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KMID : 1044620190520020140
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Journal of Preventive Medicine and Public Health
2019 Volume.52 No. 2 p.140 ~ p.144
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Estimating the Survival of Patients With Lung Cancer: What Is the Best Statistical Model?
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Abedi Siavosh
Janbabaei Ghasem
Afshari Mahdi
Moosazadeh Mahmood
Alashti Masoumeh Rashidi
Hedayatizadeh-Omran Akbar
Alizadeh-Navaei Reza
Abedini Ehsan
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Abstract
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Objectives: Investigating the survival of patients with cancer is vitally necessary for controlling the disease and for assessing treatment methods. This study aimed to compare various statistical models of survival and to determine the survival rate and its related factors among patients suffering from lung cancer.
Method: In this retrospective cohort, the cumulative survival rate, median survival time, and factors associated with the survival of lung cancer patients were estimated using Cox, Weibull, exponential, and Gompertz regression models. Kaplan-Meier tables and the log-rank test were also used to analyze the survival of patients in different subgroups.
Results: Of 102 patients with lung cancer, 74.5% were male. During the follow-up period, 80.4% died. The incidence rate of death among patients was estimated as 3.9 (95% confidence [CI], 3.1 to 4.8) per 100 person-months. The 5-year survival rate for all patients, males, females, patients with non-small cell lung carcinoma (NSCLC), and patients with small cell lung carcinoma (SCLC) was 17%, 13%, 29%, 21%, and 0%, respectively. The median survival time for all patients, males, females, those with NSCLC, and those with SCLC was 12.7 months, 12.0 months, 16.0 months, 16.0 months, and 6.0 months, respectively. Multivariate analyses indicated that the hazard ratios (95% CIs) for male sex, age, and SCLC were 0.56 (0.33 to 0.93), 1.03 (1.01 to 1.05), and 2.91 (1.71 to 4.95), respectively.
Conclusions: Our results showed that the exponential model was the most precise. This model identified age, sex, and type of cancer as factors that predicted survival in patients with lung cancer.
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KEYWORD
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Survival, Lung neoplasms, Pulmonary cancer, Statistical models, Iran
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