scholarly journals PEMODELAN RISIKO PENYAKIT PNEUMONIA PADA BALITA DI PROVINSI JAWA TIMUR DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION

2015 ◽  
Vol 4 (2) ◽  
pp. 31
Author(s):  
EVI NOVIYANTARI FATIMAH ◽  
I KOMANG GDE SUKARSA ◽  
MADE SUSILAWATI
Keyword(s):  
Logistic Regression ◽  
Spatial Data ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Geographic Location ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Spatial Factor ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

This research is aim to determine the comparison of logistic regression models and models Geographically Weighted Logistic Regression and the factors that significantly affect the risk of pneumonia in toddlers in East Java Province. Logistic regression is a statistical analysis that is used to describe the response variable is categorical with the independent variables are categorical or continuous. The main problem of this method if  it’s applied in data that is affected of geographic location or spatial data. One of many method to solve the spatial data is Geographically Weighted Logistic Regression (GWLR). GWLR is a statistical method for analyze the data to account for spatial factor. The results showed that there are no significant differences between the logistic regression model with GWLR model. Factors that significantly affect the risk of pneumonia in toddlers in East Java Province is the percentage of low birth weight, the percentage of  toddlers who get measles immunization, the percentage of toddlers who get vitamin A, and the percentage of toddlers who get DPT+HB immunization.

Comparing the application of logistic and geographically weighted logistic regression models for Fujian forest fire forecasting

2017 ◽  
Vol 37 (12) ◽  
Author(s):  
梁慧玲 LIANG Huiling ◽  
王文辉 WANG Wenhui ◽  
郭福涛 GUO Futao ◽  
林芳芳 LIN Fangfang ◽  
林玉蕊 LIN Yurui
Keyword(s):  
Logistic Regression ◽  
Forest Fire ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

scholarly journals Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Daiquan Xiao ◽  
Xuecai Xu ◽  
Li Duan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Injury Severity ◽  
Temporal Analysis ◽  
Human Beings ◽  
Temporal Perspective ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials.

scholarly journals Sonographic Cervical Shortening after Labor Induction is a Predictor of Vaginal Delivery

2016 ◽  
Vol 38 (12) ◽  
pp. 585-588 ◽  
Author(s):  
Ugo Indraccolo ◽  
Gennaro Scutiero ◽  
Pantaleo Greco
Keyword(s):  
Logistic Regression ◽  
Vaginal Delivery ◽  
Regression Models ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Cervical Length ◽  
Labor Induction ◽  
Logistic Regression Models ◽  
Independent Variables ◽  
Sonographic Evaluation

Objective Analyzing if the sonographic evaluation of the cervix (cervical shortening) is a prognostic marker for vaginal delivery. Methods Women who underwent labor induction by using dinoprostone were enrolled. Before the induction and three hours after it, the cervical length was measured by ultrasonography to obtain the cervical shortening. The cervical shortening was introduced in logistic regression models among independent variables and for calculating receiver operating characteristic (ROC) curves. Results Each centimeter in the cervical shortening increases the odds of vaginal delivery in 24.4% within 6 hours; in 16.1% within 24 hours; and in 10.5% within 48 hours. The best predictions for vaginal delivery are achieved for births within 6 and 24 hours, while the cervical shortening poorly predicts vaginal delivery within 48 hours. Conclusion The greater the cervical shortening 3 hours after labor induction, the higher the likelihood of vaginal delivery within 6, 24 and 48 hours.

scholarly journals Aplicación de modelos de regresión logística en metodología observacional: modalidades de competición en la iniciación al fútbol.

