scholarly journals Identification of Factors Determining Winning in Men’s and Women’s Beach Volleyball: a Logistical Regression Approach

2021 ◽  
Vol 21 (1) ◽  
pp. 26-35
Author(s):  
Gopal Kumar ◽  
Anshuman Shukla ◽  
Amit Chhoker ◽  
Rohit Kumar Thapa
Keyword(s):  
Odds Ratio ◽  
Goodness Of Fit ◽  
Binary Logistic Regression ◽  
Ratio Test ◽  
Goodness Of Fit Test ◽  
Men And Women ◽  
Regression Approach ◽  
Lemeshow Test ◽  
Calculated Model ◽  
Women's Competition

The purpose of this study was to find the factors responsible for winning in the men’s and women’s beach volleyball championship. Materials and methods. The study sample consisted of a total of 212 matches for men and 214 matches for women of the 2017 & 2019 FIVB Men and Women Beach Volleyball World Championships held at Vienna & Hamburg from 28 July to 6 Aug 2017 and 28 June to 7 July 2019. The matches were played by 192 teams (both men and women combined) consisting of 384 numbers (both men and women combined) of players from different nations. The data were analyzed using Binary Logistic Regression (Forward: LR Method) with the result of the game as the dependent variable and predictor variables as covariates. β, standard error β, Wald’s χ2, odds ratio with 95% confidence interval were calculated. Model evaluation was conducted using the likelihood ratio test, Cox & Snell (R2), and Nagelkerke (R2) tests. The goodness of fit test for the models was conducted using the Hosmer & Lemeshow test. Results. The analysis revealed seven factors related to winning in men’s and women’s competition. While in league rounds, six factors in men’s and seven factors in women’s competition were related to winning. Besides, in knockout rounds, four factors in men’s and six factors in women’s competition were related to winning. Conclusion. The study shows that there is a significant association of important factors with respect to winning a match in an elite beach volleyball championship. The coaches and players can take note of the important factors responsible for winning in the elite beach volleyball championship, with different factors playing an important role in men’s and women’s competition during league and knockout rounds as well.

scholarly journals Pharmacogenetics–Based Preliminary Algorithm to Predict the Incidence of Infection in Patients Receiving Cytotoxic Chemotherapy for Hematological Malignancies: A Discovery Cohort

2021 ◽  
Vol 12 ◽  
Author(s):  
Matias F. Martinez ◽  
Enzo Alveal ◽  
Tomas G. Soto ◽  
Eva I. Bustamante ◽  
Fernanda Ávila ◽  
...  
Keyword(s):  
Inflammatory Response ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Health Resources ◽  
Cytotoxic Chemotherapy ◽  
Goodness Of Fit Test ◽  
Lemeshow Test ◽  
Life Threatening ◽  
Limited Health ◽  
Good Diagnostic Accuracy

Introduction: Infections in hematological cancer patients are common and usually life-threatening; avoiding them could decrease morbidity, mortality, and cost. Genes associated with antineoplastics’ pharmacokinetics or with the immune/inflammatory response could explain variability in infection occurrence.Objective: To build a pharmacogenetic-based algorithm to predict the incidence of infections in patients undergoing cytotoxic chemotherapy.Methods: Prospective cohort study in adult patients receiving cytotoxic chemotherapy to treat leukemia, lymphoma, or myeloma in two hospitals in Santiago, Chile. We constructed the predictive model using logistic regression. We assessed thirteen genetic polymorphisms (including nine pharmacokinetic—related genes and four inflammatory response-related genes) and sociodemographic/clinical variables to be incorporated into the model. The model’s calibration and discrimination were used to compare models; they were assessed by the Hosmer-Lemeshow goodness-of-fit test and area under the ROC curve, respectively, in association with Pseudo-R2.Results: We analyzed 203 chemotherapy cycles in 50 patients (47.8 ± 16.1 years; 56% women), including 13 (26%) with acute lymphoblastic and 12 (24%) with myeloblastic leukemia.Pharmacokinetics-related polymorphisms incorporated into the model were CYP3A4 rs2242480C>T and OAT4 rs11231809T>A. Immune/inflammatory response-related polymorphisms were TLR2 rs4696480T>A and IL-6 rs1800796C>G. Clinical/demographic variables incorporated into the model were chemotherapy type and cycle, diagnosis, days in neutropenia, age, and sex. The Pseudo-R2 was 0.56, the p-value of the Hosmer-Lemeshow test was 0.98, showing good goodness-of-fit, and the area under the ROC curve was 0.93, showing good diagnostic accuracy.Conclusions: Genetics can help to predict infections in patients undergoing chemotherapy. This algorithm should be validated and could be used to save lives, decrease economic costs, and optimize limited health resources.

