The clinical characteristics of the groups were analyzed by t-test or chi-square test. To evaluate the association between uric acid levels and insulin resistance the Pearson's test and logistic regression were applied. The prevalence of insulin resistance was 26.9%. The anthropometric variables, systolic and diastolic blood pressure and biochemical variables were significantly higher in the obese group (p<0.001), except for the high-density-lipoprotein cholesterol. There was a positive and significant correlation between anthropometric variables and uric acid with HOMA-IR in the obese and in the control groups, which was higher in the obese group and in the total sample. The logistic regression model that included age, gender and obesity, showed an odds ratio of uric acid as a variable associated with insulin resistance of 1.91 (95%CI 1.40 to 2.62; p<-0.001). The increase in serum uric acid showed a positive statistical correlation with insulin resistance and it is associated with and increased risk of insulin resistance in obese children and adolescents. Copyright © 2015 Sociedade de Pediatria de São Paulo. Publicado por Elsevier Editora Ltda. All rights reserved.

Sarcopenia is associated with an increased risk of advanced colorectal neoplasia.

PubMed

Park, Youn Su; Kim, Ji Won; Kim, Byeong Gwan; Lee, Kook Lae; Lee, Jae Kyung; Kim, Joo Sung; Koh, Seong-Joon

2017-04-01

Although sarcopenia is associated with an increased risk for mortality after the curative resection of colorectal cancer, its influence on the development of advanced colonic neoplasia remains unclear. This study included 1270 subjects aged 40 years or older evaluated with first-time screening colonoscopy at Seoul National University Boramae Health Care Center from January 2010 to February 2015. Skeletal muscle mass was measured with a body composition analyzer (direct segmental multifrequency bioelectrical impedance analysis method). Multiple logistic regression analysis was performed to determine whether sarcopenia is associated with advanced colorectal neoplasia. Of 1270 subjects, 139 (10.9%) were categorized into the sarcopenia group and 1131 (89.1%) into the non-sarcopenia group. In the non-sarcopenia group, 55 subjects (4.9%) had advanced colorectal neoplasia. However, in the sarcopenia group, 19 subjects (13.7%) had advanced colorectal neoplasia, including 1 subject with invasive colorectal cancer (0.7%). In addition, subjects with sarcopenia had a higher prevalence of advanced adenoma (P < 0.001) than those without sarcopenia. According to the multiple logistic regression analysis adjusted for variable confounders, age (odds ratio 1.062, 95% confidence interval 1.032-1.093; P < 0.001), male sex (odds ratio 1.749, 95% confidence interval 1.008-3.036; P = 0.047), and sarcopenia (odds ratio 2.347, 95% confidence interval 1.311-4.202; P = 0.004) were associated with an advanced colorectal neoplasia. Sarcopenia is associated with an increased risk of advanced colorectal neoplasia.

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

USGS Publications Warehouse

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

2006-01-01

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

Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression.

PubMed

Ali, Faraz Mahmood; Kay, Richard; Finlay, Andrew Y; Piguet, Vincent; Kupfer, Joerg; Dalgard, Florence; Salek, M Sam

2017-11-01

The Dermatology Life Quality Index (DLQI) and the European Quality of Life-5 Dimension (EQ-5D) are separate measures that may be used to gather health-related quality of life (HRQoL) information from patients. The EQ-5D is a generic measure from which health utility estimates can be derived, whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates, we explored an established mathematical technique known as ordinal logistic regression (OLR) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates. Retrospective data from 4010 patients were randomly divided five times into two groups for the derivation and testing of the mapping model. Split-half cross-validation was utilized resulting in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age, sex, and all ten items of the DLQI. Using Monte Carlo simulation, predicted health utility estimates were derived and compared against those observed. This method was repeated for both OLR and a previously tested mapping methodology based on linear regression. The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression. The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises, with an average overall difference of 0.0120 (a 1.6% underestimate, not of clinical importance). This modeling framework developed in this study will enable researchers to calculate EQ-5D health utility estimates from a specialty-specific study population, reducing patient and economic burden.

Gender Differential Item Functioning on a National Field-Specific Test: The Case of PhD Entrance Exam of TEFL in Iran

ERIC Educational Resources Information Center

Ahmadi, Alireza; Bazvand, Ali Darabi

2016-01-01

Differential Item Functioning (DIF) exists when examinees of equal ability from different groups have different probabilities of successful performance in a certain item. This study examined gender differential item functioning across the PhD Entrance Exam of TEFL (PEET) in Iran, using both logistic regression (LR) and one-parameter item response…

Factors Contributing to Successful Employment Outcomes for Hispanic Women Who Are Deaf: Utilization of Chi-Squared Automatic Interaction Detector and Logistic Regression Analysis

ERIC Educational Resources Information Center

Feist, Amber M.

2013-01-01

Hispanic women who are deaf constitute a heterogeneous group of individuals with varying vocational needs. To understand the unique needs of this population, it is important to analyze how consumer characteristics, presence of public supports, and type of services provided influence employment outcomes for Hispanic women who are deaf. The purpose…

Adherence to oral anticoagulants in patients with atrial fibrillation-a population-based retrospective cohort study linking health information systems in the Valencia region, Spain: a study protocol.

PubMed

Sanfélix-Gimeno, G; Rodríguez-Bernal, C L; Hurtado, I; Baixáuli-Pérez, C; Librero, J; Peiró, S

2015-10-19

Adherence to oral anticoagulation (OAC) treatment, vitamin K antagonists or new oral anticoagulants, is an essential element for effectiveness. Information on adherence to OAC in atrial fibrillation (AF) and the impact of adherence on clinical outcomes using real-world data barely exists. We aim to describe the patterns of adherence to OAC over time in patients with AF, estimate the associated factors and their impact on clinical events, and assess the same issues with conventional measures of primary and secondary adherence-proportion of days covered (PDC) and persistence-in routine clinical practice. This is a population-based retrospective cohort study including all patients with AF treated with OAC from 2010 to date in Valencia, Spain; data will be obtained from diverse electronic records of the Valencia Health Agency. adherence trajectories. (1) primary non-adherence; (2) secondary adherence: (a) PDC, (b) persistence. Clinical outcomes: hospitalisation for haemorrhagic or thromboembolic events and death during follow-up. (1) description of baseline characteristics, adherence patterns (trajectory models or latent class growth analysis models) and conventional adherence measures; (2) logistic or Cox multivariate regression models, to assess the associations between adherence measures and the covariates, and logistic multinomial regression models, to identify characteristics associated with each trajectory; (3) Cox proportional hazard models, to assess the relationship between adherence and clinical outcomes, with propensity score adjustment applied to further control for potential confounders; (4) to estimate the importance of different healthcare levels in the variations of adherence, logistic or Cox multilevel regression models. This study has been approved by the corresponding Clinical Research Ethics Committee. We plan to disseminate the project's findings through peer-reviewed publications and presentations at relevant health conferences. Policy reports will also be prepared in order to promote the translation of our findings into policy and clinical practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Identifying patterns of item missing survey data using latent groups: an observational study

PubMed Central

McElwee, Paul; Nathan, Andrea; Burton, Nicola W; Turrell, Gavin

2017-01-01

Objectives To examine whether respondents to a survey of health and physical activity and potential determinants could be grouped according to the questions they missed, known as ‘item missing’. Design Observational study of longitudinal data. Setting Residents of Brisbane, Australia. Participants 6901 people aged 40–65 years in 2007. Materials and methods We used a latent class model with a mixture of multinomial distributions and chose the number of classes using the Bayesian information criterion. We used logistic regression to examine if participants’ characteristics were associated with their modal latent class. We used logistic regression to examine whether the amount of item missing in a survey predicted wave missing in the following survey. Results Four per cent of participants missed almost one-fifth of the questions, and this group missed more questions in the middle of the survey. Eighty-three per cent of participants completed almost every question, but had a relatively high missing probability for a question on sleep time, a question which had an inconsistent presentation compared with the rest of the survey. Participants who completed almost every question were generally younger and more educated. Participants who completed more questions were less likely to miss the next longitudinal wave. Conclusions Examining patterns in item missing data has improved our understanding of how missing data were generated and has informed future survey design to help reduce missing data. PMID:29084795

Multinomial logistic regression analysis for differentiating 3 treatment outcome trajectory groups for headache-associated disability.

PubMed

Lewis, Kristin Nicole; Heckman, Bernadette Davantes; Himawan, Lina

2011-08-01

Growth mixture modeling (GMM) identified latent groups based on treatment outcome trajectories of headache disability measures in patients in headache subspecialty treatment clinics. Using a longitudinal design, 219 patients in headache subspecialty clinics in 4 large cities throughout Ohio provided data on their headache disability at pretreatment and 3 follow-up assessments. GMM identified 3 treatment outcome trajectory groups: (1) patients who initiated treatment with elevated disability levels and who reported statistically significant reductions in headache disability (high-disability improvers; 11%); (2) patients who initiated treatment with elevated disability but who reported no reductions in disability (high-disability nonimprovers; 34%); and (3) patients who initiated treatment with moderate disability and who reported statistically significant reductions in headache disability (moderate-disability improvers; 55%). Based on the final multinomial logistic regression model, a dichotomized treatment appointment attendance variable was a statistically significant predictor for differentiating high-disability improvers from high-disability nonimprovers. Three-fourths of patients who initiated treatment with elevated disability levels did not report reductions in disability after 5 months of treatment with new preventive pharmacotherapies. Preventive headache agents may be most efficacious for patients with moderate levels of disability and for patients with high disability levels who attend all treatment appointments. Copyright © 2011 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.

[Risk factors for patent ductus arteriosus in early preterm infants: a case-control study].

PubMed

Du, Jin-Feng; Liu, Tian-Tian; Wu, Hui

2016-01-01

To investigate the risk factors for the occurrence of patent ductus arteriosus (PDA) and to provide a clinical basis for reducing the occurrence of PDA in early preterm infants. A total of 136 early preterm infants (gestational age≤32 weeks) who were hospitalized between January 2013 and December 2014 and diagnosed with hemodynamicalhy significant PDA (hs-PDA) were enrolled as the case group. Based on the matched case-control principle, 136 early preterm infants without hs-PDA were selected among those who were hospitalized within the same period at a ratio of 1:1 and enrolled as the control group. The two groups were matched for sex and gestational age. The basic information of neonates and maternal conditions during the pregnancy and perinatal periods were collected. Logistic regression analysis was performed to identify the risk factors for the development of PDA. Univariate analysis showed that neonatal infectious diseases, neonatal respiratory distress syndrome, decreased platelet count within 24 hours after birth, and low birth weight were associated with the development of hs-PDA (P<0.05). Multivariate conditional logistic regression analysis revealed that neonatal infectious diseases (OR=2.368) and decreased platelet count within 24 hours after birth (OR=0.996) were independent risk factors for hs-PDA. Neonatal infectious diseases and decreased platelet count within 24 hours after birth increase the risk of hs-PDA in early preterm infants.

Prevalence of Neuropsychiatric Symptoms in CIND and Its Subtypes: The Cache County Study

PubMed Central

Peters, ME; Rosenberg, P; Steinberg, M; Tschanz, J; Norton, MC; Welsh-Bohmer, KA; Hayden, KM; Breitner, JCS; Lyketsos, CG

2011-01-01

Objectives 1) To report rates of neuropsychiatric symptoms (NPS) in cognitive impairment, no dementia (CIND). 2) To compare the 30-day prevalence of NPS in CIND with that in dementia and cognitively normal individuals. 3) To compare the prevalence of NPS in amnestic MCI (aMCI) with other predementia syndromes. Design Comparison of prevalence proportions among several defined groups. Setting Population-based study. Participants A subsample of the permanent residents of Cache County, Utah, aged 65 years or older in January 1995 (N = 5092) and who had completed clinical assessments and had an informant-completed Neuropsychiatric Inventory. Measurements Chi-square statistics, tests for trend, and logistic regression models were used to analyze the three objectives listed earlier. Results The most prevalent NPS in those with CIND were depression (16.9%), irritability (9.8%), nighttime behaviors (7.6%), apathy (6.9%), and anxiety (5.4%). Trend analyses confirmed that the CIND group had NPS prevalence rates that fell between the normal and dementia groups for most NPS. Logistic regression models showed no significant difference between aMCI and other CIND participants in the prevalence of any NPS (lowest p: 0.316). Conclusions These data confirm the relatively high prevalence of NPS in CIND reported by other studies, especially for affective symptoms. No differences in NPS prevalence were found between aMCI and other types of CIND. PMID:22522960

Locomotive syndrome is associated not only with physical capacity but also degree of depression.

PubMed

Ikemoto, Tatsunori; Inoue, Masayuki; Nakata, Masatoshi; Miyagawa, Hirofumi; Shimo, Kazuhiro; Wakabayashi, Toshiko; Arai, Young-Chang P; Ushida, Takahiro

2016-05-01

Reports of locomotive syndrome (LS) have recently been increasing. Although physical performance measures for LS have been well investigated to date, studies including psychiatric assessment are still scarce. Hence, the aim of this study was to investigate both physical and mental parameters in relation to presence and severity of LS using a 25-question geriatric locomotive function scale (GLFS-25) questionnaire. 150 elderly people aged over 60 years who were members of our physical-fitness center and displayed well-being were enrolled in this study. Firstly, using the previously determined GLFS-25 cutoff value (=16 points), subjects were divided into two groups accordingly: an LS and non-LS group in order to compare each parameter (age, grip strength, timed-up-and-go test (TUG), one-leg standing with eye open, back muscle and leg muscle strength, degree of depression and cognitive impairment) between the groups using the Mann-Whitney U-test followed by multiple logistic regression analysis. Secondly, a multiple linear regression was conducted to determine which variables showed the strongest correlation with severity of LS. We confirmed 110 people for non-LS (73%) and 40 people for LS using the GLFS-25 cutoff value. Comparative analysis between LS and non-LS revealed significant differences in parameters in age, grip strength, TUG, one-leg standing, back muscle strength and degree of depression (p < 0.006, after Bonferroni correction). Multiple logistic regression revealed that functional decline in grip strength, TUG and one-leg standing and degree of depression were significantly associated with LS. On the other hand, we observed that the significant contributors towards the GLFS-25 score were TUG and degree of depression in multiple linear regression analysis. The results indicate that LS is associated with not only the capacity of physical performance but also the degree of depression although most participants fell under the criteria of LS. Copyright © 2016 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Tobacco Smoking and Its Association with Illicit Drug Use among Young Men Aged 15-24 Years Living in Urban Slums of Bangladesh

PubMed Central

Kabir, Mohammad Alamgir; Goh, Kim-Leng; Kamal, Sunny Mohammad Mostafa; Khan, Md. Mobarak Hossain

2013-01-01

Background Tobacco smoking (TS) and illicit drug use (IDU) are of public health concerns especially in developing countries, including Bangladesh. This paper aims to (i) identify the determinants of TS and IDU, and (ii) examine the association of TS with IDU among young slum dwellers in Bangladesh. Methodology/Principal Findings Data on a total of 1,576 young slum dwellers aged 15–24 years were extracted for analysis from the 2006 Urban Health Survey (UHS), which covered a nationally representative sample of 13,819 adult men aged 15–59 years from slums, non-slums and district municipalities of six administrative regions in Bangladesh. Methods used include frequency run, Chi-square test of association and multivariable logistic regression. The overall prevalence of TS in the target group was 42.3%, of which 41.4% smoked cigarettes and 3.1% smoked bidis. The regression model for TS showed that age, marital status, education, duration of living in slums, and those with sexually transmitted infections were significantly (p<0.001 to p<0.05) associated with TS. The overall prevalence of IDU was 9.1%, dominated by those who had drug injections (3.2%), and smoked ganja (2.8%) and tari (1.6%). In the regression model for IDU, the significant (p<0.01 to p<0.10) predictors were education, duration of living in slums, and whether infected by sexually transmitted diseases. The multivariable logistic regression (controlling for other variables) revealed significantly (p<0.001) higher likelihood of IDU (OR = 9.59, 95% CI = 5.81–15.82) among users of any form of TS. The likelihood of IDU increased significantly (p<0.001) with increased use of cigarettes. Conclusions/Significance Certain groups of youth are more vulnerable to TS and IDU. Therefore, tobacco and drug control efforts should target these groups to reduce the consequences of risky lifestyles through information, education and communication (IEC) programs. PMID:23935885

The effects of cognitive – linguistic variables and language experience on behavioural and kinematic performances in nonword learning

PubMed Central

Sasisekaran, Jayanthi; Weisberg, Sanford

2013-01-01

The aim of the present study was to investigate the effect of cognitive – linguistic variables and language experience on behavioral and kinematic measures of nonword learning in young adults. Group 1 consisted of thirteen participants who spoke American English as the first and only language. Group 2 consisted of seven participants with varying levels of proficiency in a second language. Logistic regression of the percent of correct productions revealed short-term memory to be a significant contributor. The bilingual group showed better performance compared to the monolinguals. Linear regression of the kinematic data revealed that the short – term memory variable contributed significantly to movement coordination. Differences were not observed between the bilingual and the monolingual speakers in kinematic performance. Nonword properties including syllable length and complexity influenced both behavioral and kinematic performance. The findings supported the observation that nonword repetition is multiply determined in adults. PMID:22476630

Red-cockaded Woodpecker Picoides borealis Microhabitat Characteristics and Reproductive Success in a Loblolly-Shortleaf Pine Forest

USGS Publications Warehouse

Wood, Douglas R.; Burger, L. Wesley; Vilella, Francisco

2014-01-01

We investigated the relationship between red-cockaded woodpecker (Picoides borealis) reproductive success and microhabitat characteristics in a southeastern loblolly (Pinus taeda) and shortleaf (P. echinata) pine forest. From 1997 to 1999, we recorded reproductive success parameters of 41 red-cockaded woodpecker groups at the Bienville National Forest, Mississippi. Microhabitat characteristics were measured for each group during the nesting season. Logistic regression identified understory vegetation height and small nesting season home range size as predictors of red-cockaded woodpecker nest attempts. Linear regression models identified several variables as predictors of red-cockaded woodpecker reproductive success including group density, reduced hardwood component, small nesting season home range size, and shorter foraging distances. Red-cockaded woodpecker reproductive success was correlated with habitat and behavioral characteristics that emphasize high quality habitat. By providing high quality foraging habitat during the nesting season, red-cockaded woodpeckers can successfully reproduce within small home ranges.

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study.

PubMed

Shi, Xiao; Zhang, Ting-Ting; Hu, Wei-Ping; Ji, Qing-Hai

2017-04-25

The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187-1.339, P < 0.001) and OS (HR: 1.328, 95% CI: 1.266-1.392, P < 0.001). Multivariate logistic regression showed married patients were more likely to be diagnosed at earlier stage (P < 0.001) and receive surgery (P < 0.001). Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role.

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study

PubMed Central

Shi, Xiao; Zhang, Ting-ting; Hu, Wei-ping; Ji, Qing-hai

2017-01-01

Background The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Results Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187–1.339, P < 0.001) and OS (HR: 1.328, 95% CI: 1.266–1.392, P < 0.001). Multivariate logistic regression showed married patients were more likely to be diagnosed at earlier stage (P < 0.001) and receive surgery (P < 0.001). Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). Materials and Methods 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Conclusions Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role. PMID:28415710

Factors Associated with Dental Caries in a Group of American Indian Children at age 36 Months

PubMed Central

Warren, John J.; Blanchette, Derek; Dawson, Deborah V.; Marshall, Teresa A.; Phipps, Kathy R.; Starr, Delores; Drake, David R.

