Cox Regression and Parametric Models: Comparison of How They Determine Factors Influencing Survival of Patients with Non-Small Cell Lung Carcinoma

Khaksar, Elahe; Askarishahi, Mohsen; Hekmatimoghaddam, Seyedhossein; Vahedian_Ardakani, Hassanali

doi:10.22034/APJCP.2017.18.12.3389

Cox Regression and Parametric Models: Comparison of How They Determine Factors Influencing Survival of Patients with Non-Small Cell Lung Carcinoma

Document Type : Research Articles

Authors

¹ Department of Biostatistics and Epidemiology, School of Health, Shahid Sadoughi University of Medical Sciences, Safayeh, Yazd, Iran.

² Department of Laboratory Sciences, School of Paramedicine, Shahid Sadoughi University of Medical Sciences, Safayeh, Yazd, Iran.

³ Department of Internal Medicine, School of Medicine, Shahid Sadoughi University of Medical Sciences, Safayeh, Yazd, Iran.

10.22034/APJCP.2017.18.12.3389

Abstract

Background and objectives: The present study of survival rate of patients with non-small cell carcinoma (NSCLC) compared the efficiency of Cox semi-parametric vs. parametric models in determination of influencing factors. Methods: In this retrospective cohort study, data were gathered from 190 patients with a confirmed diagnosis of NSCLC referred to Shahid Sadoughi and Shohadaye Kargar Hospitals in Yazd, Iran during 2005 to 2014. To identify and compare factors influencing the survival rate, a Cox semi-parametric model was fitted to the data. Data analysis was performed using the R software version R3.3.1, and the significance level was set at 0.05. Results: The average age was 64.5 years. About 40% of patients had stage 4 disease. The median survival was 8 months. After comparing the models, the more efficient was the log-normal distribution (AIC=889.3829), with which disease stage, type of therapy, and age were significant factors. Among the different types of therapy, chemotherapy and radiotherapy yielded higher survival rates, and increased age was associated with lower survival. Conclusion: The most efficient model was a log-normal model. Implementation of optimal therapies at early stages can improve the survival of patients.

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