A discrete time logistic regression model for analyzing censored survival data

1994 ◽  
Vol 5 (2) ◽  
pp. 145-157 ◽  
Author(s):  
A. Maul
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Discrete Time ◽  
Survival Data ◽  
Logistic Regression Model ◽  
Censored Survival Data

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

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= θ0+θ1xi1+...+θixij+σԑ. 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+0.639037ԑi.

Discrete choice and survival models in employee turnover analysis

2018 ◽  
Vol 40 (2) ◽  
pp. 381-395 ◽  
Author(s):  
Rafa Madariaga ◽  
Ramon Oller ◽  
Joan Carles Martori
Keyword(s):  
Logistic Regression ◽  
Survival Analysis ◽  
Regression Model ◽  
Discrete Choice ◽  
Survival Data ◽  
Employee Turnover ◽  
Logistic Regression Model ◽  
Data Set ◽  
Content Type ◽  
Set Up

Purpose The purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover. Design/methodology/approach The authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated. Findings When no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study). Originality/value This paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

scholarly journals Sensitivity Analysis to Select the Most Influential Risk Factors in a Logistic Regression Model

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Jassim N. Hussain
Keyword(s):  
Risk Factors ◽  
Sensitivity Analysis ◽  
Logistic Regression ◽  
Regression Model ◽  
Survival Data ◽  
Logistic Regression Model ◽  
Global Sensitivity Analysis ◽  
Selection Methods ◽  
Tuning Parameters ◽  
And Control

The traditional variable selection methods for survival data depend on iteration procedures, and control of this process assumes tuning parameters that are problematic and time consuming, especially if the models are complex and have a large number of risk factors. In this paper, we propose a new method based on the global sensitivity analysis (GSA) to select the most influential risk factors. This contributes to simplification of the logistic regression model by excluding the irrelevant risk factors, thus eliminating the need to fit and evaluate a large number of models. Data from medical trials are suggested as a way to test the efficiency and capability of this method and as a way to simplify the model. This leads to construction of an appropriate model. The proposed method ranks the risk factors according to their importance.

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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