Lung Cancer Study with Tobit Regression Analysis: Sivas Case

Åžebnem Zorlutuna (), Necati Alp Erä°llä° () and Birsen YÃœCEL ()
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Åžebnem Zorlutuna: Cumhuriyet Ãœniversitesi
Necati Alp Erä°llä°: Cumhuriyet Ãœniversitesi
Birsen YÃœCEL: Cumhuriyet Ãœniversitesi

Eurasian Eononometrics, Statistics and Emprical Economics Journal, 2016, vol. 3, issue 3, 13-22

Abstract: : Tobit regression models are the limited dependent variable models in a statement which is widely used and is defined as a non-parametric alternative for the method of least squares regression. Model is known as knowledge of the dependent variable models for some samples containing censored observations. Some experimental process, cannot be achieved for all values of the dependent variable. Dependent variables for these models are called limited dependent variable which value limited by ordered models. In this study Tobit model is used for the analysis, which widely used in the statement in limited dependent variable models. Data taken from Sivas Cumhuriyet University Faculty of Medicine Research and Application Hospital Oncology Center, consists 535 patients who have lung cancer. Tobit regression analysis was applied in the measurement of lung cancer patients (the lifetime) was tried to measure the effect of certain arguments. Using right and left censored models, cancers that are thought to trigger the patient's age, stage of disease, determining the variables affecting the disease status variables , data infrastructure has been created for the disease. Tobit regression results when the dependent variable phase of the patient's disease, the patient's gender, patient's condition , the pathological consequences of the disease was found to be statistically significant variables. The sex of the patient, while a positive effect on the stage of disease, condition and pathological condition of the patients were found to negative influences.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eas:econst:v:3:y:2016:i:3:p:13-22

DOI: 10.17740/eas.stat.2016�V3�02

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