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

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.

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

Journal of Human Kinetics ◽

10.1515/hukin-2016-0047 ◽

2016 ◽

Vol 54 (1) ◽

pp. 163-171 ◽

Cited By ~ 8

Author(s):

Mario Amatria ◽

Daniel Lapresa ◽

Javier Arana ◽

M. Teresa Anguera ◽

Belén Garzón

Keyword(s):

Logistic Regression ◽

Regression Models ◽

Age Groups ◽

Soccer Players ◽

Learning Needs ◽

Multiple Logistic Regression ◽

Logistic Regression Models ◽

Game Format ◽

The Creation ◽

Simple Logistic

AbstractSmall-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).

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

Entropy ◽

10.3390/e23111517 ◽

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.

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

Spinal Cord ◽

10.1038/s41393-020-00551-y ◽

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.

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

Archives of Clinical Neuropsychology ◽

10.1093/arclin/acaa068.140 ◽

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 (>4) and MMPI-2-RF ANX (>64 T), and BDI-II (>13) and MMPI-2-RF RC2 (>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.

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Bootstrap internal validation command for predictive logistic regression models

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x211025836 ◽

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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Determinants of Contraceptive Practice in Oman by Using Multilevel Modeling

Sultan Qaboos University Journal for Science [SQUJS] ◽

10.24200/squjs.vol20iss1pp83-92 ◽

2015 ◽

Vol 20 (1) ◽

pp. 83 ◽

Cited By ~ 1

Author(s):

Moza S. Al-Balushi ◽

Mohammed S. Ahmed ◽

M. Mazharul Islam

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Regional Variation ◽

Regression Models ◽

Logistic Regression Model ◽

Contraceptive Use ◽

Multilevel Logistic Regression ◽

Logistic Regression Models ◽

Living Children ◽

Multilevel Logistic Regression Model

In this paper, multilevel logistic regression models are developed for examining the hierarchical effects of contraceptive use and its selected determinants in Oman using the 2008 Oman National Reproductive Health Survey (ONRHS). Comparison between single level and multilevel logistic regression models has been made to examine the plausibility of multilevel effects of contraceptive use. From the multilevel logistic regression model analysis, it was found that there is real multilevel variation among contraceptive users in Oman. The results indicate that a multilevel logistic regression model is the best fit over ordinary multiple logistic regression models. Generally, this study revealed that women’s age, education, number of living children and region of residence are important factors that affect contraceptive use in Oman. The effect of regional variation for age of women, education of women and number of living children further implies that there exists considerable differences in modern contraceptive use among regions, and a model with a random coefficient or slope is more appropriate to explain the regional variation than a model with fixed coefficients or without random effects. The study suggests that researchers should use multilevel models rather than traditional regression methods when their data structure is hierarchal.

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Analisis Survival dengan Model Regresi pada Data Tersensor Berdistribusi Log-Logistik

Jurnal Statistika dan Aplikasinya ◽

10.21009/jsa.02204 ◽

2018 ◽

Vol 2 (2) ◽

pp. 28-35

Author(s):

Gatri Eka Kusumawardhani ◽

Vera Maya Santi ◽

Suyono Suyono

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Survival Data ◽

Regression Models ◽

Logistic Regression Model ◽

Likelihood Method ◽

Censored Survival Data ◽

Logistic Regression Models ◽

Lung Cancer Patients ◽

Time Required

Survival analysis is an analysis used to determine the length of time required by an object in order to survive. That time is sometimes influenced by several factors called independent variables. One way to know relationship is through a regression model. The dependent variable in this regression model is a survival time which is log-logistic distributed. The data used in this study were right censored survival data. Log-logistic regression models for survival data can be expressed by transformation Y=lnT= Î¸0+Î¸1xi1+...+Î¸ixij+ÏƒÔ‘. The parameter of the log-logistic regression models for right censored survival data are estimated with the maximum likelihood method. In this study, the application of log-logistic regression model for survival data is in data of lung cancer patients. Based on the data already performed, best log-logistic regression model is obtained yi=1.92458+0.0242393 xi1+0.639037Ô‘i.

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Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified

10.1101/2021.05.10.443518 ◽

2021 ◽

Author(s):

Takuma Shibahara ◽

Chisa Wada ◽

Yasuho Yamashita ◽

Kazuhiro Fujita ◽

Masamichi Sato ◽

...

Keyword(s):

Breast Cancer ◽

Logistic Regression ◽

Deep Learning ◽

Regression Model ◽

Linear Model ◽

Regression Models ◽

Logistic Regression Model ◽

Learning Approach ◽

Logistic Regression Models ◽

Custom Made

Breast cancer is the most frequently found cancer in women and the one most often subjected to genetic analysis. Nonetheless, it has been causing the largest number of women's cancer-related deaths. PAM50, the intrinsic subtype assay for breast cancer, is beneficial for diagnosis and stratified treatment but does not explain each subtype's mechanism. Nowadays, deep learning can predict the subtypes from genetic information more accurately than conventional statistical methods. However, the previous studies did not directly use deep learning to examine which genes associate with the subtypes. Ours is the first study on a deep-learning approach to reveal the mechanisms embedded in the PAM50-classified subtypes. We developed an explainable deep learning model called a point-wise linear model, which uses a meta-learning approach to generate a custom-made logistic regression model for each sample. Logistic regression is familiar to physicians and medical informatics researchers, and we can use it to analyze which genes are important for subtype prediction. The custom-made logistic regression models generated by the point-wise linear model for each subtype used the specific genes selected in other subtypes compared to the conventional logistic regression model: the overlap ratio is less than twenty percent. And analyzing the point-wise linear model's inner state, we found that the point-wise linear model used genes relevant to the cell cycle-related pathways. The results of this study suggest the potential of our explainable deep learning to play a vital role in cancer treatment.

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Some New Robust Estimators for Circular Logistic Regression Model with Applications on Meteorological and Ecological Data

Mathematical Problems in Engineering ◽

10.1155/2021/9944363 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Shokrya S. Alshqaq ◽

Abdullah A. Ahmadini ◽

Ali H. Abuzaid

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Regression Models ◽

Logistic Regression Model ◽

Likelihood Estimation ◽

Simulation Studies ◽

Robust Estimators ◽

Finite Sample ◽

Ecological Data ◽

Logistic Regression Models

Maximum likelihood estimation ( MLE ) is often used to estimate the parameters of the circular logistic regression model due to its efficiency under a parametric model. However, evidence has shown that the classical MLE extremely affects the parameter estimation in the presence of outliers. This article discusses the effect of outliers on circular logistic regression and extends four robust estimators, namely, Mallows, Schweppe, Bianco and Yohai estimator BY , and weighted BY estimators, to the circular logistic regression model. These estimators have been successfully used in linear logistic regression models for the same purpose. The four proposed robust estimators are compared with the classical MLE through simulation studies. They demonstrate satisfactory finite sample performance in the presence of misclassified errors and leverage points. Meteorological and ecological datasets are analyzed for illustration.

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