2015-01-01

Objectives Early childhood caries (ECC) is rampant among American Indian children, but there has been relatively little study of this problem. This paper reports on risk factors for caries for a group of American Indian children at age 36 months as part of a longitudinal study. Methods Pregnant women from a Northern Plains Tribal community were recruited to participate in a longitudinal study of caries and caries risk factors. Standardized dental examinations were completed on children and questionnaires were completed by mothers at baseline and when children were 4, 8, 12, 16, 22, 28 and 36 months of age. Examinations were surface-specific for dental caries, and the questionnaires collected data on demographic, dietary and behavioral factors. Non-parametric bivariate tests and logistic regression models were used to identify risk factors for caries at 36 months, and negative binomial regression was used to identify factors related to caries severity (dmf counts). Results Among the 232 children, and caries prevalence for cavitated lesions was 80%, with an additional 15% having only non-cavitated lesions. The mean dmfs was 9.6, and of the total dmfs, nearly 62% of affected surfaces were decayed, 31% were missing, and 7% were filled. Logistic regression identified higher added sugar beverage consumption, younger maternal age at baseline, higher maternal DMFS at baseline, and greater number of people in the household as significant (p<0.05) risk factors. Negative binomial regression found that only maternal DMFS was associated with child dmf counts. Conclusions By the age of 36 months, dental caries is nearly universal in this population of American Indian children. Caries risk factors included sugared beverage consumption, greater household size and maternal factors, but further analyses are needed to better understand caries in this population. PMID:26544674

Influence of warfarin and low-dose aspirin on the outcomes of geriatric patients with traumatic intracranial hemorrhage resulting from ground-level fall.

PubMed

Inamasu, Joji; Nakatsukasa, Masashi; Miyatake, Satoru; Hirose, Yuichi

2012-10-01

Ground-level fall is the most common cause of traumatic intracranial hemorrhage (TICH) in the elderly, and is a major cause of morbidity and mortality in that population. A retrospective study was carried out to evaluate whether the use of warfarin/low-dose aspirin (LDA) is predictive of unfavorable outcomes in geriatric patients who sustain a fall-induced TICH. Charts of 76 geriatric patients (≥ 65 years-of-age) with fall-induced TICH were reviewed. The number of patients taking warfarin and LDA was 12 and 21, respectively, whereas the other 43 took neither medication (non-user group). The frequency of patients with unfavorable outcomes (Glasgow Outcome Scale score of 1-3) at discharge was calculated. Furthermore, variables predictive of unfavorable outcomes were identified by logistic regression analysis. The frequency of patients with unfavorable outcomes was 75% in the warfarin group, 33% in the LDA group and 27% in the non-user group, respectively. The risk of having unfavorable outcomes was significantly higher in the warfarin group compared with the LDA group (P = 0.03) and non-user group (P < 0.01). Logistic regression analysis showed that variables predictive of unfavorable outcomes were: age, initial Glasgow Coma Scale score ≤ 13 and presence of midline shift ≥ 5 mm. The use of warfarin, but not of LDA, might be associated with unfavorable outcomes in elderly with fall-induced TICH. The risk of TICH should be communicated properly to elderly taking warfarin. The information might be important not only to trauma surgeons who take care of injured elderly, but also to geriatric physicians who prescribe warfarin/LDA to them. © 2012 Japan Geriatrics Society.

Associations between peer victimization, self-reported depression and social phobia among adolescents: the role of comorbidity.

PubMed

Ranta, Klaus; Kaltiala-Heino, Riittakerttu; Pelkonen, Mirjami; Marttunen, Mauri

2009-02-01

Associations of peer victimization with adolescent depression and social phobia (SP), while controlling for comorbidity between them, have not been sufficiently explored in earlier research. A total of 3156 Finnish adolescents aged 15-16 years participated in a survey study. Self-reported peer victimization, as well as self-reported depression (Beck Depression Inventory), SP (Social Phobia Inventory), and selected background variables were assessed. Frequency of overt and covert peer victimization was examined among four groups: (1) adolescents with depression non-comorbid with SP (DEP), (2) those with SP non-comorbid with depression (SP), (3) those with both SP and depression (SP+DEP), and (4) controls, with neither. A logistic regression analysis controlling for confounding familial (family moving, parental unemployment), and psychopathology (delinquency, aggressiveness, general anxiety) covariates was conducted to confirm the associations between peer victimization and the four groups. Among boys the comorbid SP+DEP group reported the highest rates of both overt and covert victimization, these being significantly higher than among both DEP and SP groups. Among girls covert victimization was again most frequent in the SP+DEP group, but overt victimization was not more frequent in the comorbid group than it was in the DEP and SP groups. In the logistic regression analysis depression without SP did not maintain an independent association with either type of victimization. Instead, SP without depression with ORs from 2.8 to 4.3, and SP comorbid with depression, with ORs between 3.2 and 11.4 had independent associations with peer victimization. In conclusion, overt and covert peer victimization seem to be associated with SP, rather than depression, among adolescents.

One size does not fit all: an examination of low birthweight disparities among a diverse set of racial/ethnic groups.

PubMed

Johnelle Sparks, P

2009-11-01

To examine disparities in low birthweight using a diverse set of racial/ethnic categories and a nationally representative sample. This research explored the degree to which sociodemographic characteristics, health care access, maternal health status, and health behaviors influence birthweight disparities among seven racial/ethnic groups. Binary logistic regression models were estimated using a nationally representative sample of singleton, normal for gestational age births from 2001 using the ECLS-B, which has an approximate sample size of 7,800 infants. The multiple variable models examine disparities in low birthweight (LBW) for seven racial/ethnic groups, including non-Hispanic white, non-Hispanic black, U.S.-born Mexican-origin Hispanic, foreign-born Mexican-origin Hispanic, other Hispanic, Native American, and Asian mothers. Race-stratified logistic regression models were also examined. In the full sample models, only non-Hispanic black mothers have a LBW disadvantage compared to non-Hispanic white mothers. Maternal WIC usage was protective against LBW in the full models. No prenatal care and adequate plus prenatal care increase the odds of LBW. In the race-stratified models, prenatal care adequacy and high maternal health risks are the only variables that influence LBW for all racial/ethnic groups. The race-stratified models highlight the different mechanism important across the racial/ethnic groups in determining LBW. Differences in the distribution of maternal sociodemographic, health care access, health status, and behavior characteristics by race/ethnicity demonstrate that a single empirical framework may distort associations with LBW for certain racial and ethnic groups. More attention must be given to the specific mechanisms linking maternal risk factors to poor birth outcomes for specific racial/ethnic groups.

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.

Urinary Incontinence of Women in a Nationwide Study in Sri Lanka: Prevalence and Risk Factors.

PubMed

Pathiraja, Ramya; Prathapan, Shamini; Goonawardena, Sampatha

2017-05-23

Urinary incontinence, be stress incontinence or urge incontinence or a mixed type incontinence affects women of all ages. The aim of this study was to describe the prevalence and risk factors of urinary incontinence in Sri Lanka. A community based cross-sectional study was performed in Sri Lanka. The age group of the women in Sri Lanka was categorized into 3 age groups: Less than or equal to 35 years, 36 to 50 years of age and more than or equal to 51 years of age. A sample size of 675 women was obtained from each age category obtaining a total sample of 2025 from Sri Lanka. An interviewer-administered questionnaire consisting of two parts; Socio demographic factors, Medical and Obstetric History, and the King's Health Questionnaire (KHQ), was used for data collection. Stepwise logistic regression analysis was performed. The Prevalence of women with only stress incontinence was 10%, with urge incontinence was 15.6% and with stress and urge incontinence was 29.9%. Stepwise logistic regression analysis showed that the age groups of 36 - 50 years (OR = 2.03; 95% CI = 1.56 - 2.63) and 51 years and above (OR = 2.61; 95% CI= 1.95 - 3.48), Living in one of the districts in Sri Lanka (OR = 4.58; 95% CI = 3.35 - 6.27) and having given birth to multiple children (OR = 1.1; 95% CI = 1.02 - 1.21), diabetes mellitus (OR = 1.97; 95% CI = 1.19 - 3.23), and respiratory diseases (OR = 2.17; 95% CI = 1.48 - 3.19 ) showed a significant risk in the regression analysis. The risk factor, mostly modifiable, if prevented early, could help to reduce the symptoms of urinary incontinence.

A retrospective study: Multivariate logistic regression analysis of the outcomes after pressure sores reconstruction with fasciocutaneous, myocutaneous, and perforator flaps.

PubMed

Chiu, Yu-Jen; Liao, Wen-Chieh; Wang, Tien-Hsiang; Shih, Yu-Chung; Ma, Hsu; Lin, Chih-Hsun; Wu, Szu-Hsien; Perng, Cherng-Kang

2017-08-01

Despite significant advances in medical care and surgical techniques, pressure sore reconstruction is still prone to elevated rates of complication and recurrence. We conducted a retrospective study to investigate not only complication and recurrence rates following pressure sore reconstruction but also preoperative risk stratification. This study included 181 ulcers underwent flap operations between January 2002 and December 2013 were included in the study. We performed a multivariable logistic regression model, which offers a regression-based method accounting for the within-patient correlation of the success or failure of each flap. The overall complication and recurrence rates for all flaps were 46.4% and 16.0%, respectively, with a mean follow-up period of 55.4 ± 38.0 months. No statistically significant differences of complication and recurrence rates were observed among three different reconstruction methods. In subsequent analysis, albumin ≤3.0 g/dl and paraplegia were significantly associated with higher postoperative complication. The anatomic factor, ischial wound location, significantly trended toward the development of ulcer recurrence. In the fasciocutaneous group, paraplegia had significant correlation to higher complication and recurrence rates. In the musculocutaneous flap group, variables had no significant correlation to complication and recurrence rates. In the free-style perforator group, ischial wound location and malnourished status correlated with significantly higher complication rates; ischial wound location also correlated with significantly higher recurrence rate. Ultimately, our review of a noteworthy cohort with lengthy follow-up helped identify and confirm certain risk factors that can facilitate a more informed and thoughtful pre- and postoperative decision-making process for patients with pressure ulcers. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program.

PubMed

Chen, Robert; Sun, Jimeng; Dittus, Robert S; Fabbri, Daniel; Kirby, Jacqueline; Laffer, Cheryl L; McNaughton, Candace D; Malin, Bradley

2016-01-04

The goal of this study is to devise a machine learning framework to assist care coordination programs in prognostic stratification to design and deliver personalized care plans and to allocate financial and medical resources effectively. This study is based on a de-identified cohort of 2,521 hypertension patients from a chronic care coordination program at the Vanderbilt University Medical Center. Patients were modeled as vectors of features derived from electronic health records (EHRs) over a six-year period. We applied a stepwise regression to identify risk factors associated with a decrease in mean arterial pressure of at least 2 mmHg after program enrollment. The resulting features were subsequently validated via a logistic regression classifier. Finally, risk factors were applied to group the patients through model-based clustering. We identified a set of predictive features that consisted of a mix of demographic, medication, and diagnostic concepts. Logistic regression over these features yielded an area under the ROC curve (AUC) of 0.71 (95% CI: [0.67, 0.76]). Based on these features, four clinically meaningful groups are identified through clustering - two of which represented patients with more severe disease profiles, while the remaining represented patients with mild disease profiles. Patients with hypertension can exhibit significant variation in their blood pressure control status and responsiveness to therapy. Yet this work shows that a clustering analysis can generate more homogeneous patient groups, which may aid clinicians in designing and implementing customized care programs. The study shows that predictive modeling and clustering using EHR data can be beneficial for providing a systematic, generalized approach for care providers to tailor their management approach based upon patient-level factors.

Bioelectrical impedance analysis-derived phase angle at admission as a predictor of 90-day mortality in intensive care patients.

PubMed

Stapel, Sandra N; Looijaard, Wilhelmus G P M; Dekker, Ingeborg M; Girbes, Armand R J; Weijs, Peter J M; Oudemans-van Straaten, Heleen M

2018-05-11

A low bioelectrical impedance analysis (BIA)-derived phase angle (PA) predicts morbidity and mortality in different patient groups. An association between PA and long-term mortality in ICU patients has not been demonstrated before. The purpose of the present study was to determine whether PA on ICU admission independently predicts 90-day mortality. This prospective observational study was performed in a mixed university ICU. BIA was performed in 196 patients within 24 h of ICU admission. To test the independent association between PA and 90-day mortality, logistic regression analysis was performed using the APACHE IV predicted mortality as confounder. The optimal cutoff value of PA for mortality prediction was determined by ROC curve analysis. Using this cutoff value, patients were categorized into low or normal PA group and the association with 90-day mortality was tested again. The PA of survivors was higher than of the non-survivors (5.0° ± 1.3° vs. 4.1° ± 1.2°, p < 0.001). The area under the ROC curve of PA for 90-day mortality was 0.70 (CI 0.59-0.80). PA was associated with 90-day mortality (OR = 0.56, CI: 0.38-0.77, p = 0.001) on univariate logistic regression analysis and also after adjusting for BMI, gender, age, and APACHE IV on multivariable logistic regression (OR = 0.65, CI: 0.44-0.96, p = 0.031). A PA < 4.8° was an independent predictor of 90-day mortality (adjusted OR = 3.65, CI: 1.34-9.93, p = 0.011). Phase angle at ICU admission is an independent predictor of 90-day mortality. This biological marker can aid in long-term mortality risk assessment of critically ill patients.

SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression

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

Huang, W; Tu, S

Purpose: We conducted a retrospective study of Radiomics research for classifying malignancy of small pulmonary nodules. A machine learning algorithm of logistic regression and open research platform of Radiomics, IBEX (Imaging Biomarker Explorer), were used to evaluate the classification accuracy. Methods: The training set included 100 CT image series from cancer patients with small pulmonary nodules where the average diameter is 1.10 cm. These patients registered at Chang Gung Memorial Hospital and received a CT-guided operation of lung cancer lobectomy. The specimens were classified by experienced pathologists with a B (benign) or M (malignant). CT images with slice thickness ofmore » 0.625 mm were acquired from a GE BrightSpeed 16 scanner. The study was formally approved by our institutional internal review board. Nodules were delineated and 374 feature parameters were extracted from IBEX. We first used the t-test and p-value criteria to study which feature can differentiate between group B and M. Then we implemented a logistic regression algorithm to perform nodule malignancy classification. 10-fold cross-validation and the receiver operating characteristic curve (ROC) were used to evaluate the classification accuracy. Finally hierarchical clustering analysis, Spearman rank correlation coefficient, and clustering heat map were used to further study correlation characteristics among different features. Results: 238 features were found differentiable between group B and M based on whether their statistical p-values were less than 0.05. A forward search algorithm was used to select an optimal combination of features for the best classification and 9 features were identified. Our study found the best accuracy of classifying malignancy was 0.79±0.01 with the 10-fold cross-validation. The area under the ROC curve was 0.81±0.02. Conclusion: Benign nodules may be treated as a malignant tumor in low-dose CT and patients may undergo unnecessary surgeries or treatments. Our study may help radiologists to differentiate nodule malignancy for low-dose CT.« less

Endovascular Therapy is Effective and Safe for Patients with Severe Ischemic Stroke: Pooled Analysis of IMS III and MR CLEAN Data

PubMed Central

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

2015-01-01

Background and Purpose We assessed the effect of endovascular treatment in acute ischemic stroke patients with severe neurological deficit (NIHSS ≥20) following a pre-specified analysis plan. Methods The pooled analysis of the IMS III and MR CLEAN trial included participants with an NIHSS ≥20 prior to intravenous (IV) t-PA treatment (IMS III) or randomization (MR CLEAN) who were treated with IV t-PA ≤ 3 hours of stroke onset. Our hypothesis was that participants with severe stroke randomized to endovascular therapy following IV t-PA would have improved 90-day outcome (distribution of modified Rankin scale [mRS] scores), as compared to those who received IV t-PA alone. Results Among 342 participants in the pooled analysis (194 from IMS III, 148 from MR CLEAN), an ordinal logistic regression model showed that the endovascular group had superior 90-day outcome compared to the IV t-PA group (adjusted odds ratio [aOR] 1.78; 95% confidence interval [CI] 1.20-2.66). In the logistic regression model of the dichotomous outcome (mRS 0-2, or ‘functional independence’), the endovascular group had superior outcomes (aOR 1.97; 95% CI 1.09-3.56). Functional independence (mRS ≤2) at 90 days was 25% in the endovascular group as compared to 14% in the IV t-PA group. Conclusions Endovascular therapy following IV t-PA within 3 hours of symptom onset improves functional outcome at 90 days after severe ischemic stroke. PMID:26486865

Relationship between postoperative lung atelectasis and position of the endotracheal tube in pediatric living-donor liver transplantation.

PubMed

Lee, H-Y; Lu, C-H; Lu, H-F; Chen, C-L; Wang, C-H; Cheng, K-W; Wu, S-C; Jawan, B; Huang, C-J

2012-05-01

The aims of current study were: 1) to evaluate the incidence of lung atelectasis; and 2) to investigate whether or not the position of the endotracheal (ET) tube is associated with this complication. The medical records and chest roentgenograms of 183 pediatric patients who underwent living-donor liver transplantation were retrospectively reviewed and analyzed. Patients without atelectasis were grouped in group I (GI) and those with atelectasis in group II (GII). The patients' characteristics and ET tube level between groups were compared with unpaired Student's t test. Multiple binary logistic regressions were also performed to identify the important risk factors associated with lung atelectasis. Right upper lung (RUL) atelectsis could be found in ET tube at any level from T1 to T5, with incidence rates of 12.7%, 15.2%, 26.3%, 6.7%, and 100% for T1, T2, T3, T4, and T5, respectively. The incidence of atelectasis is 16.6%, and all of the atelectasis occurred in the RUL. No significant difference between groups was observed in the patients' characteristics, except for the amount of preoperative ascites. The likelihood of this risk factor could not be confirmed by multivariate binary logistic regression analysis. The incidence of lung atelectasis in our study was 16.6%, which all occurred in the RUL. No predictive risk factor from the patients' characteristics could be found, and no correlation between the level of the ET tube and the occurrence of RUL atelectasis could be observed. Copyright © 2012 Elsevier Inc. All rights reserved.

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

The effects of designation and volume of neonatal care on mortality and morbidity outcomes of very preterm infants in England: retrospective population-based cohort study.

PubMed

Watson, S I; Arulampalam, W; Petrou, S; Marlow, N; Morgan, A S; Draper, E S; Santhakumaran, S; Modi, N

2014-07-07

To examine the effects of designation and volume of neonatal care at the hospital of birth on mortality and morbidity outcomes in very preterm infants in a managed clinical network setting. A retrospective, population-based analysis of operational clinical data using adjusted logistic regression and instrumental variables (IV) analyses. 165 National Health Service neonatal units in England contributing data to the National Neonatal Research Database at the Neonatal Data Analysis Unit and participating in the Neonatal Economic, Staffing and Clinical Outcomes Project. 20 554 infants born at <33 weeks completed gestation (17 995 born at 27-32 weeks; 2559 born at <27 weeks), admitted to neonatal care and either discharged or died, over the period 1 January 2009-31 December 2011. Tertiary designation or high-volume neonatal care at the hospital of birth. Neonatal mortality, any in-hospital mortality, surgery for necrotising enterocolitis, surgery for retinopathy of prematurity, bronchopulmonary dysplasia and postmenstrual age at discharge. Infants born at <33 weeks gestation and admitted to a high-volume neonatal unit at the hospital of birth were at reduced odds of neonatal mortality (IV regression odds ratio (OR) 0.70, 95% CI 0.53 to 0.92) and any in-hospital mortality (IV regression OR 0.68, 95% CI 0.54 to 0.85). The effect of volume on any in-hospital mortality was most acute among infants born at <27 weeks gestation (IV regression OR 0.51, 95% CI 0.33 to 0.79). A negative association between tertiary-level unit designation and mortality was also observed with adjusted logistic regression for infants born at <27 weeks gestation. High-volume neonatal care provided at the hospital of birth may protect against in-hospital mortality in very preterm infants. Future developments of neonatal services should promote delivery of very preterm infants at hospitals with high-volume neonatal units. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

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.

Three quantitative approaches to the diagnosis of abdominal pain in children: practical applications of decision theory.