scholarly journals Binary Logistic Model to Identify the Factors Associated with Households with Bank Accounts in Nepal

2020 ◽  
Vol 2 (2) ◽  
pp. 323-336
Author(s):  
Santosh Kumar Shah
Keyword(s):  
Logistic Regression ◽  
Financial Institutions ◽  
Goodness Of Fit ◽  
Receiver Operating Curve ◽  
P Value ◽  
Ratio Test ◽  
Multiple Logistic Regression ◽  
Goodness Of Fit Test ◽  
Factors Associated ◽  
Bank Accounts

Introduction: Banks play an important role in ensuringthe economicand social stability, and the sustainablegrowth of the economy. The savings and other accounts in financial institutions, including banks, finances, microfinances and cooperatives, enable people to execute important financial functions. Thus, households that have accounts in any of financial institutions can have access to various banking services. Objective: The objective of the study is to identify the factors associated with households having bank accounts in Nepal. Methods: The analysis is based on household data extracted from the dataset of Nepal Demographic and Health Survey, 2016. The dependent variable is dichotomous, as the households with bank accounts and without bank accounts in any formal financial channels. In order to identify the factors associated with households receiving financial services in Nepal, multiple logistic regression models were developed by examining the model adequacy test. Results: The study finds that a total of 66.9% of the households had bank accounts. Several variables were found to be 1% of significance level. The predictive power of the model is found to be 31.2% and multicollinearity among the independent variables was absent. The Hosmer-Lemoshow goodness of fit test revealed that the data were poorly (p-value=0.056) fitted by the model. However, Osius-Rojek goodness of fit test (z=0.11; p-value=0.911), Stukel test (Z=0.683, p-value=0.494), likelihood ratio test (χ2=2770; p-value<0.0001) and area under receiver operating curve (79.8%) revealed that fitted model was good. Conclusion: Multiple logistic regression model revealed that in mountainous and hilly regions, women-headed households have less chances of not having bank accounts compared to the Terai region and men-headed households. The chances of having a bank account in province-2 is even worse than in Karnali and other provinces. The odds of not having bank accounts gradually decreased with the increase in size of agricultural land, wealth index, increase in family size and the number of family members who have completed secondary education.

scholarly journals External validation of simplified out-of-hospital cardiac arrest and cardiac arrest hospital prognosis scores in a Japanese population: A multicentre retrospective cohort study

2020 ◽  
Author(s):  
Keita Shibahashi ◽  
Kazuhiro Sugiyama ◽  
Yusuke Kuwahara ◽  
Takuto Ishida ◽  
Atsushi Sakurai ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Japanese Population ◽  
Goodness Of Fit ◽  
External Validation ◽  
Predictive Score ◽  
Calibration Plot ◽  
Goodness Of Fit Test ◽  
Neurological Outcomes ◽  
Lemeshow Test ◽  
Hospital Cardiac Arrest

Abstract Background Out-of-hospital cardiac arrest (OHCA) is a global medical problem. The newly-developed simplified out-of-hospital cardiac arrest (sOHCA) and cardiac arrest hospital prognosis (sCAHP) scores used for prognostication of patients admitted alive have not been validated externally. This study was, thus, conducted to externally validate sOHCA and sCAHP scores in a Japanese population. Methods Adult patients resuscitated and admitted to hospitals after intrinsic OHCA (n=2,428, age ≥18 years) were selected from a prospectively collected Japanese database (January 2012–March 2013). We validated sOHCA and sCAHP scores with reference to the original ones in predicting 1-month unfavourable neurological outcomes based on discrimination and calibration measures. Discrimination and calibration were assessed using area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test with calibration plot, respectively. Results One-month unfavourable neurological outcome was observed in 82% of patients. Score availability was significantly higher in the simplified scores than in the original ones and was highest in the sCAHP score (76%). The AUCs of simplified scores were not significantly different from those of original ones, whereas the AUC of the sCAHP score was significantly higher than that of the sOHCA score (0.88 vs. 0.81, P <0.001). Goodness-of-fit was poor in the sOHCA score (ν= 8, χ 2 =19.1, Hosmer-Lemeshow test: P =0.014) but not in the sCAHP score (ν= 8, χ 2 =13.5, Hosmer-Lemeshow test: P =0.10). Conclusion Performance of original and simplified OHCA and CAHP scores in predicting neurological outcomes in successfully resuscitated OHCA patients were acceptable. Based on the highest availability, similar discrimination, and good calibration, the sCAHP score was the better candidate for clinical implementation. The validated predictive score can help patients’ families, healthcare providers, and researchers by accurately stratifying patients.

