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 Statistical Models to Analysis Survival Time of Lung Cancer Patients in the Gaza Strip

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dc.contributor.author Saleh Y. AL-Diqs
dc.date.accessioned 6/12/2020
dc.date.accessioned 6/12/2020
dc.date.accessioned 2021-01-09T15:40:45Z
dc.date.available 6/12/2020
dc.date.available 2021-01-09T15:40:45Z
dc.date.issued 6/23/2014
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/2440
dc.description.abstract Cancer is a global disease and represents one of the biggest health problems as it has one of the highest prevalence rates and the highest cost of educational programs required on preventive measures, early detection and access to rehabilitation. Lung cancer is the most common causes of cancer mortality in the Gaza Strip. The aim of this thesis is to provide a model that is appropriate to predict survival time of lung cancer patients in the Gaza strip, and to identify risk factors on lung cancer mortality. Data on 181 patients with lung cancer was collected from the Cancer Registry in Shifa hospital - Gaza in the period 2005–2010. The patients had been followed up for a period of 6 years and the data involve some variables such as gender, age at diagnosis, residence address, smoking status and tumor grade. Exponential proportional hazards and Weibull proportional hazards regression were applied as parametric models with Cox regression and Akaike Information Criterion (AIC) was used to compare the efficiency of the models. Hazard ratio was used to interpret the risk of death to explore factors affecting the survival of patients. Multivariable analysis according to parametric and semi - parametric models showed that the smoking status and tumor grade of cancer increase the risk of death from cancer significantly. The study concluded that the probability of death by lung cancer for grade 2 patients is more than those at grade 1. For smoking patients, the probability of death is more than that of nonsmokers. Based on AIC scores, the Weibull parametric proportional hazard model seems more appropriate for our data set, and we propose that the model should be used as a statistical model for the survival analysis of patients with lung cancer. en_US
dc.language.iso en_US en_US
dc.publisher Batch2 en_US
dc.title  Statistical Models to Analysis Survival Time of Lung Cancer Patients in the Gaza Strip en_US
dc.type Thesis en_US


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