PubMed

Klein, M D; Rabbani, A B; Rood, K D; Durham, T; Rosenberg, N M; Bahr, M J; Thomas, R L; Langenburg, S E; Kuhns, L R

2001-09-01

The authors compared 3 quantitative methods for assisting clinicians in the differential diagnosis of abdominal pain in children, where the most common important endpoint is whether the patient has appendicitis. Pretest probability in different age and sex groups were determined to perform Bayesian analysis, binary logistic regression was used to determine which variables were statistically significantly likely to contribute to a diagnosis, and recursive partitioning was used to build decision trees with quantitative endpoints. The records of all children (1,208) seen at a large urban emergency department (ED) with a chief complaint of abdominal pain were immediately reviewed retrospectively (24 to 72 hours after the encounter). Attempts were made to contact all the patients' families to determine an accurate final diagnosis. A total of 1,008 (83%) families were contacted. Data were analyzed by calculation of the posttest probability, recursive partitioning, and binary logistic regression. In all groups the most common diagnosis was abdominal pain (ICD-9 Code 789). After this, however, the order of the most common final diagnoses for abdominal pain varied significantly. The entire group had a pretest probability of appendicitis of 0.06. This varied with age and sex from 0.02 in boys 2 to 5 years old to 0.16 in boys older than 12 years. In boys age 5 to 12, recursive partitioning and binary logistic regression agreed on guarding and anorexia as important variables. Guarding and tenderness were important in girls age 5 to 12. In boys age greater than 12, both agreed on guarding and anorexia. Using sensitivities and specificities from the literature, computed tomography improved the posttest probability for the group from.06 to.33; ultrasound improved it from.06 to.48; and barium enema improved it from.06 to.58. Knowing the pretest probabilities in a specific population allows the physician to evaluate the likely diagnoses first. Other quantitative methods can help judge how much importance a certain criterion should have in the decision making and how much a particular test is likely to influence the probability of a correct diagnosis. It now should be possible to make these sophisticated quantitative methods readily available to clinicians via the computer. Copyright 2001 by W.B. Saunders Company.

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

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.

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.

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

Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election

PubMed Central

2013-01-01

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention–designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs. PMID:24143060

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.

The comparison of landslide ratio-based and general logistic regression landslide susceptibility models in the Chishan watershed after 2009 Typhoon Morakot

NASA Astrophysics Data System (ADS)

WU, Chunhung

2015-04-01

The research built the original logistic regression landslide susceptibility model (abbreviated as or-LRLSM) and landslide ratio-based ogistic regression landslide susceptibility model (abbreviated as lr-LRLSM), compared the performance and explained the error source of two models. The research assumes that the performance of the logistic regression model can be better if the distribution of landslide ratio and weighted value of each variable is similar. Landslide ratio is the ratio of landslide area to total area in the specific area and an useful index to evaluate the seriousness of landslide disaster in Taiwan. The research adopted the landside inventory induced by 2009 Typhoon Morakot in the Chishan watershed, which was the most serious disaster event in the last decade, in Taiwan. The research adopted the 20 m grid as the basic unit in building the LRLSM, and six variables, including elevation, slope, aspect, geological formation, accumulated rainfall, and bank erosion, were included in the two models. The six variables were divided as continuous variables, including elevation, slope, and accumulated rainfall, and categorical variables, including aspect, geological formation and bank erosion in building the or-LRLSM, while all variables, which were classified based on landslide ratio, were categorical variables in building the lr-LRLSM. Because the count of whole basic unit in the Chishan watershed was too much to calculate by using commercial software, the research took random sampling instead of the whole basic units. The research adopted equal proportions of landslide unit and not landslide unit in logistic regression analysis. The research took 10 times random sampling and selected the group with the best Cox & Snell R2 value and Nagelkerker R2 value as the database for the following analysis. Based on the best result from 10 random sampling groups, the or-LRLSM (lr-LRLSM) is significant at the 1% level with Cox & Snell R2 = 0.190 (0.196) and Nagelkerke R2 = 0.253 (0.260). The unit with the landslide susceptibility value > 0.5 (≦ 0.5) will be classified as a predicted landslide unit (not landslide unit). The AUC, i.e. the area under the relative operating characteristic curve, of or-LRLSM in the Chishan watershed is 0.72, while that of lr-LRLSM is 0.77. Furthermore, the average correct ratio of lr-LRLSM (73.3%) is better than that of or-LRLSM (68.3%). The research analyzed in detail the error sources from the two models. In continuous variables, using the landslide ratio-based classification in building the lr-LRLSM can let the distribution of weighted value more similar to distribution of landslide ratio in the range of continuous variable than that in building the or-LRLSM. In categorical variables, the meaning of using the landslide ratio-based classification in building the lr-LRLSM is to gather the parameters with approximate landslide ratio together. The mean correct ratio in continuous variables (categorical variables) by using the lr-LRLSM is better than that in or-LRLSM by 0.6 ~ 2.6% (1.7% ~ 6.0%). Building the landslide susceptibility model by using landslide ratio-based classification is practical and of better performance than that by using the original logistic regression.

Clinical Outcome And Arginine Serum of Acute Ischemic Stroke Patients Supplemented by Snakehead Fish Extract

NASA Astrophysics Data System (ADS)

Pudjonarko, Dwi; Retnaningsih; Abidin, Zainal

2018-02-01

Background: Levels of arginine associated with clinical outcome in acute ischemic stroke (AIS). Arginine is a protein needed to synthesis nitric oxide (NO), a potential vasodilator and antioxidant. Snakehead fish is a source of protein which has antioxidant activity. Snakehead fish contains mineral, vitamin, and amino acids. One of the amino acids that were found quite high in snakehead fish extract is arginine. The aim of this study was done to determine the effect of snakehead fish extracts (SFE) on serum arginin levels and clinical outcome of AIS patients. Methods: It was double-blind randomized pretest-posttest control group design, with. AIS patients were divided into two groups i.e. snakehead fish extracts (SFE) and control. SFE group were administered 15 grams SFE for 7 days . Arginine serum levels and clinical outcome (measured by National Institute of Health Stroke Scale = NIHSS) were measured before and after treatment, other related factors were also analyzed in Logistic regression. Results: A total of 42 subjects who were performed random allocation as SFE or control group. There was no differences in subject characteristics between the two groups. There was a differences Δ arginine serum levels between SFE and control (33.6±19.95 μmol/L 0.3±2.51 μmol/L p<0.001). Change in NIHSS score in SFE improved significantly compared to the control group (4.14 ± 2.03; 2.52 ± 1.81;p=0.009 ). Logistic regression analysis showed only female gender factor that affected on improvement of NIHSS (OR=7; p=0,01). Conclusion: There is Clinical outcome improvement and enhancement of arginine serum levels in AIS patient with snakehead fish extract supplementation.

Decreased Levels of Circulating Carboxylated Osteocalcin in Children with Low Energy Fractures: A Pilot Study.

PubMed

Popko, Janusz; Karpiński, Michał; Chojnowska, Sylwia; Maresz, Katarzyna; Milewski, Robert; Badmaev, Vladimir; Schurgers, Leon J

2018-06-06

In the past decades, an increased interest in the roles of vitamin D and K has become evident, in particular in relation to bone health and prevention of bone fractures. The aim of the current study was to evaluate vitamin D and K status in children with low-energy fractures and in children without fractures. The study group of 20 children (14 boys, 6 girls) aged 5 to 15 years old, with radiologically confirmed low-energy fractures was compared with the control group of 19 healthy children (9 boys, 10 girls), aged 7 to 17 years old, without fractures. Total vitamin D (25(OH)D3 plus 25(OH)D2), calcium, BALP (bone alkaline phosphatase), NTx (N-terminal telopeptide), and uncarboxylated (ucOC) and carboxylated osteocalcin (cOC) serum concentrations were evaluated. Ratio of serum uncarboxylated osteocalcin to serum carboxylated osteocalcin ucOC:cOC (UCR) was used as an indicator of bone vitamin K status. Logistic regression models were created to establish UCR influence for odds ratio of low-energy fractures in both groups. There were no statistically significant differences in the serum calcium, NTx, BALP, or total vitamin D levels between the two groups. There was, however, a statistically significant difference in the UCR ratio. The median UCR in the fracture group was 0.471 compared with the control group value of 0.245 ( p < 0.0001). In the logistic regression analysis, odds ratio of low-energy fractures for UCR was calculated, with an increased risk of fractures by some 78.3 times. In this pilot study, better vitamin K status expressed as the ratio of ucOC:cOC-UCR—is positively and statistically significantly correlated with lower rate of low-energy fracture incidence.

Postpartum wound and bleeding complications in women who received peripartum anticoagulation.

PubMed

Limmer, Jane S; Grotegut, Chad A; Thames, Elizabeth; Dotters-Katz, Sarah K; Brancazio, Leo R; James, Andra H

2013-07-01

The objective of this study was to compare wound and bleeding complications between women who received anticoagulation after cesarean delivery due to history of prior venous thromboembolic disease, arterial disease, or being a thrombophilia carrier with adverse pregnancy outcome, to women not receiving anticoagulation. Women in the Duke Thrombosis Center Registry who underwent cesarean delivery during 2003-2011 and received postpartum anticoagulation (anticoagulation group, n=77), were compared with a subset of women who delivered during the same time period, but did not receive anticoagulation (no anticoagulation group, n=77). The no anticoagulation group comprised women who were matched to the anticoagulation group by age, body mass index, type of cesarean (no labor vs. labor), and date of delivery. Bleeding and wound complications were compared between the two groups. A multivariable logistic regression model was constructed to determine if anticoagulation was an independent predictor of wound complication. Women who received anticoagulation during pregnancy had a greater incidence of wound complications compared to those who did not (30% vs. 8%, p<0.001). Using multivariable logistic regression, while controlling for race, diabetes, chorioamnionitis, and aspirin use, anticoagulation predicted the development of any wound complication (OR 5.8, 95% CI 2.2, 17.6), but there were no differences in the mean estimated blood loss at delivery (782 vs. 778 ml, p=0.91), change in postpartum hematocrit (5.4 vs. 5.2%, p=0.772), or percent of women receiving blood products (6.5 vs. 1.3%, p=0.209) between the two groups. Anticoagulation following cesarean delivery is associated with an increased risk of post-cesarean wound complications, but not other postpartum bleeding complications. Copyright © 2013 Elsevier Ltd. All rights reserved.

Sleep architecture and obstructive sleep apnea in obese children with and without metabolic syndrome: a case control study.

PubMed

Jalilolghadr, Shabnam; Yazdi, Zohreh; Mahram, Manoochehr; Babaei, Farkhondeh; Esmailzadehha, Neda; Nozari, Hoormehr; Saffari, Fatemeh

2016-05-01

Obesity and biochemical parameters of metabolic disorders are both closely related to obstructive sleep apnea (OSA). The aim of this study was to compare sleep architecture and OSA in obese children with and without metabolic syndrome. Forty-two children with metabolic syndrome were selected as case group and 38 children without metabolic syndrome were matched for age, sex, and BMI as control group. The standardized Persian version of bedtime problems, excessive daytime sleepiness, awakenings during the night, regularity and duration of sleep, snoring (BEARS) and Children's Sleep Habits Questionnaires were completed, and polysomnography (PSG) was performed for all study subjects. Scoring was performed using the manual of American Academy of Sleep Medicine for children. Data were analyzed using chi-square test, T test, Mann-Whitney U test, and logistic regression analysis. Non-rapid eye movement (NREM) sleep and N1 stage in the case group were significantly longer than the control group, while REM sleep was significantly shorter. Waking after sleep onset (WASO) was significantly different between two groups. Severe OSA was more frequent in the control group. Multivariate logistic regression analysis showed that severe OSA (OR 21.478, 95 % CI 2.160-213.600; P = 0.009) and REM sleep (OR 0.856, 95 % CI 0.737-0.994; P = 0.041) had independent association with metabolic syndrome. Obese children with metabolic syndrome had increased WASO, N1 sleep stage, and severe OSA. But the results regarding sleep architecture are most likely a direct result of OSA severity. More longitudinal studies are needed to confirm the association of metabolic syndrome and OSA.

Differences of prevalence of dyslipidemia and risk factors related to LDL-c in the patients with abnormal fasting glucose between Uygur and Han in Xinjiang.

PubMed

Quan, Li; Hu, Lin; Zhang, Li; Jiang, Sheng

2015-01-01

To evaluate the incidence of dyslipidemia among Uygur and Han patients with impaired fasting glucose (IFG). To investigate the influence factors on LDL-c in this population. This cross-sectional study included a total of 4709 participants, consisting of Uygurs patients (n=2053) and Han patients (n=2656) from Xinjiang province, who were screened for diabetes mellitus. A stratified multistage sampling design was used to collect the participants. The influence factors on LDL-c were analyzed by Logistic regression analysis. Among the IFG patients (n=1757), Uighur IFG group had a higher prevalence of dyslipidemia than that of Han IFG group, 99.8% vs. 63.7%, P<0.05. Similar trends were existed in the prevalence of hypercholesteremia, hypertriglyceridemia, high LDL-c and low HDL-c (all P<0.05). Among the Uighur groups, IFG group had higher dyslipidemia rate than that of euglycemia group (74%). However, there was no such difference in the Han groups. Logistic regression analysis revealed that risk factors associated with LDL-c were age, total cholesterol and 2 h postprandial blood glucose for the Uighur IFG patients. However, gender and total cholesterol were risk factors for Han IFG patients. Uighur IFG patients had higher incidence of dyslipidemia than that of Han IFG patients. For Uyghur IFG patients, closing follow-up of total cholesterol and 2 h postprandial blood glucose were necessary. As to the Han IFG patients, we should pay more attention to male and total cholesterol in order to lower LDL-c levels. So, appropriately preventive and therapeutic measures should be chosen based on the characteristics of abnormal lipid profiles in different nationality.

The heterogeneous health latent classes of elderly people and their socio-demographic characteristics in Taiwan.

PubMed

Liu, Li-Fan; Tian, Wei-Hua; Yao, Hui-Ping

2014-01-01

The health care needs of elderly people were influenced by their heterogeneity. This study aimed to identify the health latent classes of elderly people by using latent class analysis to deal with heterogeneity and examine their socio-demographic characteristics. Data came from the 2005 National Health Interview Survey (NHIS) in Taiwan. In total, 2449 elderly individuals with available health indicators were examined in latent class analysis (LCA), and 2217 elderly community-dwellings with complete socio-demographic data were analyzed by multinomial logistic regression. Four health latent classes were identified which included 1066 (43.5%) people in the High Comorbidity (HC), 152 (6.2%) in the Functional Impairment (FI), 252 (10.3%) in the Frail (FR), and 979 (40.0%) in the Relatively Healthy (RH) group. Multinomial logistic regressions revealed socio-demographic characteristics among health classes. The variables associated with an increased likelihood of being in the FR group were age, female, and living with families. They were also correlated to ethnicity and educations. Apart from age and gender, the Functional Impairment group was less likely to be ethnicity of Hakka, more likely to live with others than were the RH group. The HC group tended to be younger, with higher educations, and more likely to live in urban area than the Functional Impairment group. The correlations between health classes and socio-demographic factors were discussed. The health status of elderly people includes a variety of health indicators. A person-centered approach is critical to identify the health heterogeneity of elderly people and manage their care needs by targeting differential aging. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Data Analysis of Criteria Governing Selection of Active Guard/Reserve Colonel

DTIC Science & Technology

2014-09-01

20  Figure 7.  Graphically depicts the Marital Status breakdown of the packets submitted by Married (M); Divorced (D); Single (S); Widowed...Status and compares them to the number of packets selected within the each group. Married (M); Divorced (D); Single (S); Widowed (W...logistic regression to examine the determining factors of poverty in Kenya. The study 8 digs deeper than the three indicators commonly thought to

'Many people know the law, but also many people violate it': discrimination experienced by people living with HIV/AIDS in Vietnam--results of a national study.

PubMed

Messersmith, Lisa J; Semrau, Katherine; Hammett, Theodore M; Phong, Nguyen Tuan; Tung, Nguyen Duy; Nguyen, Ha; Glandon, Douglas; Huong, Nguyen Mai; Anh, Hoang Tu

2013-01-01

In Vietnam, discrimination against people living with HIV/AIDS (PLHIV) is defined within and prohibited by the 2007 national HIV/AIDS law. Despite the law, PLHIV face discrimination in health care, employment, education and other spheres. This study presents the first national estimates of the levels and types of discrimination that are defined in Vietnamese law and experienced by PLHIV in Vietnam. A nationally representative sample of 1200 PLHIV was surveyed, and 129 PLHIV participated in focus group discussions (FGDs). In the last 12 months, nearly half of the survey population experienced at least one form of discrimination and many experienced up to six different types of discrimination. The most common forms of discrimination included disclosure of HIV status without consent; denial of access to education for children; loss of employment; advice, primarily from health care providers, to abstain from sex; and physical and emotional harm. In logistic regression analysis, the experience of discrimination differed by gender, region of residence and membership status in a PLHIV support group. The logistic regression and FGD results indicate that disclosure of HIV status without consent was associated with experiencing other forms of discrimination. Key programme and policy recommendations are discussed.

Medication adherence among patients in a chronic disease clinic.

PubMed

Tourkmani, Ayla M; Al Khashan, Hisham I; Albabtain, Monirah A; Al Harbi, Turki J; Al Qahatani, Hala B; Bakhiet, Ahmed H

2012-12-01

To assess motivation and knowledge domains of medication adherence intention, and to determine their predictors in an ambulatory setting. We conducted a cross-sectional survey study among patients attending a chronic disease clinic at the Family and Community Medicine Department, Prince Sultan Military Medical City, Riyadh, Kingdom of Saudi Arabia between June and September 2010. Adherence intention was assessed using Modified Morisky Scale. Predictors of low motivation and/or knowledge were determined using logistic regression models. A total of 347 patients were interviewed during the study duration. Most patients (75.5%) had 2 or more chronic diseases with an average of 6.3 +/- 2.3 medications, and 6.5 +/- 2.9 pills per prescription. The frequency of adherence intention was low (4.6%), variable (37.2%), and high (58.2%). In multivariate logistic regression analysis, younger age and having asthma were significantly associated with low motivation, while male gender, single status, and not having hypertension were significantly associated with low knowledge. Single status was the only independent predictor of low adherence intention. In a population with multiple chronic diseases and high illiteracy rate, more than 40% had low/variable intention to adhere to prescribed medications. Identifying predictors of this group may help in providing group-specific interventional programs.

Prevalence of comorbid substance use disorder during long-term central stimulant treatment in adult ADHD.

PubMed

Torgersen, Terje; Gjervan, Bjørn; Rasmussen, Kirsten; Vaaler, Arne; Nordahl, Hans M

2013-03-01

Central stimulant (CS) therapy is a cornerstone in treatment of adult attention-deficit/hyperactivity disorder (ADHD). Substance use disorder (SUD) is a common comorbid disorder of ADHD and might complicate the treatment. Our main objectives were to investigate the prevalence of SUD during CS treatment, and identify variables associated with SUD during the treatment. The collection of data was based on a naturalistic, retrospective approach using the medical records of a cohort of all adult ADHD patients (N = 117) starting treatment with CS in a specific catchment area in the period 1997 to May 2005. A logistic regression model was applied to identify possible predictors of SUD during CS treatment. The study showed no onset of SUD during the CS treatment in the group of patients without comorbid SUD at baseline (mean CS treatment length 41.1 months). In the group of patients with comorbid SUD at baseline, 58.5 % had one or more relapses of SUD during treatment (mean CS treatment length 27.9 months). Younger age and comorbid antisocial personality disorder were associated with relapse. In a logistic regression analysis, cannabis abstinence for more than 12 months was a negative predictor for relapse of SUD. CS treatment does not precipitate onset of SUD in adults without previous SUD.

Association between bullous pemphigoid and neurologic diseases: a case-control study.

PubMed

Casas-de-la-Asunción, E; Ruano-Ruiz, J; Rodríguez-Martín, A M; Vélez García-Nieto, A; Moreno-Giménez, J C

2014-11-01

In the past 10 years, bullous pemphigoid has been associated with other comorbidities and neurologic and psychiatric conditions in particular. Case series, small case-control studies, and large population-based studies in different Asian populations, mainland Europe, and the United Kingdom have confirmed this association. However, no data are available for the Spanish population. This was an observational, retrospective, case-control study with 1:2 matching. Fifty-four patients with bullous pemphigoid were selected. We compared the percentage of patients in each group with concurrent neurologic conditions, ischemic heart disease, diabetes, chronic obstructive pulmonary disease, and solid tumors using univariate logistic regression. An association model was constructed with conditional multiple logistic regression. The case group had a significantly higher percentage of patients with cerebrovascular accident and/or transient ischemic attack (odds ratio [OR], 3.06; 95% CI, 1.19-7.87], dementia (OR, 5.52; 95% CI, 2.19-13.93), and Parkinson disease (OR, 5; 95% CI, 1.57-15.94). A significantly higher percentage of cases had neurologic conditions (OR, 6.34; 95% CI, 2.89-13.91). Dementia and Parkinson disease were independently associated with bullous pemphigoid in the multivariate analysis. Patients with bullous pemphigoid have a higher frequency of neurologic conditions. Copyright © 2013 Elsevier España, S.L.U. and AEDV. All rights reserved.