scholarly journals External validation of simplified out-of-hospital cardiac arrest and cardiac arrest hospital prognosis scores in a Japanese population: a multicentre retrospective cohort study

2021 ◽  
pp. emermed-2020-210103
Author(s):  
Keita Shibahashi ◽  
Kazuhiro Sugiyama ◽  
Yusuke Kuwahara ◽  
Takuto Ishida ◽  
Atsushi Sakurai ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Japanese Population ◽  
Goodness Of Fit ◽  
External Validation ◽  
Calibration Plot ◽  
Goodness Of Fit Test ◽  
Neurological Outcomes ◽  
Hospitalised Patients ◽  
Lemeshow Test ◽  
Hospital Cardiac Arrest

BackgroundThe novel simplified out-of-hospital cardiac arrest (sOHCA) and simplified cardiac arrest hospital prognosis (sCAHP) scores used for prognostication of hospitalised patients have not been externally validated. Therefore, this study aimed to externally validate the sOHCA and sCAHP scores in a Japanese population.MethodsWe retrospectively analysed data from a prospectively maintained Japanese database (January 2012 to March 2013). We identified adult patients who had been resuscitated and hospitalised after intrinsic out-of-hospital cardiac arrest (OHCA) (n=2428, age ≥18 years). We validated the sOHCA and sCAHP scores with reference to the original scores in predicting 1-month unfavourable neurological outcomes (cerebral performance categories 3–5) based on the discrimination and calibration measures of area under the receiver operating characteristic curves (AUCs) and a Hosmer-Lemeshow goodness-of-fit test with a calibration plot, respectively.ResultsIn total, 1985/2484 (82%) patients had a 1-month unfavourable neurological outcome. The original OHCA, sOHCA, original cardiac arrest hospital prognosis (CAHP) and sCAHP scores were available for 855/2428 (35%), 1359/2428 (56%), 1130/2428 (47%) and 1834/2428 (76%) patients, respectively. The AUCs of simplified scores did not differ significantly from those of the original scores, whereas the AUC of the sCAHP score was significantly higher than that of the sOHCA score (0.88 vs 0.81, p<0.001). The goodness of fit was poor in the sOHCA score (ν=8, χ2=19.1 and Hosmer-Lemeshow test: p=0.014) but not in the sCAHP score (ν=8, χ2=13.5 and Hosmer-Lemeshow test: p=0.10).ConclusionThe performances of the original and simplified OHCA and CAHP scores in predicting neurological outcomes in successfully resuscitated OHCA patients were acceptable. With the highest availability, similar discrimination and good calibration, the sCAHP score has promising potential for clinical implementation, although further validation studies to evaluate its clinical acceptance are necessary.

A generalized logistic model of individual tree mortality for aspen, white spruce, and lodgepole pine in Alberta mixedwood forests

2001 ◽  
Vol 31 (2) ◽  
pp. 283-291 ◽  
Author(s):  
Xiaohong Yao ◽  
Stephen J Titus ◽  
S Ellen MacDonald
Keyword(s):  
Populus Tremuloides ◽  
Logistic Model ◽  
Goodness Of Fit ◽  
Tree Mortality ◽  
Lodgepole Pine ◽  
White Spruce ◽  
Ratio Test ◽  
Goodness Of Fit Test ◽  
Individual Tree ◽  
Mixedwood Forests

