scholarly journals Effect of using bias-corrected estimators in logistic regression model in small samples: prostate-specific antigen (PSA) data

2006 ◽  
Vol 5 ◽  
pp. 100-107 ◽  
Author(s):  
M.A. Matin
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Logistic Regression Model ◽  
Specific Antigen ◽  
Small Samples

Evaluation of a Multivariate Prostate-Specific Antigen and Percentage of Free Prostate-Specific Antigen Logistic Regression Model in the Diagnosis of Prostate Cancer

Tumor Biology ◽  
1999 ◽  
Vol 20 (6) ◽  
pp. 312-318 ◽  
Author(s):  
Xavier Filella ◽  
Juan Alcover ◽  
Llorenç Quintó ◽  
Rafael Molina ◽  
Xavier Bosch-Capblanch ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Logistic Regression Model ◽  
Specific Antigen

scholarly journals Prospects for the application of a new logistic regression model for the differential diagnosis of prostate cancer

2021 ◽  
Vol 99 (2) ◽  
pp. 98-102
Author(s):  
S. V. Ponkratov ◽  
I. B. Oleksjuk ◽  
K. L. Kozlov ◽  
A. V. Oleksjuk
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Differential Diagnosis ◽  
Regression Model ◽  
Prostate Specific Antigen ◽  
Logistic Regression Model ◽  
Specific Antigen ◽  
Transrectal Ultrasound ◽  
Modern Medicine ◽  
Ultrasound Investigation

The differential diagnostic of prostate cancer is the actual task of modern medicine. The existing methods lack accuracy and specificity. It’s the reason of hyper- or hypo-diagnostic of this disease. We developed and tested the new logistic regression model for diagnostic of prostate cancer in men of various age. The model includes age, the volume of prostate, concentration of prostate specific antigen (PCA), 2-pro-PCA in the blood, the presence of the concretion revealed during digital rectal investigation of prostate and hypoechogenic area in transrectal ultrasound investigation. The model was tested in 114 patients. It has shown the higher accuracy and specificity of the new regression model in comparison to other methods of differential diagnostic of prostate cancer.

Comparison of robust logistic regression estimators for variables with generalized extreme value distributions

2021 ◽  
Vol 16 (3) ◽  
pp. 177-187
Author(s):  
Şaban Kızılarslan ◽  
Ceren Camkıran
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Extreme Values ◽  
Real Data ◽  
Extreme Value ◽  
Small Samples ◽  
Robust Estimators ◽  
Generalized Extreme Value ◽  
Explanatory Variables

The aim of this study is to compare the performance of robust estimators in the presence of explanatory variables with Generalized Extreme Value (GEV) distributions in the logistic regression model. Existence of extreme values in the logistic regression model negatively affects the bias and effectiveness of classical Maximum Likelihood (ML) estimators. For this reason, robust estimators that are less sensitive to extreme values have been developed. Random variables with extreme values may be fit in one of specific distributions. In study, the GEV distribution family was examined and five robust estimators were compared for the Fréchet, Gumbel and Weibull distributions. To the simulation results, the CUBIF estimator is prominent according to both bias and efficiency criteria for small samples. In medium and large samples, while the MALLOWS estimator has the minimum bias, the CUBIF estimator has the best efficiency. The same results apply for different contamination ratios and different scale parameter values of the distributions. Simulation findings were supported by a meteorological real data application.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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