Prognostic value of perfusion-weighted magnetic resonance imaging in acute intracerebral hemorrhage.

PubMed

Hu, Xibin; Bai, Xueqin; Zai, Ning; Sun, Xinhai; Zhu, Laimin; Li, Xian

2016-07-01

This study intends to investigate the prognostic value of perfusion-weighted magnetic resonance imaging in acute intracerebral hemorrhage. Demographic, clinical and biochemical data between acute intracerebral hemorrhage (AICH) and healthy volunteer groups were assessed in this study, such as rCBV and MTT values. The optimal cutoff values of rCBV and MTT for diagnosing AICH were determined by the ROC curves. Apart from that, we also investigated the association between rCBV/MTT values and cerebral hematoma volumes of AICH patients. The unconditional logistic regression was conducted to determine significant risk factors for AICH. AICH patients have significantly lower rCBV and higher MTT compared to the control group (all P < 0.05). As suggested by the relatively high sensitivity and specificity, both rCBV and MTT values could be utilized for AICH diagnosis. Moreover, rCBV and MTT were significantly associated with the cerebral hematoma volumes of AICH patients (all P < 0.05). Results from unconditional logistic regression analysis revealed that MTT was a significant risk factor for AICH (P < 0.05 and OR > 1), while rCBV is considered as a protective factor (P < 0.05 and OR < 1). Perfusion-weighted magnetic resonance imaging produces a high prognostic value for diagnosing AICH.

Statistical Modelling for Dropped Out School Children (DOSC) in East Nusa Tenggara Province Indonesia

NASA Astrophysics Data System (ADS)

Guntur, R. D.; Lobo, M.

2017-02-01

A research has been carried out to investigate the characteristics of reasons for DOSC and to determine the statistical model explaining factors which influence on the DOSC in the age group 7 - 18 years in East Nusa Tenggara (ENT) Province. Primary data of out of school children had been collected throughout interviews using prepared questionnaires in three selected districts. Data was then analysed using descriptive and logistic regression method. The analysis shows that from the 341 samples, there were 194DOSC. The majority of them were males, lived in the countryside, had farmer parents, had family size of 5, and had mothers with only primary education level. The main reasons of children to drop out from the primary and junior education levels were the inabilities of paying the school fees and the willingness to work in the farms to help their parents. For senior education level, it was because of the unaffordable school tuitions and no desire of children in having good education. Both partial and simultaneous parameter tests in the logistic regression model show that children who lived in countryside, from poor families, males were the three factors that significantly affected the number of DOSC in the group age with odds ratio values 2.48; 2.37; 1.97 respectively.

Personality traits, level of anxiety and styles of coping with stressin people with asthma and chronic obstructive pulmonary disease - a comparative analysis.

PubMed

Tabała, Klaudia; Wrzesińska, Magdalena; Stecz, Patryk; Kocur, Józef

2016-12-23

Chronic obstructive pulmonary disease (COPD) and asthma are a challenge to public health, with the sufferers experiencing a range of psychological factors affecting their health and behavior. The aim of the present study was to determine the level of anxiety, personality traits and stress-coping ability of patients with obstructive lung disease and comparison with a group of healthy controls. The research was conducted on a group of 150 people with obstructive lung diseases (asthma and COPD) and healthy controls (mean age = 56.0 ± 16.00). Four surveys were used: a sociodemographic survey, NEO-FFI Personality Inventory, State-Trait Anxiety Inventory (STAI), and Brief Cope Inventory. Logistic regression was used to identify the investigated variables which best differentiated the healthy and sick individuals. Patients with asthma or COPD demonstrated a significantly lower level of conscientiousness, openness to experience, active coping and planning, as well as higher levels of neuroticism and a greater tendency to behavioral disengagement. Logistic regression found trait-anxiety, openness to experience, positive reframing, acceptance, humor and behavioral disengagement to be best at distinguishing people with lung diseases from healthy individuals. The results indicate the need for intervention in the psychological functioning of people with obstructive diseases.

The CD4/CD8 ratio is associated with coronary artery disease (CAD) in elderly Chinese patients.

PubMed

Gao, Pan; Rong, Hong-Hui; Lu, Ting; Tang, Gang; Si, Liang-Yi; Lederer, James A; Xiong, Wei

2017-01-01

The aim of this study was to investigate the relationship between number of circulating T cells and coronary artery disease (CAD) in an elderly Chinese population. A total of 295 elderly inpatients (age≥60) were included in this cross-sectional study. Their clinical and biochemical characteristics were recorded. Patients were divided to two groups: control patients and CAD patients. The risk factors of CAD were explored by binary logistic regression analysis. Compared with control patients, the ratio of CD4 to CD8 T cells was significantly increased in CAD patients. There was no difference in the number of CD3, CD4, and CD8 T cells between the two groups. Multiple logistic regression analysis showed that CAD was independently associated with age, gender, body mass index (BMI), systolic blood pressure (SBP), chronic heart failure (CHF) and the CD4/CD8 ratio. In addition, after adjusting for different clinical parameters (including gender, age, CHF, hypertension, arrhythmia, SBP, and BMI), the risk of CAD was significantly increased in patients with a CD4/CD8 ratio>1.5. There was a strong and independent association between the ratio of CD4/CD8 and CAD in elderly Chinese population. Copyright © 2016. Published by Elsevier B.V.

What does theory-driven evaluation add to the analysis of self-reported outcomes of diabetes education? A comparative realist evaluation of a participatory patient education approach.

PubMed

Pals, Regitze A S; Olesen, Kasper; Willaing, Ingrid

2016-06-01

To explore the effects of the Next Education (NEED) patient education approach in diabetes education. We tested the use of the NEED approach at eight intervention sites (n=193). Six additional sites served as controls (n=58). Data were collected through questionnaires, interviews and observations. We analysed data using descriptive statistics, logistic regression and systematic text condensation. Results from logistic regression demonstrated better overall assessment of education program experiences and enhanced self-reported improvements in maintaining medications correctly among patients from intervention sites, as compared to control sites. Interviews and observations suggested that improvements in health behavior could be explained by mechanisms related to the education setting, including using person-centeredness and dialogue. However, similar mechanisms were observed at control sites. Observations suggested that the quality of group dynamics, patients' motivation and educators' ability to facilitate participation in education, supported by the NEED approach, contributed to better results at intervention sites. The use of participatory approaches and, in particular, the NEED patient education approach in group-based diabetes education improved self-management skills and health behavior outcomes among individuals with diabetes. The use of dialogue tools in diabetes education is advised for educators. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

A predictive model to allocate frequent service users of community-based mental health services to different packages of care.

PubMed

Grigoletti, Laura; Amaddeo, Francesco; Grassi, Aldrigo; Boldrini, Massimo; Chiappelli, Marco; Percudani, Mauro; Catapano, Francesco; Fiorillo, Andrea; Perris, Francesco; Bacigalupi, Maurizio; Albanese, Paolo; Simonetti, Simona; De Agostini, Paola; Tansella, Michele

2010-01-01

To develop predictive models to allocate patients into frequent and low service users groups within the Italian Community-based Mental Health Services (CMHSs). To allocate frequent users to different packages of care, identifying the costs of these packages. Socio-demographic and clinical data and GAF scores at baseline were collected for 1250 users attending five CMHSs. All psychiatric contacts made by these patients during six months were recorded. A logistic regression identified frequent service users predictive variables. Multinomial logistic regression identified variables able to predict the most appropriate package of care. A cost function was utilised to estimate costs. Frequent service users were 49%, using nearly 90% of all contacts. The model classified correctly 80% of users in the frequent and low users groups. Three packages of care were identified: Basic Community Treatment (4,133 Euro per six months); Intensive Community Treatment (6,180 Euro) and Rehabilitative Community Treatment (11,984 Euro) for 83%, 6% and 11% of frequent service users respectively. The model was found to be accurate for 85% of users. It is possible to develop predictive models to identify frequent service users and to assign them to pre-defined packages of care, and to use these models to inform the funding of psychiatric care.

Association between Nurse Staffing and In-Hospital Bone Fractures: A Retrospective Cohort Study.

PubMed

Morita, Kojiro; Matsui, Hiroki; Fushimi, Kiyohide; Yasunaga, Hideo

2017-06-01

To determine if sufficient nurse staffing reduced in-hospital fractures in acute care hospitals. The Japanese Diagnosis Procedure Combination inpatient (DPC) database from July 2010 to March 2014 linked with the Surveys for Medical Institutions. We conducted a retrospective cohort study to examine the association of inpatient nurse-to-occupied bed ratio (NBR) with in-hospital fractures. Multivariable logistic regression with generalized estimating equations was performed, adjusting for patient characteristics and hospital characteristics. We identified 770,373 patients aged 50 years or older who underwent planned major surgery for some forms of cancer or cardiovascular diseases. We used ICD-10 codes and postoperative procedure codes to identify patients with in-hospital fractures. Hospital characteristics were obtained from the "Survey of Medical Institutions and Hospital Report" and "Annual Report for Functions of Medical Institutions." Overall, 662 (0.09 percent) in-hospital fractures were identified. Logistic regression analysis showed that the proportion of in-hospital fractures in the group with the highest NBR was significantly lower than that in the group with the lowest NBR (adjusted odd ratios, 0.67; 95 percent confidence interval, 0.44-0.99; p = .048). Sufficient nurse staffing may be important to reduce postsurgical in-hospital fractures in acute care hospitals. © Health Research and Educational Trust.

Oral Microbiota and Risk for Esophageal Squamous Cell Carcinoma in a High-Risk Area of China.

PubMed

Chen, Xingdong; Winckler, Björn; Lu, Ming; Cheng, Hongwei; Yuan, Ziyu; Yang, Yajun; Jin, Li; Ye, Weimin

2015-01-01

Poor oral health has been linked with an increased risk of esophageal squamous cell carcinoma (ESCC). We investigated whether alteration of oral microbiota is associated with ESCC risk. Fasting saliva samples were collected from 87 incident and histopathologicallly diagnosed ESCC cases, 63 subjects with dysplasia and 85 healthy controls. All subjects were also interviewed with a questionnaire. V3-V4 region of 16S rRNA was amplified and sequenced by 454-pyrosequencing platform. Carriage of each genus was compared by means of multivariate-adjusted odds ratios derived from logistic regression model. Relative abundance was compared using Metastats method. Beta diversity was estimated using Unifrac and weighted Unifrac distances. Principal coordinate analysis (PCoA) was applied to ordinate dissimilarity matrices. Multinomial logistic regression was used to compare the coordinates between different groups. ESCC subjects had an overall decreased microbial diversity compared to control and dysplasia subjects (P<0.001). Decreased carriage of genera Lautropia, Bulleidia, Catonella, Corynebacterium, Moryella, Peptococcus and Cardiobacterium were found in ESCC subjects compared to non-ESCC subjects. Multinomial logistic regression analyses on PCoA coordinates also revealed that ESCC subjects had significantly different levels for several coordinates compared to non-ESCC subjects. In conclusion, we observed a correlation between altered salivary bacterial microbiota and ESCC risk. The results of our study on the saliva microbiome are of particular interest as it reflects the shift in microbial communities. Further studies are warranted to verify this finding, and if being verified, to explore the underlying mechanisms.

[Optimization of diagnosis indicator selection and inspection plan by 3.0T MRI in breast cancer].

PubMed

Jiang, Zhongbiao; Wang, Yunhua; He, Zhong; Zhang, Lejun; Zheng, Kai

2013-08-01

To optimize 3.0T MRI diagnosis indicator in breast cancer and to select the best MRI scan program. Totally 45 patients with breast cancers were collected, and another 35 patients with benign breast tumor served as the control group. All patients underwent 3.0T MRI, including T1- weighted imaging (T1WI), fat suppression of the T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), 1H magnetic resonance spectroscopy (1H-MRS) and dynamic contrast enhanced (DCE) sequence. With operation pathology results as the gold standard in the diagnosis of breast diseases, the pathological results of benign and malignant served as dependent variables, and the diagnostic indicators of MRI were taken as independent variables. We put all the indicators of MRI examination under Logistic regression analysis, established the Logistic model, and optimized the diagnosis indicators of MRI examination to further improve MRI scan of breast cancer. By Logistic regression analysis, some indicators were selected in the equation, including the edge feature of the tumor, the time-signal intensity curve (TIC) type and the apparent diffusion coefficient (ADC) value when b=500 s/mm2. The regression equation was Logit (P)=-21.936+20.478X6+3.267X7+ 21.488X3. Valuable indicators in the diagnosis of breast cancer are the edge feature of the tumor, the TIC type and the ADC value when b=500 s/mm2. Combining conventional MRI scan, DWI and dynamic enhanced MRI is a better examination program, while MRS is the complementary program when diagnosis is difficult.

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

PubMed

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

2016-08-01

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

Prevalence of abortion and stillbirth in a beef cattle system in Southeastern Mexico.

PubMed

Segura-Correa, José C; Segura-Correa, Victor M

2009-12-01

Prenatal mortality is an important cause of production losses in the livestock industry. This study estimates the prevalences of abortion and stillbirth in a beef cattle system and determines the significance of some risk factors, in the tropics of Mexico. Data were obtained from a Zebu cattle herd and their crosses with Bos taurus breeds, in Yucatan, Mexico. The logit of the probability of an abortion or stillbirth was modeled using binary logistic regression. The risk factors tested were: year of abortion (or calving), season of abortion (or calving), parity number and dam breed group. The effect of twins on stillbirth was tested using Fisher exact test. Of the 4175 calvings studied 49 were abortions (1.17%). Significant factors in the logistic regression analysis for abortions were season of abortion and parity number. The risk of abortion was lower in the dry seasons compared to the rainy and windy seasons (P = 0.009). The risk of abortion was higher in second parity cows followed by the third and first parity cows, as compared to older cows (P = 0.015). Of the 4126 births, 87 were stillbirths (2.11%). Significant factors in the logistic regression analysis for stillbirth were year of calving (P = 0.0001) and parity number (P < 0.001). The risk of stillbirth in first parity cows was 2.6 times that of old cows. Of the total births, 15 were twins (0.36%) of which 7 were born dead calves. Herd owners must focus on the significant risk factors under their control to reduce the prevalence of prenatal mortality.

[Multivariate ordinal logistic regression analysis on the association between consumption of fried food and both esophageal cancer and precancerous lesions].

PubMed

Guo, L W; Liu, S Z; Zhang, M; Chen, Q; Zhang, S K; Sun, X B

2017-12-10

Objective: To investigate the effect of fried food intake on the pathogenesis of esophageal cancer and precancerous lesions. Methods: From 2005 to 2013, all the residents aged 40-69 years from 11 counties (cities) where cancer screening of upper gastrointestinal cancer had been conducted in rural areas of Henan province, were recruited as the subjects of study. Information on demography and lifestyle was collected. The residents under study were screened with iodine staining endoscopic examination and biopsy samples were diagnosed pathologically, under standardized criteria. Subjects with high risk were divided into the groups based on their different pathological degrees. Multivariate ordinal logistic regression analysis was used to analyze the relationship between the frequency of fried food intake and esophageal cancer and precancerous lesions. Results: A total number of 8 792 cases with normal esophagus, 3 680 with mild hyperplasia, 972 with moderate hyperplasia, 413 with severe hyperplasia carcinoma in situ, and 336 cases of esophageal cancer were recruited. Results from multivariate logistic regression analysis showed that, when compared with those who did not eat fried food, the intake of fried food (<2 times/week: OR =1.60, 95% CI : 1.40-1.83; ≥2 times/week: OR =2.58, 95% CI : 1.98-3.37) appeared a risk factor for both esophageal cancer or precancerous lesions after adjustment for age, sex, marital status, educational level, body mass index, smoking and alcohol intake. Conclusion: The intake of fried food appeared a risk factor for both esophageal cancer and precancerous lesions.

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…

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.

[Relationship between dietary vitamin C and Type 2 diabetes].

PubMed

Li, Xiaoxiao; Wang, Xinliang; Wei, Jie; Yang, Tubao

2015-10-01

To examine the correlation between dietary vitamin C intake and Type 2 diabetes.
 A total of 5 168 participants from Xiangya Hospital, Central South University were randomly selected. According to the vitamin C intake, the participants were divided into 5 groups: a Q1 group (n=1 033), a Q2 group (n=1 034), a Q3 group (n=1 034), a Q4 group (n=1 034) and a Q5 group (n=1 033). They were also divided into a Type 2 diabetes group (n=502) and a non-diabetes group (n=4 666). The height, weight, and blood pressure were measured, and vitamin C intake and other dairy consumption were evaluated using a food frequency questionnaire and fasting plasma glucose (FPG). The analysis of variance (ANOVA), Chi-square test, Mann-Whitney U test and logistic regression model were used to analyze the relationship between dietary vitamin C and Type 2 diabetes.
 The univariate analysis showed that there were significant differences in the vitamin C consumption in energy intake, activity level, dietary fiber intake, nutritional supplementation status, drinking or not drinking, education level among the different vitamin C intake groups (all P<0.05). There were also significant differences in age, sex, body mass index (BMI), smoking status and vitamin C intake between the Type 2 diabetes group and the non-diabetes group (all P<0.05). After the adjustment for age, gender, hypertension, energy intake or smoking status, the multiple logistic regression model found that the multivariable adjusted OR was 0.610 (95% CI 0.428-0.870) for 
the highest level of vitamin C intake (>154.78 mg/d) in comparison with the lowest level (≤ 63.26 mg/d). The results suggested that the vitamin C intake was inversely associated with the Type 2 diabetes (r=-0.029, P<0.05).
 There is a significant negative correlation between the dietary vitamin C intake and the risk of Type 2 diabetes.

International consensus on preliminary definitions of improvement in adult and juvenile myositis.

PubMed

Rider, Lisa G; Giannini, Edward H; Brunner, Hermine I; Ruperto, Nicola; James-Newton, Laura; Reed, Ann M; Lachenbruch, Peter A; Miller, Frederick W

2004-07-01

To use a core set of outcome measures to develop preliminary definitions of improvement for adult and juvenile myositis as composite end points for therapeutic trials. Twenty-nine experts in the assessment of myositis achieved consensus on 102 adult and 102 juvenile paper patient profiles as clinically improved or not improved. Two hundred twenty-seven candidate definitions of improvement were developed using the experts' consensus ratings as a gold standard and their judgment of clinically meaningful change in the core set of measures. Seventeen additional candidate definitions of improvement were developed from classification and regression tree analysis, a data-mining decision tree tool analysis. Six candidate definitions specifying percentage change or raw change in the core set of measures were developed using logistic regression analysis. Adult and pediatric working groups ranked the 13 top-performing candidate definitions for face validity, clinical sensibility, and ease of use, in which the sensitivity and specificity were >/=75% in adult, pediatric, and combined data sets. Nominal group technique was used to facilitate consensus formation. The definition of improvement (common to the adult and pediatric working groups) that ranked highest was 3 of any 6 of the core set measures improved by >/=20%, with no more than 2 worse by >/=25% (which could not include manual muscle testing to assess strength). Five and 4 additional preliminary definitions of improvement for adult and juvenile myositis, respectively, were also developed, with several definitions common to both groups. Participants also agreed to prospectively test 6 logistic regression definitions of improvement in clinical trials. Consensus preliminary definitions of improvement were developed for adult and juvenile myositis, and these incorporate clinically meaningful change in all myositis core set measures in a composite end point. These definitions require prospective validation, but they are now proposed for use as end points in all myositis trials.

Association of comorbid mental health symptoms and physical health conditions with employee productivity.