A generalized logistic model of individual tree mortality was developed for trembling aspen (Populus tremuloides Michx.), white spruce (Picea glauca (Moench) Voss), and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) in Alberta boreal mixedwood forests based on an empirical data base of permanent sample plots. The model is suitable for observations from unequal remeasurement intervals. The maximum likelihood estimation was used to fit the model, the likelihood ratio test was combined with our understanding of mortality process to select the important variables, and the Hosmer-Lemeshow goodness-of-fit test was conducted to evaluate the fit. The fitted model predicts the survival probability of an individual tree based on the tree diameter at breast height, annual diameter increment, stand basal area, species composition, and site productivity.

scholarly journals An Analytical EM Algorithm for Sub-Gaussian Vectors

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 945
Author(s):  
Audrius Kabašinskas ◽  
Leonidas Sakalauskas ◽  
Ingrida Vaičiulytė
Keyword(s):  
Em Algorithm ◽  
Likelihood Ratio ◽  
Goodness Of Fit ◽  
Stable Distribution ◽  
Likelihood Estimation ◽  
Estimation Methods ◽  
Normal Vector ◽  
Ratio Test ◽  
Estimation Of Parameters ◽  
Goodness Of Fit Test

The area in which a multivariate α-stable distribution could be applied is vast; however, a lack of parameter estimation methods and theoretical limitations diminish its potential. Traditionally, the maximum likelihood estimation of parameters has been considered using a representation of the multivariate stable vector through a multivariate normal vector and an α-stable subordinator. This paper introduces an analytical expectation maximization (EM) algorithm for the estimation of parameters of symmetric multivariate α-stable random variables. Our numerical results show that the convergence of the proposed algorithm is much faster than that of existing algorithms. Moreover, the likelihood ratio (goodness-of-fit) test for a multivariate α-stable distribution was implemented. Empirical examples with simulated and real world (stocks, AIS and cryptocurrencies) data showed that the likelihood ratio test can be useful for assessing goodness-of-fit.

scholarly journals The development of using nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles

2020 ◽  
Author(s):  
Yang Wang ◽  
Ziru Niu ◽  
Liyuan Tao ◽  
Xiaoying Zheng ◽  
Yifeng Yuan ◽  
...  
Keyword(s):  
Pregnancy Outcomes ◽  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Model Performance ◽  
Goodness Of Fit Test ◽  
Discriminatory Ability ◽  
Female Age ◽  
Lemeshow Test ◽  
Development And Validation ◽  
Good Calibration

Abstract Background: To study which characteristics of a pre-oocyte-retrieval patient can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles. Methods: Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots. Data was collected from the Reproductive Center, Peking University Third Hospital of China. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test and calibration plots.Results: The predictors in the model of ‘no embryo to transfer’ are female age (OR= 1.099, 95% CI=1.003-1.205, P=0.044), duration of infertility(OR= 1.140, 95% CI=1.018-1.276, P=0.024), basal FSH level (OR= 1.205, 95% CI=1.051-1.382, P=0.0084), basal E2 level (OR=1.006, 95% CI=1.001-1.010, P=0.012) and sperm from MESA (OR=7.741, 95% CI=2.905-20.632, P<0.001). Upon assessing predictive ability, the AUC for this model was 0.799 (95% CI: 0.722–0.875, p＜0.001). The Hosmer-Lemeshow test (p=0.721) and calibration curve showed good calibration. The predictors in the cumulative live birth were the number of follicles on the day of hCG administration (OR= 1.088, 95% CI=1.030-1.149, P=0.002) and endometriosis (OR= 0.172, 95% CI=0.035-0.853, P=0.031). The AUC for this model was 0.724 (95% CI: 0.647–0.801, p＜0.001). The Hosmer-Lemeshow test (p=0.562) and calibration curve showed good calibration for the prediction of cumulative live birth. Conclusion: The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, basal FSH and E2 level, the number of follicles with a diameter greater than 10 mm on the day of hCG administration, endometriosis and sperm from microdissection testicular sperm extraction (MESA).

scholarly journals Regression Analysis for Analyzing Life Style Risk Factor of Diabetes in Pakistan

2021 ◽  
pp. 94-103
Author(s):  
Zahid Iqbal ◽  
Qaiser
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Cholesterol Level ◽  
Goodness Of Fit ◽  
Significant Risk ◽  
Binary Logistic Regression ◽  
P Value ◽  
Binary Logistic Regression Model ◽  
Goodness Of Fit Test ◽  
Chi Square