2015 ◽  
Vol 32 (1) ◽  
pp. 288 ◽  
Author(s):  
Daniel Lapresa ◽  
Javier Arana ◽  
M.Teresa Anguera ◽  
J.Ignacio Pérez-Castellanos ◽  
Mario Amatria
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Multiple Logistic Regression ◽  
Observational Methodology ◽  
Logistic Regression Models ◽  
Game Format ◽  
Simple Logistic

This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.

scholarly journals Directly and Simultaneously Expressing Absolute and Relative Treatment Effects in Medical Data Models and Applications

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1517
Author(s):  
Hao Yang Teng ◽  
Zhengjun Zhang
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Treatment Effects ◽  
Real Data ◽  
Medical Data ◽  
Severe Problem ◽  
New Model ◽  
Logistic Regression Models

Logistic regression is widely used in the analysis of medical data with binary outcomes to study treatment effects through (absolute) treatment effect parameters in the models. However, the indicative parameters of relative treatment effects are not introduced in logistic regression models, which can be a severe problem in efficiently modeling treatment effects and lead to the wrong conclusions with regard to treatment effects. This paper introduces a new enhanced logistic regression model that offers a new way of studying treatment effects by measuring the relative changes in the treatment effects and also incorporates the way in which logistic regression models the treatment effects. The new model, called the Absolute and Relative Treatment Effects (AbRelaTEs) model, is viewed as a generalization of logistic regression and an enhanced model with increased flexibility, interpretability, and applicability in real data applications than the logistic regression. The AbRelaTEs model is capable of modeling significant treatment effects via an absolute or relative or both ways. The new model can be easily implemented using statistical software, with the logistic regression model being treated as a special case. As a result, the classical logistic regression models can be replaced by the AbRelaTEs model to gain greater applicability and have a new benchmark model for more efficiently studying treatment effects in clinical trials, economic developments, and many applied areas. Moreover, the estimators of the coefficients are consistent and asymptotically normal under regularity conditions. In both simulation and real data applications, the model provides both significant and more meaningful results.

scholarly journals Use of plasma lactate level to predict 28-day mortality in non-elderly and elderly sepsis patients based on the MIMIC-III database

2020 ◽  
Author(s):  
Yihua Dong ◽  
Xiaoyang Miao ◽  
Yufeng Hu ◽  
Yueyue Huang ◽  
Jie Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Elderly Patients ◽  
Lactate Level ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Plasma Lactate ◽  
Logistic Regression Models ◽  
Lactate Levels ◽  
Plasma Lactate Level

Abstract Purpose: We co mpared the use of lactate level for predicting 28-day mortality in non-elderly (<65 years) and elderly (≥65 years) sepsis patients who were admitted to an intensive care unit (ICU). A multivariate logistic regression model was established to predict 28-day mortality for each group. Methods: This retrospective study used the Medical Information Mart for Intensive Care Ⅲ, a publicly available database of ICUs. Eligible sepsis patients were at least 18 years-old, hospitalized for at least 24 h, and had lactate levels measured in the ICU. Univariate logistic regression analysis and step-wise multivariable logistic regression models were used to identify factors associated with 28-day mortality. Results: The 28-day mortality was 30.9% among the 2482 patients, and was significantly greater in elderly than non-elderly patients. Within each age group, the lactate level was greater for non-survivors than survivors. Among non-survivors, the lactate level was significantly higher for the non-elderly than the elderly. Adjusted logistic regression analysis showed that non-elderly patients with lactate levels of 2.0–4.0 mmol/L and above 4.0 mmol/L had greater risk of death than those with normal lactate levels. For all patients, the stepwise logistic regression model had an area under the receiver operating curve (AUROC) of 0.752; for non-elderly patients, the model had an AUROC of 0.793; for elderly patients, the model had an AUROC of 0.711. The Hosmer-Lemeshow test indicated acceptable goodness-of-fit for each group (P=0.206, P=0.646, and P= 0.482, respectively). Conclusion: In our population of sepsis patients, the lactate level was about 0.9 mmol/L lower in elderly non-survivors than non-elderly survivors. A plasma lactate level above 2.0 mmol/L was an independent risk factor for death at 28-days among non-elderly patients. Our logistic regression models effectively predicted 28-day mortality of sepsis patients in different age groups.

scholarly journals Prediction of independence in bowel function after spinal cord injury: validation of a logistic regression model

Spinal Cord ◽  
2020 ◽  
Author(s):  
Omar Khan ◽  
Jetan H. Badhiwala ◽  
Michael G. Fehlings
Keyword(s):  
Spinal Cord ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Clinical Utility ◽  
Logistic Regression Model ◽  
Bowel Function ◽  
Motor Score ◽  
Logistic Regression Models ◽  
Cord Injury