PubMed

Parker, Kristin M; Wilson, Mark G; Vandenberg, Robert J; DeJoy, David M; Orpinas, Pamela

2009-10-01

This study tests the hypothesis that employees with comorbid physical health conditions and mental health symptoms are less productive than other employees. Self-reported health status and productivity measures were collected from 1723 employees of a national retail organization. chi2, analysis of variance, and linear contrast analyses were conducted to evaluate whether health status groups differed on productivity measures. Multivariate linear regression and multinomial logistic regression analyses were conducted to analyze how predictive health status was of productivity. Those with comorbidities were significantly less productive on all productivity measures compared with all other health status groups and those with only physical health conditions or mental health symptoms. Health status also significantly predicted levels of employee productivity. These findings provide evidence for the relationship between health statuses and productivity, which has potential programmatic implications.

Case-control study on the associations between lifestyle-behavioral risk factors and phlegm-wetness constitution.

PubMed

Zhu, Yanbo; Wang, Qi; Dai, Zhaoyu; Origasa, Hideki; Di, Jie; Wang, Yangyang; Lin, Lin; Fan, Chunpok

2014-06-01

To explore the relationships between different lifestyle-behavioral factors and phlegm-wetness type of Traditional Chinese Medicine constitution, so as to provide health management strategies for phlegm-wetness constitution. A case-control study was conducted with the cases selected from the database of Chinese constitution survey in 9 provinces or municipalities of China. 1380 cases met the diagnostic criteria of phlegm-wetness type were taken as the case group, and 1380 cases were randomly selected from gentleness type as the control group. Using Chi-square test to compare the differences of lifestyle-behavior composition in each group; single factor and multiple logistic regression analysis were used to compare the relationships of lifestyle-behavioral factors and phlegm-wetness type. There were statistically significant differences between phlegm-wetness type group and gentleness type group in lifestyle behaviors (dietary habits, tobacco and liquor consumptions, exercise habits, sleeping habits). The results of single factor logistic regression analysis demonstrated that the risk of phlegm-wetness constitution decreased significantly in light diet (odds ratio, OR = 0.68); The risk factors of phlegm-wetness type were fatty food intake (OR = 2.36), sleeping early and getting up late (OR = 1.87), tobacco smoking (OR = 1.83), barbecued food intake (OR = 1.68), alcohol drinking (OR = 1.63), salty food intake (OR = 1.44), sleeping erratically (OR = 1.43), less physical activities (OR = 1.42), sweet food intake (OR = 1.29), sleeping and getting up late (OR = 1.26), and pungent food intake (OR = 1.21), respectively. Regardless of the interaction among lifestyle-behavioral factors, the results of the multiple logistic regression analysis revealed that the risk factors of phlegm-wetness type were sleeping early and getting up late (OR = 1.94), fatty food intake (OR = 1.80), tobacco smoking (OR = 1.50), sleeping erratically (OR = 1.50), barbecued food intake (OR = 1.40), sleeping and getting up late (OR = 1.40), less physical activities (OR = 1.31), sleeping late and getting up early (OR = 1.27), and sweet food intake (OR = 1.27, respectively, and the risk of phlegm-wetness type still decreased significantly in light food intake (OR = 0.79). Light diet can decrease the risk of being phlegm-wetness constitution, and bad lifestyle behaviors such as sleeping early and getting up late, sleeping erratically, fatty food, barbecued food or sweet food intake, tobacco and liquor consumptions, and less physical activities can increase the risks of becoming phlegm-wetness constitution.

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.

Understanding the Personality and Behavioral Mechanisms Defining Hypersexuality in Men Who Have Sex with Men

PubMed Central

Miner, Michael H.; Romine, Rebecca Swinburne; Raymond, Nancy; Janssen, Erick; MacDonald, Angus; Coleman, Eli

2016-01-01

Objective The purpose of this study was to investigate personality factors and behavioral mechanisms that are relevant to hypersexuality in men who have sex with men. Method A sample of 242 men who have sex with men were recruited from various sites in a moderate size mid-western city. Participants were assigned to hypersexuality or control group using a SCID-type interview. Self-report inventories were administered that measured the broad band personality constructs of positive emotionality, negative emotionality and constraint, and more narrow constructs related to sexual behavioral control, behavioral activation, behavioral inhibition, sexual excitation, sexual inhibition, impulsivity, ADHD, and sexual behavior. Hierarchical logistic regression was used to determine the relationship between these personality and behavioral variables and group membership. Results A hierarchical logistic regression, controlling for age, revealed a significant positive relationship between hypersexuality and negative emotionality and a negative relationship with constraint. None of the behavioral mechanism variables entered this equation. However, a hierarchical multiple regression predicting sexual behavioral control indicated that lack of such control was positively related to sexual excitation and sexual inhibition due to the threat of performance failure and negatively related to sexual inhibition due to the threat of performance consequences and general behavioral inhibition Conclusions Hypersexuality was found to be related to two broad personality factors that are characterized by emotional reactivity, risk-taking, and impulsivity. The associated lack of sexual behavior control is influenced by both sexual excitatory and inhibitory mechanisms, but not general behavioral activation and inhibitory mechanisms. PMID:27486137

The influence of sexual harassment on mental health among female military personnel of the Republic of Korea Armed Forces.

PubMed

Kim, Tae Kyung; Lee, H-C; Lee, S G; Han, K-T; Park, E-C

2017-04-01

Reports of sexual harassment are becoming more frequent in Republic of Korea (ROK) Armed Forces. This study aimed to analyse the impact of sexual harassment on mental health among female military personnel of the ROK Armed Forces. Data from the 2014 Military Health Survey were used. Instances of sexual harassment were recorded as 'yes' or 'no'. Analysis of variance (ANOVA) was carried out to compare Kessler Psychological Distress Scale 10 (K-10) scores. Multiple logistic regression analysis was performed to identify associations between sexual harassment and K-10 scores. Among 228 female military personnel, 13 (5.7%) individuals experienced sexual harassment. Multiple logistic regression analysis revealed that sexual harassment had a significantly negative impact on K-10 scores (3.486, p<0.04). Higher K-10 scores among individuals experiencing sexual harassment were identified in the unmarried (including never-married) group (6.761, p<0.04), the short-term military service group (12.014, p<0.03) and the group whose length of service was <2 years (11.067, p<0.02). Sexual harassment has a negative impact on mental health. Factors associated with worse mental health scores included service classification and length of service. The results provide helpful information with which to develop measures for minimising the negative psychological effects from sexual harassment and promoting sexual harassment prevention policy. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

[Characteristics of Schizophrenia Patients' Homicide Behaviors and Their Correlations with Criminal Capacity].

PubMed

Sun, Z W; Shi, T T; Fu, P X

2017-02-01

To explore the characteristics of schizophrenia patients' homicide behaviors and the influences of the assessments of criminal capacity. Indicators such as demographic and clinical data, characteristics of criminal behaviors and criminal capacity from the suspects whom were diagnosed by forensic psychiatry as schizophrenia （ n =110） and normal mental （ n =70） with homicide behavior, were collected by self-made investigation form and compared. The influences of the assessments of criminal capacity on the suspects diagnosed as schizophrenia were also analyzed using logistic regression analysis. There were no significant statistical differences between the schizophrenic group and the normal mental group concerning age, gender, education and marital status （ P >0.05）. There were significant statistical differences between the two groups concerning thought disorder, emotion state and social function before crime （ P <0.05） and there were significant statistical differences in some characteristics of the case such as aggressive history （ P <0.05）, cue, trigger, plan, criminal incentives, object of crime, circumstance cognition and self-protection （ P <0.05）. Multivariate logistic regression analysis suggested that thought disorder, emotion state, social function, criminal incentives, plan and self-protection before crime of the schizophrenic group were positively correlated with the criminal capacity （ P <0.05）. The relevant influences of psychopathology and crime characteristics should be considered comprehensively for improving the accuracy of the criminal capacity evaluation on the suspects diagnosed as schizophrenia with homicide behavior. Copyright© by the Editorial Department of Journal of Forensic Medicine

Increased risk of pulmonary tuberculosis among patients with appendectomy in Taiwan.

PubMed

Lai, S-W; Lin, C-L; Liao, K-F; Tsai, S-M

2014-09-01

The aim of this study was to determine whether there is a relationship between appendectomy and pulmonary tuberculosis in Taiwan. We designed a case-control study by analyzing the database from the Taiwan National Health Insurance Program. In total, we found 11,366 individuals (aged 20 years and older) with newly diagnosed pulmonary tuberculosis as the case group and 45,464 individuals without pulmonary tuberculosis as the control group from 1998 to 2011. The case group and the control group were matched on sex, age, and index year of diagnosing pulmonary tuberculosis. Using the multivariable unconditional logistic regression model, we measured the odds ratio (OR) and 95 % confidence interval (CI) for the risk of pulmonary tuberculosis associated with appendectomy and other comorbidities. After controlling for covariables, the multivariable unconditional logistic regression model disclosed that the OR of pulmonary tuberculosis was 1.4 in appendectomized patients (95 % CI = 1.13, 1.75) when compared to individuals without appendectomy. In further analysis, comorbidity with chronic obstructive pulmonary diseases (OR = 4.63, 95 % CI = 3.21, 6.68), pneumoconiosis (OR = 7.80, 95 % CI = 1.43, 42.5), chronic kidney diseases (OR = 5.65, 95 % CI = 1.79, 17.8), or diabetes mellitus (OR = 2.11, 95 % CI = 1.30, 3.44) increased the risk of pulmonary tuberculosis in appendectomized patients. Individuals with appendectomy are at a 1.4-fold increased risk of pulmonary tuberculosis. Comorbidities, including chronic obstructive pulmonary disease, pneumoconiosis, chronic kidney diseases, and diabetes mellitus, enhance the risk of pulmonary tuberculosis.

Socioeconomic Inequality in Concurrent Tobacco and Alcohol Consumption

PubMed Central

Intarut, Nirun; Pukdeesamai, Piyalak

2017-01-01

Background: Whilst several studies have examined inequity of tobacco use and inequity of alcohol drinking individually, comparatively little is known about concurrent tobacco and alcohol consumption. The present study therefore investigated inequity of concurrent tobacco and alcohol consumption in Thailand. Methods: The 2015 Health and Welfare Survey was obtained from Thailand’s National Statistical Office and used as a source of national representative data. Concurrent tobacco and alcohol consumption was defined as current and concurrent use of both tobacco and alcohol. The wealth assets index was used as an indicator of socioeconomic inequity. Socioeconomic status included 5 groups ranging from poorest (Q1) to richest (Q5). A total of 55,920 households and 113,705 participants aged 15 years or over were included and analyzed. A weighted multiple logistic regression was performed. Results: The prevalence of concurrent tobacco and alcohol consumption, tobacco consumption only, and alcohol consumption only were 15.2% (95% CI: 14.9, 15.4), 4.7% (95% CI: 4.5, 4.8), and 18.9% (95% CI: 18.7, 19.1), respectively. Weighted multiple logistic regression showed that concurrent tobacco and alcohol consumption was high in the poorest socioeconomic group (P for trend <0.001), and tobacco consumption only was also high in the poorest group (P for trend <0.001). A high prevalence of alcohol consumption was observed in the richest group (P for trend <0.001). Conclusions: These findings suggest that tobacco and alcohol consumption prevention programs would be more effective if they considered socioeconomic inequities in concurrent tobacco and alcohol consumption rather than focusing on single drug use. PMID:28749620

Risk factors for acute surgical site infections after lumbar surgery: a retrospective study.

PubMed

Lai, Qi; Song, Quanwei; Guo, Runsheng; Bi, Haidi; Liu, Xuqiang; Yu, Xiaolong; Zhu, Jianghao; Dai, Min; Zhang, Bin

2017-07-19

Currently, many scholars are concerned about the treatment of postoperative infection; however, few have completed multivariate analyses to determine factors that contribute to the risk of infection. Therefore, we conducted a multivariate analysis of a retrospectively collected database to analyze the risk factors for acute surgical site infection following lumbar surgery, including fracture fixation, lumbar fusion, and minimally invasive lumbar surgery. We retrospectively reviewed data from patients who underwent lumbar surgery between 2014 and 2016, including lumbar fusion, internal fracture fixation, and minimally invasive surgery in our hospital's spinal surgery unit. Patient demographics, procedures, and wound infection rates were analyzed using descriptive statistics, and risk factors were analyzed using logistic regression analyses. Twenty-six patients (2.81%) experienced acute surgical site infection following lumbar surgery in our study. The patients' mean body mass index, smoking history, operative time, blood loss, draining time, and drainage volume in the acute surgical site infection group were significantly different from those in the non-acute surgical site infection group (p < 0.05). Additionally, diabetes mellitus, chronic obstructive pulmonary disease, osteoporosis, preoperative antibiotics, type of disease, and operative type in the acute surgical site infection group were significantly different than those in the non-acute surgical site infection group (p < 0.05). Using binary logistic regression analyses, body mass index, smoking, diabetes mellitus, osteoporosis, preoperative antibiotics, fracture, operative type, operative time, blood loss, and drainage time were independent predictors of acute surgical site infection following lumbar surgery. In order to reduce the risk of infection following lumbar surgery, patients should be evaluated for the risk factors noted above.

Farmers' mental health: A longitudinal sibling comparison - the HUNT study, Norway.

PubMed

Torske, Magnhild Oust; Bjørngaard, Johan Håkon; Hilt, Bjørn; Glasscock, David; Krokstad, Steinar

2016-06-01

Studies of the mental health of farmers have been largely cross-sectional and possibly confounded. We performed a prospective cohort study as well as a sibling comparison to control for unmeasured confounding. Our study included 76 583 participants aged ≥19 years from the Nord-Trøndelag Health Study [HUNT1 (1984-1986), HUNT2 (1995-1997) and HUNT3 (2006-2008)]. We used the Anxiety and Depression Index (ADI) and the Hospital Anxiety and Depression Scale (HADS) to measure symptoms of mental distress. We used logistic regression to investigate the association between occupation at baseline and symptoms of mental distress 11 years later and fixed effects conditional logistic regression to compare farmers with their siblings working in other occupations. In the prospective cohort study, farmers had similar odds of having symptoms of psychological distress and anxiety as other manual occupational groups. Among all the occupational groups in the study, farmers had the highest odds of having symptoms of depression [odds ratio (OR) 1.99, 95% confidence interval (CI) 1.55-2.55, reference group: higher grade professionals]. Compared with their farming brothers and sisters, siblings in other occupations had lower odds of having high depression (OR 0.70, 95% CI 0.55-0.89) and anxiety (OR 0.79, 95% CI 0.63-1.00) scores in 2006-2008. Farmers had higher odds of having high depression scores compared to both other occupational groups and their siblings who were not working as farmers, suggesting that working in agriculture may impact mental health.

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.…

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.

Impaired IQ and academic skills in adults who experienced moderate to severe infantile malnutrition: a 40-year study.

PubMed

Waber, Deborah P; Bryce, Cyralene P; Girard, Jonathan M; Zichlin, Miriam; Fitzmaurice, Garrett M; Galler, Janina R

2014-02-01

To evaluate IQ and academic skills in adults who experienced an episode of moderate-to-severe infantile malnutrition and a healthy control group, all followed since childhood in the Barbados Nutrition Study. IQ and academic skills were assessed in 77 previously malnourished adults (mean age = 38.4 years; 53% male) and 59 controls (mean age = 38.1 years; 54% male). Group comparisons were carried out by multiple regression and logistic regression, adjusted for childhood socioeconomic factors. The previously malnourished group showed substantial deficits on all outcomes relative to healthy controls (P < 0.0001). IQ scores in the intellectual disability range (< 70) were nine times more prevalent in the previously malnourished group (odds ratio = 9.18; 95% confidence interval = 3.50-24.13). Group differences in IQ of approximately one standard deviation were stable from adolescence through mid-life. Moderate-to-severe malnutrition during infancy is associated with a significantly elevated incidence of impaired IQ in adulthood, even when physical growth is completely rehabilitated. An episode of malnutrition during the first year of life carries risk for significant lifelong functional morbidity.

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

PubMed

Huang, Francis L

2018-04-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

The influence of sexual harassment on mental health among female military personnel of the Republic of Korea Armed Forces

PubMed Central

Kim, Tae Kyung; Lee, H-C; Lee, S G; Han, K-T; Park, E-C

2017-01-01

Introduction Reports of sexual harassment are becoming more frequent in Republic of Korea (ROK) Armed Forces. This study aimed to analyse the impact of sexual harassment on mental health among female military personnel of the ROK Armed Forces. Methods Data from the 2014 Military Health Survey were used. Instances of sexual harassment were recorded as ‘yes’ or ‘no’. Analysis of variance (ANOVA) was carried out to compare Kessler Psychological Distress Scale 10 (K-10) scores. Multiple logistic regression analysis was performed to identify associations between sexual harassment and K-10 scores. Results Among 228 female military personnel, 13 (5.7%) individuals experienced sexual harassment. Multiple logistic regression analysis revealed that sexual harassment had a significantly negative impact on K-10 scores (3.486, p<0.04). Higher K-10 scores among individuals experiencing sexual harassment were identified in the unmarried (including never-married) group (6.761, p<0.04), the short-term military service group (12.014, p<0.03) and the group whose length of service was <2 years (11.067, p<0.02). Conclusions Sexual harassment has a negative impact on mental health. Factors associated with worse mental health scores included service classification and length of service. The results provide helpful information with which to develop measures for minimising the negative psychological effects from sexual harassment and promoting sexual harassment prevention policy. PMID:27084842

Radiographic assessment of lower third molar eruption in different anteroposterior skeletal patterns and age-related groups.

PubMed

Jakovljevic, Aleksandar; Lazic, Emira; Soldatovic, Ivan; Nedeljkovic, Nenad; Andric, Miroslav

2015-07-01

To analyze radiographic predictors for lower third molar eruption among subjects with different anteroposterior skeletal relations and of different age groups. In total, 300 lower third molars were recorded on diagnostic digital orthopantomograms (DPTs) and lateral cephalograms (LCs). The radiographs were grouped according to sagittal intermaxillary angle (ANB), subject age, and level of lower third molar eruption. The DPT was used to analyze retromolar space, mesiodistal crown width, space/width ratio, third and second molar angulation (α, γ), third molar inclination (β), and gonion angle. The LC was used to determine ANB, angles of maxillar and mandibular prognathism (SNA, SNB), mandibular plane angle (SN/MP), and mandibular lengths. A logistic regression model was created using the statistically significant predictors. The logistic regression analysis revealed a statistically significant impact of β angle and distance between gonion and gnathion (Go-Gn) on the level of lower third molar eruption (P < .001 and P < .015, respectively). The retromolar space was significantly increased in the adult subgroup for all skeletal classes. The lower third molar impaction rate was significantly higher in the adult subgroup with the Class II (62.3%) compared with Class III subjects (31.7%; P < .013). The most favorable values of linear and angular predictors of mandibular third molar eruption were measured in Class III subjects. For valid estimation of mandibular third molar eruption, certain linear and angular measures (β angle, Go-Gn), as well as the size of the retromolar space, need to be considered.

Underage drinking on saturday nights, sociodemographic and environmental risk factors: a cross-sectional study

PubMed Central

2011-01-01

Background Excessive alcohol consumption in underage people is a rising phenomenon. A major proportion of the disease burden and deaths of young people in developed nations is attributable to alcohol abuse. The aim of this study was to investigate social, demographic and environmental factors that may raise the risk of Saturday night drinking and binge drinking among Italian school students. Methods The study was conducted on a sample of 845 Italian underage school students, by means of an anonymous, self-test questionnaire. Multivariate logistic regression was applied to identify independent risk factors for alcohol drinking and binge drinking. Ordered logistic regression was used to identify independent risk factors for harmful drinking patterns. Results The independent variables that confer a higher risk of drinking in underage students are older age classes, male sex, returning home after midnight, belonging to a group with little respect for the rules, or to a group where young people are not seen as leaders. The higher the perception of alcohol consumption by the group, the higher the risk. Spending time in bars or discos coincides with a two-fold or four-fold increase, respectively, in the risk of alcohol consumption. Conclusion Our findings show that certain environmental and social risk factors are associated with underage drinking. The most important role for preventing young people's exposure to these factors lies with the family, because only parents can exert the necessary control and provide a barrier against potentially harmful situations. PMID:21729273

Underage drinking on saturday nights, sociodemographic and environmental risk factors: a cross-sectional study.