Diabetes is a worldwide metabolic disease. In Pakistan prevalence of diabetes is increasing day by day. This research aims to analyze the risk factors associated with the patient in the Malakand division, KPK Pakistan. The data is collected from four districts of Malakand division District Headquarter Hospital for the period year 2018. The insignificant risk factors are eliminated using the backward Elimination method for the Binary logistic Regression model and for a best-fitted model, the AIC technique is used, while the logistic Coefficient is tested with help of Wald statistic and Hosmer-Lemeshow is performed for the Goodness of fit test. The positive and negative association among risk factors with diabetes is checked with the help of a Chi-square and odds ratio. Based on P-value at 5% level of significance the risk factors Age, cholesterol level, Hypertension, Family History, and Obesity are sensitive risk factors to develop diabetes. The AIC also show same best-fitted model while according to Hosmer- Lemeshow 0.844 indicating a better fit and these risk factors are associated with diabetes for the combine data of Malakand division. In each districts, the significant risk factors that affect to develop diabetes are Age, Cholesterol level and Obesity while according to AIC the best-fitted model is that in which the risk factors Gender and Occupation Status are involved the risk factor obesity show low level of precision based on 95% Confidence Interval and Chi-square statistic shows these factors are associated with diabetes.

scholarly journals Investigating the Behaviors of M2 and RMSEA2 in Fitting a Unidimensional Model to Multidimensional Data

2017 ◽  
Vol 41 (8) ◽  
pp. 632-644
Author(s):  
Jie Xu ◽  
Insu Paek ◽  
Yan Xia
Keyword(s):  
Goodness Of Fit ◽  
Type I Error ◽  
Error Rates ◽  
Multidimensional Data ◽  
Ratio Test ◽  
Type I ◽  
Limited Information ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
Goodness Of Fit Tests

It has been widely known that the Type I error rates of goodness-of-fit tests using full information test statistics, such as Pearson’s test statistic χ2 and the likelihood ratio test statistic G2, are problematic when data are sparse. Under such conditions, the limited information goodness-of-fit test statistic M2 is recommended in model fit assessment for models with binary response data. A simulation study was conducted to investigate the power and Type I error rate of M2 in fitting unidimensional models to many different types of multidimensional data. As an additional interest, the behavior of RMSEA2 was also examined, which is the root mean square error approximation (RMSEA) based on M2. Findings from the current study showed that M2 and RMSEA2 are sensitive in detecting the misfits due to varying slope parameters, the bifactor structure, and the partially (or completely) simple structure for multidimensional data, but not the misfits due to the within-item multidimensional structures.

scholarly journals A Comparative Analysis of the Anxiety in Offenders of the Law Based on Structural Equational Models

2020 ◽  
Vol 14 (2) ◽  
pp. 65
Author(s):  
Catalina Quintero-López ◽  
Víctor Daniel Gil-Vera ◽  
Alejandra Bustamante-Hernández ◽  
Luis Eduardo De Ángel-Martínez
Keyword(s):  
Antisocial Personality Disorder ◽  
Latent Variable ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Negative Impact ◽  
Antisocial Personality ◽  
Goodness Of Fit Test ◽  
Men And Women ◽  
The Law

Anxiety affects men and women and have a negative impact on their lives. This paper presents two structural equation models (SEM) to evaluate the variables (physiological and cognitive), that most influenced the anxiety in men and women offenders of the law. Was used a representative sample of 60 offenders of the law (30 mens and 30 womens) of the Specialized Attention Center (SAC) &ldquo;Carlos Lleras Restrepo&rdquo; in Medellin, Colombia with diagnosis of Antisocial Personality Disorder (APD). The results of Bartlett&#39;s and KMO tests, indicated that the factorial analysis is adequate, all the constructs are statistically significant. The goodness-of-fit test indicated that the model fits well with the data. This paper concludes that, of the two constructs considered: physiological and cognition, in the men the construct that most influences the latent variable physiological are the &ldquo;Palpitations or tachycardia&rdquo;. The construct that most influences the latent variable cognitive is the &ldquo;a feeling of instability&rdquo;. In the women, the construct that most influences the latent variable physiological is the &ldquo;dizziness or vertigo&rdquo;. The construct that most influences the latent variable cognitive is &ldquo;be afraid&rdquo;.

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