Abstract Study design Retrospective analysis of prospectively collected data. Objectives Recently, logistic regression models were developed to predict independence in bowel function 1 year after spinal cord injury (SCI) on a multicenter European SCI (EMSCI) dataset. Here, we evaluated the external validity of these models against a prospectively accrued North American SCI dataset. Setting Twenty-five SCI centers in the United States and Canada. Methods Two logistic regression models developed by the EMSCI group were applied to data for 277 patients derived from three prospective multicenter SCI studies based in North America. External validation was evaluated for both models by assessing their discrimination, calibration, and clinical utility. Discrimination and calibration were assessed using ROC curves and calibration curves, respectively, while clinical utility was assessed using decision curve analysis. Results The simplified logistic regression model, which used baseline total motor score as the predictor, demonstrated the best performance, with an area under the ROC curve of 0.869 (95% confidence interval: 0.826–0.911), a sensitivity of 75.5%, and a specificity of 88.5%. Moreover, the model was well calibrated across the full range of observed probabilities and displayed superior clinical benefit on the decision curve. Conclusions A logistic regression model using baseline total motor score as a predictor of independent bowel function 1 year after SCI was successfully validated against an external dataset. These findings provide evidence supporting the use of this model to enhance the care for individuals with SCI.

scholarly journals A-140 Single-Item Screening for Anxiety and Depression using the Neurobehavioral Symptom Inventory

2020 ◽  
Vol 35 (6) ◽  
pp. 933-933
Author(s):  
Rolin S ◽  
Kitchen Andren K ◽  
Mullen C ◽  
Kurniadi N ◽  
Davis J
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Anxiety And Depression ◽  
Average Score ◽  
Characteristic Analysis ◽  
Logistic Regression Models ◽  
Symptom Inventory

Abstract Objective Previous research in a Veterans Affairs sample proposed using single items on the Neurobehavioral Symptom Inventory (NSI) to screen for anxiety (item 19) and depression (item 20). This study examined the approach in an outpatient physical medicine and rehabilitation sample. Method Participants (N = 84) underwent outpatient neuropsychological evaluation using the NSI, BDI-II, GAD-7, MMPI-2-RF, and Memory Complaints Inventory (MCI) among other measures. Anxiety and depression were psychometrically determined via cutoffs on the GAD-7 (&gt;4) and MMPI-2-RF ANX (&gt;64 T), and BDI-II (&gt;13) and MMPI-2-RF RC2 (&gt;64 T), respectively. Analyses included receiver operating characteristic analysis (ROC) and logistic regression. Logistic regression models used dichotomous anxiety and depression as outcomes and relevant NSI items and MCI average score as predictors. Results ROC analysis using NSI items to classify cases showed area under the curve (AUC) values of .77 for anxiety and .85 for depression. The logistic regression model predicting anxiety correctly classified 80% of cases with AUC of .86. The logistic regression model predicting depression correctly classified 79% of cases with AUC of .88. Conclusion Findings support the utility of NSI anxiety and depression items as screening measures in a rehabilitation population. Consideration of symptom validity via the MCI improved classification accuracy of the regression models. The approach may be useful in other clinical settings for quick assessment of psychological issues warranting further evaluation.

Bootstrap internal validation command for predictive logistic regression models

2021 ◽  
Vol 21 (2) ◽  
pp. 498-509
Author(s):  
B. M. Fernandez-Felix ◽  
E. García-Esquinas ◽  
A. Muriel ◽  
A. Royuela ◽  
J. Zamora
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Predictive Model ◽  
Performance Measures ◽  
Predictive Models ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Model Performance ◽  
Internal Validation ◽  
Logistic Regression Models

Overfitting is a common problem in the development of predictive models. It leads to an optimistic estimation of apparent model performance. Internal validation using bootstrapping techniques allows one to quantify the optimism of a predictive model and provide a more realistic estimate of its performance measures. Our objective is to build an easy-to-use command, bsvalidation, aimed to perform a bootstrap internal validation of a logistic regression model.

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