PubMed

Gallimberti, Luigi; Chindamo, Sonia; Buja, Alessandra; Forza, Giovanni; Tognazzo, Federica; Galasso, Laura; Vinelli, Angela; Baldo, Vincenzo

2011-07-05

Excessive alcohol consumption in underage people is a rising phenomenon. A major proportion of the disease burden and deaths of young people in developed nations is attributable to alcohol abuse. The aim of this study was to investigate social, demographic and environmental factors that may raise the risk of Saturday night drinking and binge drinking among Italian school students. The study was conducted on a sample of 845 Italian underage school students, by means of an anonymous, self-test questionnaire. Multivariate logistic regression was applied to identify independent risk factors for alcohol drinking and binge drinking. Ordered logistic regression was used to identify independent risk factors for harmful drinking patterns. The independent variables that confer a higher risk of drinking in underage students are older age classes, male sex, returning home after midnight, belonging to a group with little respect for the rules, or to a group where young people are not seen as leaders. The higher the perception of alcohol consumption by the group, the higher the risk. Spending time in bars or discos coincides with a two-fold or four-fold increase, respectively, in the risk of alcohol consumption. Our findings show that certain environmental and social risk factors are associated with underage drinking. The most important role for preventing young people's exposure to these factors lies with the family, because only parents can exert the necessary control and provide a barrier against potentially harmful situations.

Identifying patterns of item missing survey data using latent groups: an observational study.

PubMed

Barnett, Adrian G; McElwee, Paul; Nathan, Andrea; Burton, Nicola W; Turrell, Gavin

2017-10-30

To examine whether respondents to a survey of health and physical activity and potential determinants could be grouped according to the questions they missed, known as 'item missing'. Observational study of longitudinal data. Residents of Brisbane, Australia. 6901 people aged 40-65 years in 2007. We used a latent class model with a mixture of multinomial distributions and chose the number of classes using the Bayesian information criterion. We used logistic regression to examine if participants' characteristics were associated with their modal latent class. We used logistic regression to examine whether the amount of item missing in a survey predicted wave missing in the following survey. Four per cent of participants missed almost one-fifth of the questions, and this group missed more questions in the middle of the survey. Eighty-three per cent of participants completed almost every question, but had a relatively high missing probability for a question on sleep time, a question which had an inconsistent presentation compared with the rest of the survey. Participants who completed almost every question were generally younger and more educated. Participants who completed more questions were less likely to miss the next longitudinal wave. Examining patterns in item missing data has improved our understanding of how missing data were generated and has informed future survey design to help reduce missing data. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Perioperative factors associated with pressure ulcer development after major surgery.

PubMed

Kim, Jeong Min; Lee, Hyunjeong; Ha, Taehoon; Na, Sungwon

2018-02-01

Postoperative pressure ulcers are important indicators of perioperative care quality, and are serious and expensive complications during critical care. This study aimed to identify perioperative risk factors for postoperative pressure ulcers. This retrospective case-control study evaluated 2,498 patients who underwent major surgery. Forty-three patients developed postoperative pressure ulcers and were matched to 86 control patients based on age, sex, surgery, and comorbidities. The pressure ulcer group had lower baseline hemoglobin and albumin levels, compared to the control group. The pressure ulcer group also had higher values for lactate levels, blood loss, and number of packed red blood cell ( p RBC) units. Univariate analysis revealed that pressure ulcer development was associated with preoperative hemoglobin levels, albumin levels, lactate levels, intraoperative blood loss, number of p RBC units, Acute Physiologic and Chronic Health Evaluation II score, Braden scale score, postoperative ventilator care, and patient restraint. In the multiple logistic regression analysis, only preoperative low albumin levels (odds ratio [OR]: 0.21, 95% CI: 0.05-0.82; P < 0.05) and high lactate levels (OR: 1.70, 95% CI: 1.07-2.71; P < 0.05) were independently associated with pressure ulcer development. A receiver operating characteristic curve was used to assess the predictive power of the logistic regression model, and the area under the curve was 0.88 (95% CI: 0.79-0.97; P < 0.001). The present study revealed that preoperative low albumin levels and high lactate levels were significantly associated with pressure ulcer development after surgery.

Serum uric acid concentrations are directly associated with the presence of benign multiple sclerosis.

PubMed

Simental-Mendía, Esteban; Simental-Mendía, Luis E; Guerrero-Romero, Fernando

2017-09-01

It has been reported that patients with multiple sclerosis (MS) exhibit lower serum uric acid levels; however, the association between uric acid concentrations and benign MS (BMS) has not been assessed. Hence, the objective of the present study was to determine whether the serum concentrations of uric acid are associated with the presence of BMS. Men and non-pregnant women over 16 years of age with diagnosis of MS were enrolled in a cross-sectional study. Expanded Disability Status Scale score < 3, progression of disease ≤10 years, diabetes, renal or hepatic diseases, gout, malignancy, alcohol intake, and treatment with thiazide diuretics and/or acetylsalicylic acid were exclusion criteria. According to subtype of disease, the eligible patients were allocated into groups with BMS and other varieties of MS. A logistic regression analysis was conducted in order to evaluate the association between serum concentrations of uric acid and BMS. A total of 106 patients were included, 39 in the group with BMS and 67 in the group with other varieties of MS. The logistic regression analysis adjusted by age, sex, and disease duration showed that increased concentrations of uric acid, indeed within the physiological levels, are significantly associated with the presence of BMS (OR = 2.60; 95% CI: 1.55-4.38, p < 0.001). The results of the present study suggest that elevated concentrations of uric acid, indeed within the physiological range, are likely linked to the presence of BMS.

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

PubMed

Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

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

A Comparison of Three Different Volumes of Levobupivacaine for Caudal Block in Children Undergoing Orchidopexy and Inguinal Hernia Repair.

PubMed

Marjanovic, Vesna; Budic, Ivana; Stevic, Marija; Simic, Dusica

2017-01-01

The aim of this study was to compare the efficacy of 3 different volumes of 0.25% levobupivacaine caudally administered on the effect of intra- and postoperative analgesia in children undergoing orchidopexy and inguinal hernia repair. Forty children, aged 1-7 years, American Society of Anesthesiologists (ASA) physical status I and II, were randomized into 3 different groups according to the applied volumes of 0.25% levobupivacaine: group 1 (n = 13): 0.6 mL∙kg-1; group 2 (n = 10): 0.8 mL∙kg-1; and group 3 (n = 17): 1.0 mL∙kg-1. The age, weight, duration of anesthesia, onset time of intraoperative analgesic, dosage, and addition of intraoperative fentanyl were compared among the groups. The time to first use of the analgesic and the number of patients who required analgesic 24 h after surgery in the time intervals within 6 h, between 6 and 12 h, and between 12 and 24 h postoperatively were evaluated among the groups. Statistical analyses were performed with a Dunnett t test, ANOVA, or Kruskal-Wallis test and χ2 test. Logistic regression analysis was used in order to examine predictive factors on duration of postoperative analgesia. Age, weight, duration of anesthesia, onset time of intraoperative analgesic, dosage, and addition of intraoperative fentanyl were similar among the groups. The time to first analgesic use did not differ among the groups, and logistic regression modelling showed that using the 3 different volumes of levobupivacaine had no predictive influence on duration of postoperative analgesia. The numbers of patients who required analgesics within 6 h (3/2/3), between 6 and 12 h (3/1/3), and between 12 and 24 h (1/0/2) after surgery were similar among the groups. The 3 different volumes of 0.25% levobupivacaine provided the same quality of intra- and postoperative pain relief in pediatric patients undergoing orchidopexy and inguinal hernia repair. © 2017 S. Karger AG, Basel.

Comparison of 21-Gauge and 22-Gauge Aspiration Needle in Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration

PubMed Central

Akulian, Jason; Lechtzin, Noah; Yasin, Faiza; Kamdar, Biren; Ernst, Armin; Ost, David E.; Ray, Cynthia; Greenhill, Sarah R.; Jimenez, Carlos A.; Filner, Joshua; Feller-Kopman, David

2013-01-01

Background: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive procedure originally performed using a 22-gauge (22G) needle. A recently introduced 21-gauge (21G) needle may improve the diagnostic yield and sample adequacy of EBUS-TBNA, but prior smaller studies have shown conflicting results. To our knowledge, this is the largest study undertaken to date to determine whether the 21G needle adds diagnostic benefit. Methods: We retrospectively evaluated the results of 1,299 patients from the American College of Chest Physicians Quality Improvement Registry, Education, and Evaluation (AQuIRE) Diagnostic Registry who underwent EBUS-TBNA between February 2009 and September 2010 at six centers throughout the United States. Data collection included patient demographics, sample adequacy, and diagnostic yield. Analysis consisted of univariate and multivariate hierarchical logistic regression comparing diagnostic yield and sample adequacy of EBUS-TBNA specimens by needle gauge. Results: A total of 1,235 patients met inclusion criteria. Sample adequacy was obtained in 94.9% of the 22G needle group and in 94.6% of the 21G needle group (P = .81). A diagnosis was made in 51.4% of the 22G and 51.3% of the 21G groups (P = .98). Multivariate hierarchical logistic regression showed no statistical difference in sample adequacy or diagnostic yield between the two groups. The presence of rapid onsite cytologic evaluation was associated with significantly fewer needle passes per procedure when using the 21G needle (P < .001). Conclusions: There is no difference in specimen adequacy or diagnostic yield between the 21G and 22G needle groups. EBUS-TBNA in conjunction with rapid onsite cytologic evaluation and a 21G needle is associated with fewer needle passes compared with a 22G needle. PMID:23632441

[An evaluation of clinical characteristics and prognosis of brain-stem infarction in diabetics].

PubMed

Lu, Zheng-qi; Li, Hai-yan; Hu, Xue-qiang; Zhang, Bing-jun

2011-01-01

To analyze the relationship between diabetics and the onset, clinical outcomes and prognosis of brainstem infarction, and to evaluate the impact of diabetes on brainstem infarction. Compare 172 cases of acute brainstem infarction in patients with or without diabetes. Analyze the associated risk factors of patients with brain-stem infarction in diabetics by multi-variate logistic regression analysis. Compare the National Institutes of Health Stroke Scale (NIHSS) and Modified Rankin scale (mRS) Score, pathogenetic condition and the outcome of the two groups in different times. The systolic blood pressure (SBP), TG, LDL-C, apolipoprotein B (Apo B), glutamyl transpeptidase (γ-GT), fibrinogen (Fb), fasting blood glucose (FPG) and glycosylated hemoglobin(HbA1c)in diabetic group were higher than those in non-diabetic group, which was statistically significant (P < 0.05). From multi-variate logistic regression analysis, γ-GT, Apo B and FPG were the risk predictors of diabetes with brainstem infarction(OR = 1.017, 4.667 and 3.173, respectively), while HDL-C was protective (OR = 0.288). HbA1c was a risk predictor of severity for acute brainstem infarction (OR = 1.299), while Apo A was beneficial (OR = 0.212). Compared with brain-stem infarction in non-diabetic group, NIHSS score and intensive care therapy of diabetic groups on the admission had no statistically significance, while the NIHSS score on discharge and the outcome at 6 months' of follow-up were statistically significant. Diabetes is closely associated with brainstem infarction. Brainstem infarction with diabetes cause more rapid progression, poorer prognosis, higher rates of mortality as well as disability and higher recurrence rate of cerebral infarction.

Retrospective Evaluation of Safety, Efficacy and Risk Factors for Pneumothorax in Simultaneous Localizations of Multiple Pulmonary Nodules Using Hook Wire System.

PubMed

Zhong, Yan; Xu, Xiao-Quan; Pan, Xiang-Long; Zhang, Wei; Xu, Hai; Yuan, Mei; Kong, Ling-Yan; Pu, Xue-Hui; Chen, Liang; Yu, Tong-Fu

2017-09-01

To evaluate the safety and efficacy of the hook wire system in the simultaneous localizations for multiple pulmonary nodules (PNs) before video-assisted thoracoscopic surgery (VATS), and to clarify the risk factors for pneumothorax associated with the localization procedure. Between January 2010 and February 2016, 67 patients (147 nodules, Group A) underwent simultaneous localizations for multiple PNs using a hook wire system. The demographic, localization procedure-related information and the occurrence rate of pneumothorax were assessed and compared with a control group (349 patients, 349 nodules, Group B). Multivariate logistic regression analyses were used to determine the risk factors for pneumothorax during the localization procedure. All the 147 nodules were successfully localized. Four (2.7%) hook wires dislodged before VATS procedure, but all these four lesions were successfully resected according to the insertion route of hook wire. Pathological diagnoses were acquired for all 147 nodules. Compared with Group B, Group A demonstrated significantly longer procedure time (p < 0.001) and higher occurrence rate of pneumothorax (p = 0.019). Multivariate logistic regression analysis indicated that position change during localization procedure (OR 2.675, p = 0.021) and the nodules located in the ipsilateral lung (OR 9.404, p < 0.001) were independent risk factors for pneumothorax. Simultaneous localizations for multiple PNs using a hook wire system before VATS procedure were safe and effective. Compared with localization for single PN, simultaneous localizations for multiple PNs were prone to the occurrence of pneumothorax. Position change during localization procedure and the nodules located in the ipsilateral lung were independent risk factors for pneumothorax.

An investigation of the concomitant use of angiotensin-converting enzyme inhibitors, non-steroidal anti-inflammatory drugs and diuretics.

PubMed

Bucsa, C; Moga, D C; Farcas, A; Mogosan, C; Dumitrascu, D L

2015-08-01

To determine in retrospective data the prevalence at hospital discharge of co-prescribing angiotensin-converting enzyme inhibitors (ACE-I) and non-steroidal anti-inflammatory drugs (NSAIDs) and ACE-I/NSAIDs and diuretics and to identify factors associated with the co-prescription. Secondary, we evaluated the extent of serum creatinine and potassium monitoring in patients treated with ACE-I and these associations and determined the prevalence of values above the upper normal limit (UNL) in monitored patients. Hospitalized patients with ACE-I in their therapy at discharge were included in 3 groups as follows: ACE-I, DT (double therapy with ACE-I and NSAIDs) and TT (triple therapy with ACE-I, NSAIDs and diuretics) groups. We evaluated differences on demographic characteristics, co-morbidities, medications, laboratory monitoring and quantified the patients with serum creatinine and potassium levels above the UNL using descriptive statistics. Logistic regression analysis with backward elimination was performed to identify significant predictors of combination therapy. Of 9960 admitted patients, 1214 were prescribed ACE-I, 40 were prescribed ACE-I/NSAIDs and 22 were prescribed ACE-I/NSAIDs/diuretics (3.13% and 1.72%, respectively, of the patients prescribed with ACE-I). Serum creatinine and potassium were monitored for the great majority of patients from all groups. The highest percentage of hyperkalemia was found in the DT group (10% of the patients) and of serum creatinine above UNL in the TT group (45.45%). The logistic regression final model showed that younger patients and monitoring for potassium were significantly associated with combination therapy. The prevalence of patients receiving DT/TT was relatively low and their monitoring during hospitalization was high. Factors associated with the combinations were younger patients and patients not tested for serum potassium.

Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics

PubMed Central

Wong, Man Sing; Peng, Fen; Zou, Bin; Shi, Wen Zhong; Wilson, Gaines J.

2016-01-01

Recent studies have suggested that some disadvantaged socio-demographic groups face serious environmental-related inequities in Hong Kong due to the rising ambient urban temperatures. Identifying heat-vulnerable groups and locating areas of Surface Urban Heat Island (SUHI) inequities is thus important for prioritizing interventions to mitigate death/illness rates from heat. This study addresses this problem by integrating methods of remote sensing retrieval, logistic regression modelling, and spatial autocorrelation. In this process, the SUHI effect was first estimated from the Land Surface Temperature (LST) derived from a Landsat image. With the scale assimilated to the SUHI and socio-demographic data, a logistic regression model was consequently adopted to ascertain their relationships based on Hong Kong Tertiary Planning Units (TPUs). Lastly, inequity “hotspots” were derived using spatial autocorrelation methods. Results show that disadvantaged socio-demographic groups were significantly more prone to be exposed to an intense SUHI effect: over half of 287 TPUs characterized by age groups of 60+ years, secondary and matriculation education attainment, widowed, divorced and separated, low and middle incomes, and certain occupation groups of workers, have significant Odds Ratios (ORs) larger than 1.2. It can be concluded that a clustering analysis stratified by age, income, educational attainment, marital status, and occupation is an effective way to detect the inequity hotspots of SUHI exposure. Additionally, inequities explored using income, marital status and occupation factors were more significant than the age and educational attainment in these areas. The derived maps and model can be further analyzed in urban/city planning, in order to mitigate the physical and social causes of the SUHI effect. PMID:26985899

Spatially Analyzing the Inequity of the Hong Kong Urban Heat Island by Socio-Demographic Characteristics.

PubMed

Wong, Man Sing; Peng, Fen; Zou, Bin; Shi, Wen Zhong; Wilson, Gaines J

2016-03-12

Recent studies have suggested that some disadvantaged socio-demographic groups face serious environmental-related inequities in Hong Kong due to the rising ambient urban temperatures. Identifying heat-vulnerable groups and locating areas of Surface Urban Heat Island (SUHI) inequities is thus important for prioritizing interventions to mitigate death/illness rates from heat. This study addresses this problem by integrating methods of remote sensing retrieval, logistic regression modelling, and spatial autocorrelation. In this process, the SUHI effect was first estimated from the Land Surface Temperature (LST) derived from a Landsat image. With the scale assimilated to the SUHI and socio-demographic data, a logistic regression model was consequently adopted to ascertain their relationships based on Hong Kong Tertiary Planning Units (TPUs). Lastly, inequity "hotspots" were derived using spatial autocorrelation methods. Results show that disadvantaged socio-demographic groups were significantly more prone to be exposed to an intense SUHI effect: over half of 287 TPUs characterized by age groups of 60+ years, secondary and matriculation education attainment, widowed, divorced and separated, low and middle incomes, and certain occupation groups of workers, have significant Odds Ratios (ORs) larger than 1.2. It can be concluded that a clustering analysis stratified by age, income, educational attainment, marital status, and occupation is an effective way to detect the inequity hotspots of SUHI exposure. Additionally, inequities explored using income, marital status and occupation factors were more significant than the age and educational attainment in these areas. The derived maps and model can be further analyzed in urban/city planning, in order to mitigate the physical and social causes of the SUHI effect.

Shock Index Correlates with Extravasation on Angiographs of Gastrointestinal Hemorrhage: A Logistics Regression Analysis

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

Nakasone, Yutaka, E-mail: n-yutaka@cd5.so-net.ne.jp; Ikeda, Osamu; Yamashita, Yasuyuki

We applied multivariate analysis to the clinical findings in patients with acute gastrointestinal (GI) hemorrhage and compared the relationship between these findings and angiographic evidence of extravasation. Our study population consisted of 46 patients with acute GI bleeding. They were divided into two groups. In group 1 we retrospectively analyzed 41 angiograms obtained in 29 patients (age range, 25-91 years; average, 71 years). Their clinical findings including the shock index (SI), diastolic blood pressure, hemoglobin, platelet counts, and age, which were quantitatively analyzed. In group 2, consisting of 17 patients (age range, 21-78 years; average, 60 years), we prospectively appliedmore » statistical analysis by a logistics regression model to their clinical findings and then assessed 21 angiograms obtained in these patients to determine whether our model was useful for predicting the presence of angiographic evidence of extravasation. On 18 of 41 (43.9%) angiograms in group 1 there was evidence of extravasation; in 3 patients it was demonstrated only by selective angiography. Factors significantly associated with angiographic visualization of extravasation were the SI and patient age. For differentiation between cases with and cases without angiographic evidence of extravasation, the maximum cutoff point was between 0.51 and 0.0.53. Of the 21 angiograms obtained in group 2, 13 (61.9%) showed evidence of extravasation; in 1 patient it was demonstrated only on selective angiograms. We found that in 90% of the cases, the prospective application of our model correctly predicted the angiographically confirmed presence or absence of extravasation. We conclude that in patients with GI hemorrhage, angiographic visualization of extravasation is associated with the pre-embolization SI. Patients with a high SI value should undergo study to facilitate optimal treatment planning.« less

Promoting Colorectal Cancer Screening Discussion

PubMed Central

Christy, Shannon M.; Perkins, Susan M.; Tong, Yan; Krier, Connie; Champion, Victoria L.; Skinner, Celette Sugg; Springston, Jeffrey K.; Imperiale, Thomas F.; Rawl, Susan M.

2013-01-01

Background Provider recommendation is a predictor of colorectal cancer (CRC) screening. Purpose To compare the effects of two clinic-based interventions on patient–provider discussions about CRC screening. Design Two-group RCT with data collected at baseline and 1 week post-intervention. Participants/setting African-American patients that were non-adherent to CRC screening recommendations (n=693) with a primary care visit between 2008 and 2010 in one of 11 urban primary care clinics. Intervention Participants received either a computer-delivered tailored CRC screening intervention or a nontailored informational brochure about CRC screening immediately prior to their primary care visit. Main outcome measures Between-group differences in odds of having had a CRC screening discussion about a colon test, with and without adjusting for demographic, clinic, health literacy, health belief, and social support variables, were examined as predictors of a CRC screening discussion using logistic regression. Intervention effects on CRC screening test order by PCPs were examined using logistic regression. Analyses were conducted in 2011 and 2012. Results Compared to the brochure group, a greater proportions of those in the computer-delivered tailored intervention group reported having had a discussion with their provider about CRC screening (63% vs 48%, OR=1.81, p<0.001). Predictors of a discussion about CRC screening included computer group participation, younger age, reason for visit, being unmarried, colonoscopy self-efficacy, and family member/friend recommendation (all p-values <0.05). Conclusions The computer-delivered tailored intervention was more effective than a nontailored brochure at stimulating patient–provider discussions about CRC screening. Those who received the computer-delivered intervention also were more likely to have a CRC screening test (fecal occult blood test or colonoscopy) ordered by their PCP. Trial registration This study is registered at www.clinicaltrials.gov NCT00672828. PMID:23498096

[Cross-sectional study on high-normal blood pressure and chronic kidney disease in occupational physical examination population in Changsha].

PubMed

Cao, Xia; Xie, Xiumei; Xu, Guo; Yuan, Hong; Chen, Zhiheng

2014-06-01

To investigate the relationship between high-normal blood pressure and chronic kidney disease (CKD) in occupational physical examination population in Changsha. With a convenient sampling method, a cross-sectional survey of representative sample of 11 274 white collar workers was conducted in Changsha between March 2011 and May 2011 in a large comprehensive hospital. All subjects were assigned into 4 groups: a normal blood pressure group, a high-normal blood pressure group, an undiagnosed hypertension group, and a diagnosed hypertension group. Anthropometry, blood pressure, blood sample and urine sample were measured with standard instruments and methodology for all the subjects. Multiple logistic regression analysis was used to identify risk factors for CKD. The prevalence of CKD in the normal blood pressure, high-normal blood pressure, undiagnosed hypertension, and diagnosed hypertension were 3.31%, 6.60%, 11.78%, and 17.35%, respectively. The prevalence of CKD in males was significantly higher than that in females (P<0.01). For males with high-normal blood pressure, the CKD risk was significantly greater (OR, 1.30; 95% CI:1.03 - 1.63) than those with optimal blood pressure. The logistic regression analysis showed that there was an additive effect of hyperuricemia on CKD risk in men with high-normal blood pressure compared with men with optimal blood pressure (OR, 2.25; 95% CI, 1.59 - 3.19; P<0.05). The prevalence of CKD in people with the high-normal blood pressure is 6.60% in occupational physical examination population in Changsha. CKD is a high risk for men with highnormal blood pressure and hyperuricemia is an independent risk factor.

Sexual function in F-111 maintenance workers: the study of health outcomes in aircraft maintenance personnel.

PubMed

Brown, Anthony; Gibson, Richard; Tavener, Meredith; Guest, Maya; D'Este, Catherine; Byles, Julie; Attia, John; Horsley, Keith; Harrex, Warren; Ross, James

2009-06-01

In Australia, four formal F-111 fuel tank deseal/reseal (DSRS) repair programs were implemented over more than two decades, each involving different processes and using a range of hazardous substances. However, health concerns were raised by a number of workers. The "Study of Health Outcomes in Aircraft Maintenance Personnel" was commissioned by the Australian Department of Defence to investigate potential adverse health outcomes as a result of being involved in the deseal/reseal processes. To compare measures of sexual function in F-111 aircraft fuel tank DSRS maintenance workers, against two appropriate comparison groups. Exposed and comparison participants completed a postal questionnaire which included general questions of health and health behavior, and two specific questions on sexual functioning. They also completed the International Index of Erectile Function (IIEF) questionnaire. Logistic regression was used to explore exposure status and outcome while adjusting for potential confounders. The three outcomes of interest for this study were the proportion of participants with erectile dysfunction (ED) according to the IIEF, the proportion with self-reported loss of interest in sex, and the proportion with self-reported problems with sexual functioning. Compared with each of the comparison groups, a larger proportion of the exposed group reported sexual problems and were classified as having ED according to the IIEF. In logistic regression, the odds of all three outcomes were higher for exposed participants relative to each comparison group and after adjustment for potentially confounding variables including anxiety and depression. There was a consistent problem with sexual functioning in the exposed group that is not explained by anxiety and depression, and it appears related to DSRS activities.

RNA Viruses that Cause Hemorrhagic, Encephalitic, and Febrile Disease

DTIC Science & Technology

1990-01-01

doses to levels that are subopti- effective dose (ED,0) values for Rift Valley mal for cures in other bunyavirus mouse Fever ( RVF ) virus (ED,, = 80 g...serum protein and AST Etiologic Agent (SGOT) identified in the placebo group by logistic regression], utilizing a stepwise lo- RVF , an old-world...treatment of H FRS in this study. Treatment reduced mortality RVF , distributed throughout sub-Saharan and improved several important aspects of Africa

Postmolar gestational trophoblastic neoplasia: beyond the traditional risk factors.

PubMed

Bakhtiyari, Mahmood; Mirzamoradi, Masoumeh; Kimyaiee, Parichehr; Aghaie, Abbas; Mansournia, Mohammd Ali; Ashrafi-Vand, Sepideh; Sarfjoo, Fatemeh Sadat

2015-09-01

To investigate the slope of linear regression of postevacuation serum hCG as an independent risk factor for postmolar gestational trophoblastic neoplasia (GTN). Multicenter retrospective cohort study. Academic referral health care centers. All subjects with confirmed hydatidiform mole and at least four measurements of β-hCG titer. None. Type and magnitude of the relationship between the slope of linear regression of β-hCG as a new risk factor and GTN using Bayesian logistic regression with penalized log-likelihood estimation. Among the high-risk and low-risk molar pregnancy cases, 11 (18.6%) and 19 cases (13.3%) had GTN, respectively. No significant relationship was found between the components of a high-risk pregnancy and GTN. The β-hCG return slope was higher in the spontaneous cure group. However, the initial level of this hormone in the first measurement was higher in the GTN group compared with in the spontaneous recovery group. The average time for diagnosing GTN in the high-risk molar pregnancy group was 2 weeks less than that of the low-risk molar pregnancy group. In addition to slope of linear regression of β-hCG (odds ratio [OR], 12.74, confidence interval [CI], 5.42-29.2), abortion history (OR, 2.53; 95% CI, 1.27-5.04) and large uterine height for gestational age (OR, 1.26; CI, 1.04-1.54) had the maximum effects on GTN outcome, respectively. The slope of linear regression of β-hCG was introduced as an independent risk factor, which could be used for clinical decision making based on records of β-hCG titer and subsequent prevention program. Copyright © 2015 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Valuing a Lifestyle Intervention for Middle Eastern Immigrants at Risk of Diabetes.

PubMed

Saha, Sanjib; Gerdtham, Ulf-G; Siddiqui, Faiza; Bennet, Louise

2018-02-27

Willingness-to-pay (WTP) techniques are increasingly being used in the healthcare sector for assessing the value of interventions. The objective of this study was to estimate WTP and its predictors in a randomized controlled trial of a lifestyle intervention exclusively targeting Middle Eastern immigrants living in Malmö, Sweden, who are at high risk of type 2 diabetes. We used the contingent valuation method to evaluate WTP. The questionnaire was designed following the payment-scale approach, and administered at the end of the trial, giving an ex-post perspective. We performed logistic regression and linear regression techniques to identify the factors associated with zero WTP value and positive WTP values. The intervention group had significantly higher average WTP than the control group (216 SEK vs. 127 SEK; p = 0.035; 1 U.S.$ = 8.52 SEK, 2015 price year) per month. The regression models demonstrated that being in the intervention group, acculturation, and self-employment were significant factors associated with positive WTP values. Male participants and lower-educated participants had a significantly higher likelihood of zero WTP. In this era of increased migration, our findings can help policy makers to take informed decisions to implement lifestyle interventions for immigrant populations.

Valuing a Lifestyle Intervention for Middle Eastern Immigrants at Risk of Diabetes

PubMed Central

Siddiqui, Faiza

2018-01-01

Willingness-to-pay (WTP) techniques are increasingly being used in the healthcare sector for assessing the value of interventions. The objective of this study was to estimate WTP and its predictors in a randomized controlled trial of a lifestyle intervention exclusively targeting Middle Eastern immigrants living in Malmö, Sweden, who are at high risk of type 2 diabetes. We used the contingent valuation method to evaluate WTP. The questionnaire was designed following the payment-scale approach, and administered at the end of the trial, giving an ex-post perspective. We performed logistic regression and linear regression techniques to identify the factors associated with zero WTP value and positive WTP values. The intervention group had significantly higher average WTP than the control group (216 SEK vs. 127 SEK; p = 0.035; 1 U.S.$ = 8.52 SEK, 2015 price year) per month. The regression models demonstrated that being in the intervention group, acculturation, and self-employment were significant factors associated with positive WTP values. Male participants and lower-educated participants had a significantly higher likelihood of zero WTP. In this era of increased migration, our findings can help policy makers to take informed decisions to implement lifestyle interventions for immigrant populations. PMID:29495529

Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression.

PubMed

Houpt, Joseph W; Bittner, Jennifer L

2018-07-01

Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences. Copyright © 2018 Elsevier Ltd. All rights reserved.

Macular microcirculation in hypertensive patients with and without branch retinal vein occlusion.

PubMed

Noma, Hidetaka; Funatsu, Hideharu; Sakata, Kumi; Harino, Seiyo; Mimura, Tatsuya; Hori, Sadao

2009-09-01

Our purpose was to determine whether a reduction in blood flow velocity (BFV) in the perifoveal capillaries is involved in the pathogenesis of branch retinal vein occlusion (BRVO) in patients with hypertension. Subjects included hypertensive patients with (n = 12) and without (n = 16) BRVO and healthy volunteers (n = 16). Perifoveal BFV was measured by the tracing method using fluorescein angiography and a scanning laser ophthalmoscope. Logistic regression analysis was performed to assess factors that influenced the presence or absence of BRVO. Mean BFV showed a significant decrease across the three groups (healthy controls: 1.49 +/- 0.11 mm/second; hypertensive patients without BRVO: 1.36 +/- 0.12 mm/second; hypertensive patients with BRVO: 1.16 +/- 0.24 mm/second; p(trend) < 0.001). Multivariate logistic regression analysis showed that BFV was a significant risk factor for the presence of BRVO. Perifoveal capillary BFV is reduced in hypertensive patients with and without BRVO. It is possible that a decrease in BFV may be involved in the occurrence of BRVO. Measurement of perifoveal capillary BFV may be useful for investigating the pathogenesis and progression of BRVO.

[Transmission disequilibrium test for nonsyndromic cleft lip and palate and segment homeobox gene-1 gene].

PubMed

Wu, Ping-An; Li, Yun-Liang; Wu, Han-Jiang; Wang, Kai; Fan, Guo-Zheng

2007-09-01

To investigate the relationship between muscle segment homeobox gene-1 (MSX1) and the genetic susceptibility of nonsyndromic cleft lip and palate (NSCLP) in Hunan Hans. One microsatellite DNA marker CA repeat in MSX1 intron region was used as genetic marker. The genotypes of 387 members in 129 NSCLP nuclear family trios were analyzed by polymerase chain reaction (PCR) and denaturing polyacrylamide gel electrophoresis. Then transmission disequilibrium test (TDT) and Logistic regression analysis were used to conduct association analysis. TDT analysis confirmed that CA4 allele in CL/P and CPO groups preferentially transmitted to the affected offspring (P = 0.018, P = 0.041). Logistic regression analysis indicated that the recessive model of inheritance was supported, and CA4 itself or CA4 acting as a marker for a disease allele or haplotype was inherited in a recessive fashion (P = 0.009). MSX1 gene is associated with NSCLP, and MSX1 gene may be directly involved either in the etiology of NSCLP or in linkage disequilibrium with disease-predisposing sites.

The Association Between Perceived Discrimination and Obesity in a Population-Based Multiracial and Multiethnic Adult Sample

PubMed Central

Williams, David R.

2009-01-01

Objectives. We examined whether perceived chronic discrimination was related to excess body fat accumulation in a random, multiethnic, population-based sample of US adults. Methods. We used multivariate multinomial logistic regression and logistic regression analyses to examine the relationship between interpersonal experiences of perceived chronic discrimination and body mass index and high-risk waist circumference. Results. Consistent with other studies, our analyses showed that perceived unfair treatment was associated with increased abdominal obesity. Compared with Irish, Jewish, Polish, and Italian Whites who did not experience perceived chronic discrimination, Irish, Jewish, Polish, and Italian Whites who perceived chronic discrimination were 2 to 6 times more likely to have a high-risk waist circumference. No significant relationship between perceived discrimination and the obesity measures was found among the other Whites, Blacks, or Hispanics. Conclusions. These findings are not completely unsupported. White ethnic groups including Polish, Italians, Jews, and Irish have historically been discriminated against in the United States, and other recent research suggests that they experience higher levels of perceived discrimination than do other Whites and that these experiences adversely affect their health. PMID:18923119

The perception of the relationship between environment and health according to data from Italian Behavioural Risk Factor Surveillance System (PASSI).

PubMed

Sampaolo, Letizia; Tommaso, Giulia; Gherardi, Bianca; Carrozzi, Giuliano; Freni Sterrantino, Anna; Ottone, Marta; Goldoni, Carlo Alberto; Bertozzi, Nicoletta; Scaringi, Meri; Bolognesi, Lara; Masocco, Maria; Salmaso, Stefania; Lauriola, Paolo

2017-01-01

"OBJECTIVES: to identify groups of people in relation to the perception of environmental risk and to assess the main characteristics using data collected in the environmental module of the surveillance network Italian Behavioral Risk Factor Surveillance System (PASSI). perceptive profiles were identified using a latent class analysis; later they were included as outcome in multinomial logistic regression models to assess the association between environmental risk perception and demographic, health, socio-economic and behavioural variables. the latent class analysis allowed to split the sample in "worried", "indifferent", and "positive" people. The multinomial logistic regression model showed that the "worried" profile typically includes people of Italian nationality, living in highly urbanized areas, with a high level of education, and with economic difficulties; they pay special attention to their own health and fitness, but they have a negative perception of their own psychophysical state. the application of advanced statistical analysis enable to appraise PASSI data in order to characterize the perception of environmental risk, making the planning of interventions related to risk communication possible. ".

Risk factor for preterm labor in Haji Adam Malik General Hospital, Pirngadi General hospital and satellite hospitals in Medan from January 2014 to December 2016

NASA Astrophysics Data System (ADS)

Sukatendel, K.; Hasibuan, C. L.; Pasaribu, H. P.; Sihite, H.; Ardyansah, E.; Situmorang, M. F.

2018-03-01

In 2010, Indonesia was ranked fifth in the world for the number of premature birth. Prematurity is a multifactorial problem. Preterm Labor (PTL) can occur spontaneously without a clear cause. Preventing PTL, its associated risk factors must be recognized first. To analyze risk factors associated with the incidence of PTL. It is a cross sectional study using secondary data obtained from medical records in Haji Adam Malik general hospital, Pirngadi general hospital and satellite hospitals in Medan from January 2014 to December 2016. Data were analyzed using chi-square method and logistic regression test. 148 cases for each group of preterm labor and obtained term laborin this study. Using the logistic regression test, three factors with astrong association to the incidence of identifiedpreterm labor. Antenatal Care frequency (OR 2,326; CI 95%), leucorrhea (OR 6,291; 95%), and premature rupture of membrane (OR 9,755; CI 95%). In conclusion, antenatal care frequency, leucorrhea, and history of premature rupture of themembrane may increase the incidence of Preterm Labor (PTL).

Occupational exposures and non-Hodgkin's lymphoma: Canadian case-control study.

PubMed

Karunanayake, Chandima P; McDuffie, Helen H; Dosman, James A; Spinelli, John J; Pahwa, Punam

2008-08-07

The objective was to study the association between Non-Hodgkin's Lymphoma (NHL) and occupational exposures related to long held occupations among males in six provinces of Canada. A population based case-control study was conducted from 1991 to 1994. Males with newly diagnosed NHL (ICD-10) were stratified by province of residence and age group. A total of 513 incident cases and 1506 population based controls were included in the analysis. Conditional logistic regression was conducted to fit statistical models. Based on conditional logistic regression modeling, the following factors independently increased the risk of NHL: farmer and machinist as long held occupations; constant exposure to diesel exhaust fumes; constant exposure to ionizing radiation (radium); and personal history of another cancer. Men who had worked for 20 years or more as farmer and machinist were the most likely to develop NHL. An increased risk of developing NHL is associated with the following: long held occupations of faer and machinist; exposure to diesel fumes; and exposure to ionizing radiation (radium). The risk of NHL increased with the duration of employment as a farmer or machinist.

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

PubMed

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

2014-08-01

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

Hyperhomocysteinemia is a risk factor for Alzheimer's disease in an Algerian population.

PubMed

Nazef, Khaled; Khelil, Malika; Chelouti, Hiba; Kacimi, Ghouti; Bendini, Mohamed; Tazir, Meriem; Belarbi, Soraya; El Hadi Cherifi, Mohamed; Djerdjouri, Bahia

2014-04-01

There is growing evidence that increased blood concentration of total homocysteine (tHcy) may be a risk factor for Alzheimer's disease (AD). The present study was conducted to evaluate the association of serum tHcy and other biochemical risk factors with AD. This is a case-control study including 41 individuals diagnosed with AD and 46 nondemented controls. Serum levels of all studied biochemical parameters were performed. Univariate logistic regression showed a significant increase of tHcy (p = 0.008), urea (p = 0.036) and a significant decrease of vitamin B12 (p = 0.012) in AD group vs. controls. Using multivariate logistic regression, tHcy (p = 0.007, OR = 1.376) appeared as an independent risk factor predictor of AD. There was a significant positive correlation between tHcy and creatinine (p <0.0001). A negative correlation was found between tHcy and vitamin B12 (p <0.0001). Our findings support that hyperhomocysteinemia is a risk factor for AD in an Algerian population and is also associated with vitamin B12 deficiency. Copyright © 2014 IMSS. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2010-02-01

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

Biomass Stoves and Lens Opacity and Cataract in Nepalese Women

PubMed Central

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

2014-01-01

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

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.

The Prevalence and Clinical Significance of Low Procalcitonin Levels Among Patients With Severe Sepsis or Septic Shock in the Emergency Department.

PubMed

Choe, Eun A; Shin, Tae Gun; Jo, Ik Joon; Hwang, Sung Yeon; Lee, Tae Rim; Cha, Won Chul; Sim, Min Seob

2016-07-01

The aims of this study were to evaluate the prevalence of low procalcitonin (PCT) levels among patients with severe sepsis or septic shock, and to investigate clinical characteristics and outcomes associated with low PCT levels. We analyzed data from the sepsis registry for patients with severe sepsis or septic shock in the emergency department. Based on a specific PCT cutoff value, patients were classified into two groups: a low PCT group, PCT <0.25 ng/mL; and a high PCT group, PCT ≥0.25 ng/mL. The primary endpoint was 28-day mortality. A multivariable logistic regression model was used to evaluate independent factors associated with low PCT and 28-day mortality. A total of 1,212 patients were included. Of the eligible patients, 154 (12.7%) were assigned to the low PCT group, and 1,058 (87.3%) to the high PCT group. The 28-day mortality was 4.6% in the low PCT group and 13.5% in the high PCT group (P < 0.01). The adjusted odds ratio of the low PCT group for 28-day mortality was 0.43 (95% CI 0.19-0.98; P = 0.04). There was no trend of increasing mortality among higher PCT level patients. In a logistic regression model, factors associated with low PCT were pneumonia, lower C-reactive protein levels, lower lactate levels, the absence of bacteremia, and the absence of organ failure. Intra-abdominal infection and obesity were associated with high PCT. Initial low PCT levels were common among patients diagnosed with severe sepsis or septic shock in the emergency department, suggesting favorable outcomes. The prevalence of low PCT levels was significantly different according to obesity, the source of infection, C-reactive protein levels, lactate levels, bacteremia, and organ failure.

The influence of advanced age on venous-arterial extracorporeal membrane oxygenation outcomes.

PubMed

Salna, Michael; Takeda, Koji; Kurlansky, Paul; Ikegami, Hirohisa; Fan, Liqiong; Han, Jiho; Stein, Samantha; Topkara, Veli; Yuzefpolskaya, Melana; Colombo, Paolo C; Karmpaliotis, Dimitrios; Naka, Yoshifumi; Kirtane, Ajay J; Garan, Arthur R; Takayama, Hiroo

2018-01-22

Ethical and health care economic concerns surround the use of venous-arterial extracorporeal membrane oxygenation (VA-ECMO) in elderly patients. Patients requiring VA-ECMO are often in critical condition and the decision to cannulate is time-sensitive. We investigated the relationship between age and VA-ECMO outcomes to better inform this decision. This is a retrospective study of 355 patients placed on VA-ECMO between March 2007 and August 2016 at our institution. Using piecewise modelling, age became associated with in-hospital mortality after 63 years. Based on further analysis with the χ2 statistic maximization, patients were divided into 2 age groups: ≤72 years old [Group Y (Young), n = 310] and >72 years old [Group O (Old), n = 45]. Multivariable logistic regression was performed to identify preoperative predictors of in-hospital mortality. Patients over the age of 72 had a significantly higher prevalence of comorbidities, including coronary disease, previous strokes and chronic kidney disease. Weaning from ECMO was achieved in 76% of Group Y and 47% of Group O (P < 0.001). In-hospital mortality was 52% among Group Y and 69% among Group O (P = 0.037). Multivariable logistic regression using preoperative risk factors identified coronary artery disease, acute decompensated heart failure and an age >72 years as independent predictors of mortality (age >72 years: odds ratio 2.71, 95% confidence interval 1.22-6.00; P = 0.01). VA-ECMO in-hospital mortality is considerable across all age groups. However, age only becomes associated with mortality after 63 years and rises dramatically after 72 years. This study provides useful insight into these time-sensitive decisions for the development of possible practice guidelines. © The Author(s) 2018. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Pedestrians injured by automobiles: risk factors for cervical spine injuries.

PubMed

Yanar, Hakan; Demetriades, Demetrios; Hadjizacharia, Pantelis; Hatzizacharia, Pantelis; Nomoto, Shirley; Salim, Ali; Inaba, Kenji; Rhee, Peter; Chan, Linda S

2007-12-01

Diagnosis of cervical spine injuries (CSI) in multitrauma patients, especially in the presence of head trauma, can be difficult. Identification of risk factors associated with CSI can help avoid missed or delayed diagnosis. Trauma registry study of pedestrian injuries caused by being hit by an automobile. Data abstracted for each patient included age, gender, Glasgow Coma Score on admission, Injury Severity Score, Abbreviated Injury Scale (AIS) for each body area, level of cervical spine injuries, and associated injuries. The incidence of spine injuries was derived for 4 age groups (14 years and younger, 15 to 55 years, 56 to 65 years, and older than 65 years). Logistic regression analysis was performed to identify risk factors associated with CSI. There were 8,401 pedestrian injuries caused by automobiles, and 178 patients (2.1%) had CSI. Incidence of CSI increased with age (0.3% in the age group 14 years and younger, 2.2% in the group 15 to 55 years, 3.7% in the group 56 to 65 years, and 4.4% in the group older than 65 years). Using the youngest age group (14 years and younger) as reference, relative risk of CSI in the other groups was 7.0, 12.1, and 14.2, respectively (p < 0.0001). Patients with severe head trauma (AIS > 3) were significantly more likely to have CSI than patients with less severe head injuries (AIS 3), severe chest trauma (AIS > 3), pelvic fracture, and femur fracture as independent risk factors for CSI. Incidence of CSI after injuries to pedestrians hit by automobiles increases with age and severity of head trauma. Age, severe head trauma, severe chest trauma, pelvic fracture, and femur fractures are independent predictors of CSI.

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.

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.

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.

Optimization of Game Formats in U-10 Soccer Using Logistic Regression Analysis

PubMed Central

Amatria, Mario; Arana, Javier; Anguera, M. Teresa; Garzón, Belén

2016-01-01

Abstract Small-sided games provide young soccer players with better opportunities to develop their skills and progress as individual and team players. There is, however, little evidence on the effectiveness of different game formats in different age groups, and furthermore, these formats can vary between and even within countries. The Royal Spanish Soccer Association replaced the traditional grassroots 7-a-side format (F-7) with the 8-a-side format (F-8) in the 2011-12 season and the country’s regional federations gradually followed suit. The aim of this observational methodology study was to investigate which of these formats best suited the learning needs of U-10 players transitioning from 5-aside futsal. We built a multiple logistic regression model to predict the success of offensive moves depending on the game format and the area of the pitch in which the move was initiated. Success was defined as a shot at the goal. We also built two simple logistic regression models to evaluate how the game format influenced the acquisition of technicaltactical skills. It was found that the probability of a shot at the goal was higher in F-7 than in F-8 for moves initiated in the Creation Sector-Own Half (0.08 vs 0.07) and the Creation Sector-Opponent's Half (0.18 vs 0.16). The probability was the same (0.04) in the Safety Sector. Children also had more opportunities to control the ball and pass or take a shot in the F-7 format (0.24 vs 0.20), and these were also more likely to be successful in this format (0.28 vs 0.19). PMID:28031768

Prevalence of consistent condom use with various types of sex partners and associated factors among money boys in Changsha, China.

PubMed

Wang, Lian-Hong; Yan, Jin; Yang, Guo-Li; Long, Shuo; Yu, Yong; Wu, Xi-Lin

2015-04-01

Money boys with inconsistent condom use (less than 100% of the time) are at high risk of infection by human immunodeficiency virus (HIV) or sexually transmitted infection (STI), but relatively little research has examined their risk behaviors. We investigated the prevalence of consistent condom use (100% of the time) and associated factors among money boys. A cross-sectional study using a structured questionnaire was conducted among money boys in Changsha, China, between July 2012 and January 2013. Independent variables included socio-demographic data, substance abuse history, work characteristics, and self-reported HIV and STI history. Dependent variables included the consistent condom use with different types of sex partners. Among the participants, 82.4% used condoms consistently with male clients, 80.2% with male sex partners, and 77.1% with female sex partners in the past 3 months. A multiple stepwise logistic regression model identified four statistically significant factors associated with lower likelihoods of consistent condom use with male clients: age group, substance abuse, lack of an "employment" arrangement, and having no HIV test within the prior 6 months. In a similar model, only one factor associated significantly with lower likelihoods of consistent condom use with male sex partners was identified in multiple stepwise logistic regression analyses: having no HIV test within the prior six months. As for female sex partners, two significant variables were statistically significant in the multiple stepwise logistic regression analysis: having no HIV test within the prior 6 months and having STI history. Interventions which are linked with more realistic and acceptable HIV prevention methods are greatly warranted and should increase risk awareness and the behavior of consistent condom use in both commercial and personal relationship. © 2015 International Society for Sexual Medicine.

Association between family structure, maternal education level, and maternal employment with sedentary lifestyle in primary school-age children.

PubMed

Vázquez-Nava, Francisco; Treviño-Garcia-Manzo, Norberto; Vázquez-Rodríguez, Carlos F; Vázquez-Rodríguez, Eliza M

2013-01-01

To determine the association between family structure, maternal education level, and maternal employment with sedentary lifestyle in primary school-age children. Data were obtained from 897 children aged 6 to 12 years. A questionnaire was used to collect information. Body mass index (BMI) was determined using the age- and gender-specific Centers for Disease Control and Prevention definition. Children were categorized as: normal weight (5(th) percentile≤BMI<85(th) percentile), at risk for overweight (85(th)≤BMI<95(th) percentile), overweight (≥ 95(th) percentile). For the analysis, overweight was defined as BMI at or above the 85(th) percentile for each gender. Adjusted odds ratios (adjusted ORs) for physical inactivity were determined using a logistic regression model. The prevalence of overweight was 40.7%, and of sedentary lifestyle, 57.2%. The percentage of non-intact families was 23.5%. Approximately 48.7% of the mothers had a non-acceptable educational level, and 38.8% of the mothers worked outside of the home. The logistic regression model showed that living in a non-intact family household (adjusted OR=1.67; 95% CI=1.04-2.66) is associated with sedentary lifestyle in overweight children. In the group of normal weight children, logistic regression analysis show that living in a non-intact family, having a mother with a non-acceptable education level, and having a mother who works outside of the home were not associated with sedentary lifestyle. Living in a non-intact family, more than low maternal educational level and having a working mother, appears to be associated with sedentary lifestyle in overweight primary school-age children. Copyright © 2013 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

Predicting Grade 3 Acute Diarrhea During Radiation Therapy for Rectal Cancer Using a Cutoff-Dose Logistic Regression Normal Tissue Complication Probability Model

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

Robertson, John M., E-mail: jrobertson@beaumont.ed; Soehn, Matthias; Yan Di

Purpose: Understanding the dose-volume relationship of small bowel irradiation and severe acute diarrhea may help reduce the incidence of this side effect during adjuvant treatment for rectal cancer. Methods and Materials: Consecutive patients treated curatively for rectal cancer were reviewed, and the maximum grade of acute diarrhea was determined. The small bowel was outlined on the treatment planning CT scan, and a dose-volume histogram was calculated for the initial pelvic treatment (45 Gy). Logistic regression models were fitted for varying cutoff-dose levels from 5 to 45 Gy in 5-Gy increments. The model with the highest LogLikelihood was used to developmore » a cutoff-dose normal tissue complication probability (NTCP) model. Results: There were a total of 152 patients (48% preoperative, 47% postoperative, 5% other), predominantly treated prone (95%) with a three-field technique (94%) and a protracted venous infusion of 5-fluorouracil (78%). Acute Grade 3 diarrhea occurred in 21%. The largest LogLikelihood was found for the cutoff-dose logistic regression model with 15 Gy as the cutoff-dose, although the models for 20 Gy and 25 Gy had similar significance. According to this model, highly significant correlations (p <0.001) between small bowel volumes receiving at least 15 Gy and toxicity exist in the considered patient population. Similar findings applied to both the preoperatively (p = 0.001) and postoperatively irradiated groups (p = 0.001). Conclusion: The incidence of Grade 3 diarrhea was significantly correlated with the volume of small bowel receiving at least 15 Gy using a cutoff-dose NTCP model.« less

The Association Between Spontaneous Hyperventilation, Delayed Cerebral Ischemia, and Poor Neurological Outcome in Patients with Subarachnoid Hemorrhage.

PubMed

Williamson, Craig A; Sheehan, Kyle M; Tipirneni, Renuka; Roark, Christopher D; Pandey, Aditya S; Thompson, B Gregory; Rajajee, Venkatakrishna

2015-12-01

The frequency and associations of spontaneous hyperventilation in subarachnoid hemorrhage (SAH) are unknown. Because hyperventilation decreases cerebral blood flow, it may exacerbate delayed cerebral ischemia (DCI) and worsen neurological outcome. This is a retrospective analysis of data from a prospectively collected cohort of SAH patients at an academic medical center. Spontaneous hyperventilation was defined by PaCO2 <35 mmHg and pH >7.45 and subdivided into moderate and severe groups. Clinical and demographic characteristics of patients with and without spontaneous hyperventilation were compared using χ (2) or t tests. Bivariate and multivariable logistic regression analyses were conducted to examine the association of moderate and severe hyperventilation with DCI and discharge neurological outcome. Of 207 patients, 113 (55 %) had spontaneous hyperventilation. Spontaneously hyperventilating patients had greater illness severity as measured by the Hunt-Hess, World Federation of Neurosurgical Societies (WFNS), and SAH sum scores. They were also more likely to develop the following complications: pneumonia, neurogenic myocardial injury, systemic inflammatory response syndrome (SIRS), radiographic vasospasm, DCI, and poor neurological outcome. In a multivariable logistic regression model including age, gender, WFNS, SAH sum score, pneumonia, neurogenic myocardial injury, etiology, and SIRS, only moderate [odds ratio (OR) 2.49, 95 % confidence interval (CI) 1.10-5.62] and severe (OR 3.12, 95 % CI 1.30-7.49) spontaneous hyperventilation were associated with DCI. Severe spontaneous hyperventilation (OR 4.52, 95 % CI 1.37-14.89) was also significantly associated with poor discharge outcome in multivariable logistic regression analysis. Spontaneous hyperventilation is common in SAH and is associated with DCI and poor neurological outcome.

The South African English Smartphone Digits-in-Noise Hearing Test: Effect of Age, Hearing Loss, and Speaking Competence.

PubMed

Potgieter, Jenni-Marí; Swanepoel, De Wet; Myburgh, Hermanus Carel; Smits, Cas

2017-11-20

This study determined the effect of hearing loss and English-speaking competency on the South African English digits-in-noise hearing test to evaluate its suitability for use across native (N) and non-native (NN) speakers. A prospective cross-sectional cohort study of N and NN English adults with and without sensorineural hearing loss compared pure-tone air conduction thresholds to the speech reception threshold (SRT) recorded with the smartphone digits-in-noise hearing test. A rating scale was used for NN English listeners' self-reported competence in speaking English. This study consisted of 454 adult listeners (164 male, 290 female; range 16 to 90 years), of whom 337 listeners had a best ear four-frequency pure-tone average (4FPTA; 0.5, 1, 2, and 4 kHz) of ≤25 dB HL. A linear regression model identified three predictors of the digits-in-noise SRT, namely, 4FPTA, age, and self-reported English-speaking competence. The NN group with poor self-reported English-speaking competence (≤5/10) performed significantly (p < 0.01) poorer than the N and NN (≥6/10) groups on the digits-in-noise test. Screening characteristics of the test improved with separate cutoff values depending on English-speaking competence for the N and NN groups (≥6/10) and NN group alone (≤5/10). Logistic regression models, which include age in the analysis, showed a further improvement in sensitivity and specificity for both groups (area under the receiver operating characteristic curve, 0.962 and 0.903, respectively). Self-reported English-speaking competence had a significant influence on the SRT obtained with the smartphone digits-in-noise test. A logistic regression approach considering SRT, self-reported English-speaking competence, and age as predictors of best ear 4FPTA >25 dB HL showed that the test can be used as an accurate hearing screening tool for N and NN English speakers. The smartphone digits-in-noise test, therefore, allows testing in a multilingual population familiar with English digits using dynamic cutoff values that can be chosen according to self-reported English-speaking competence and age.

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

USGS Publications Warehouse

Staley, Dennis M.; Negri, Jacquelyn A.; Kean, Jason W.; Laber, Jayme L.; Tillery, Anne C.; Youberg, Ann M.

2016-06-30

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can generate dangerous flash floods and debris flows. To reduce public exposure to hazard, the U.S. Geological Survey produces post-fire debris-flow hazard assessments for select fires in the western United States. We use publicly available geospatial data describing basin morphology, burn severity, soil properties, and rainfall characteristics to estimate the statistical likelihood that debris flows will occur in response to a storm of a given rainfall intensity. Using an empirical database and refined geospatial analysis methods, we defined new equations for the prediction of debris-flow likelihood using logistic regression methods. We showed that the new logistic regression model outperformed previous models used to predict debris-flow likelihood.

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.

PubMed

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

2013-06-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. Copyright © 2013 Elsevier Inc. All rights reserved.

A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

Physical Activity, Body Size, Intentional Weight Loss and Breast Cancer Risk: Fellowship

DTIC Science & Technology

2000-10-01

unconditional logistic regression and were adjusted for physical activity at other time periods, age, body mass index , smoking status, postmenopausal hormone use ...This variable was used to evaluate tests for trend within the ’any vigorous activity’ group. Body mass index (BMI) was computed using recent weight... used to evaluate the relation of diabetes to the risk of endometrial cancer on the basis of body mass index (BMI). Cases (n = 723) were identified

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...

Deep cerebral microbleeds are negatively associated with HDL-C in elderly first-time ischemic stroke patients.

PubMed

Igase, Michiya; Kohara, Katsuhiko; Igase, Keiji; Yamashita, Shiro; Fujisawa, Mutsuo; Katagi, Ryosuke; Miki, Tetsuro

2013-02-15

Cerebral microbleeds (CMBs) detected on T2*-weighted MRI gradient-echo have been associated with increased risk of cerebral infarction. We evaluated risk factors for these lesions in a cohort of first-time ischemic stroke patients. Presence of CMBs in consecutive first-time ischemic stroke patients was evaluated. The location of CMBs was classified by cerebral region as strictly lobar (lobar CMBs) and deep or infratentorial (deep CMBs). Logistic regression analysis was performed to determine the contribution of lipid profile to the presence of CMBs. One hundred and sixteen patients with a mean age of 70±10years were recruited. CMBs were present in 74 patients. The deep CMBs group had significantly lower HDL-C levels than those without CMBs. In univariable analysis, advanced periventricular hyperintensity grade (PVH>2) and decreased HDL-C were significantly associated with the deep but not the lobar CMB group. On logistic regression analysis, HDL-C (beta=-0.06, p=0.002) and PVH grade >2 (beta=3.40, p=0.005) were independent determinants of deep CMBs. Low HDL-C may be a risk factor of deep CMBs, including advanced PVH status, in elderly patients with acute ischemic stroke. Management of HDL-C levels might be a therapeutic target for the prevention of recurrence of stroke. Copyright © 2012 Elsevier B.V. All rights reserved.

Impact of an Early Decrease in Systolic Blood Pressure on The Risk of Contrast-Induced Nephropathy after Percutaneous Coronary Intervention.

PubMed

Li, Hualong; Huang, Shuijin; He, Yiting; Liu, Yong; Liu, Yuanhui; Chen, Jiyan; Zhou, Yingling; Tan, Ning; Duan, Chongyang; Chen, Pingyan

2016-02-01

The early postprocedural period was thought to be the rush hour of contrast media excretion, causing rapid and prolonged renal hypoperfusion, which was the critical time window for contrast-induced nephropathy (CIN). 349 consecutive patients were enrolled into the study. The relation between an early postprocedural decrease in systolic blood pressure (SBP) and the risk of CIN was assessed using multivariate logistic regression. A postprocedural decrease in SBP was observed in 63% of patients and CIN developed in 28 (8.0%) patients. The CIN group had a lower postprocedural SBP (114.5±13.5 vs. 123.7±15.6mmHg, P=0.003) and a greater postprocedural decrease in SBP (16.2±19.1 vs. 5.9±18.7mmHg, P=0.005) than the no-CIN group. ROC analysis revealed that the optimum cutoff value for the SBP decrease in detecting CIN was >10mmHg (sensitivity 60.7%, specificity 59.5%, AUC=0.66). Multivariate logistic regression analysis found that a postprocedural decrease in SBP >10mmHg was a significant independent predictor of CIN (OR 2.368, 95%CI: 1.043-5.379, P=0.039), after adjustment for other risk factors. An early moderate postprocedural decrease in SBP may increase the risk of CIN in patients undergoing PCI. Copyright © 2015. Published by Elsevier B.V.