Repository of Research and Investigative Information

Repository of Research and Investigative Information

Zabol University of Medical Sciences

Estimating the Survival of Patients With Lung Cancer: What Is the Best Statistical Model?

(2019) Estimating the Survival of Patients With Lung Cancer: What Is the Best Statistical Model? Journal of preventive medicine and public health = Yebang Uihakhoe chi. pp. 140-144. ISSN 2233-4521 (Electronic) 1975-8375 (Linking)

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Official URL: https://www.ncbi.nlm.nih.gov/pubmed/30971081

Abstract

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. METHODS: 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.

Item Type: Article
Keywords: Adult Age Factors Aged Aged, 80 and over Cancer Survivors/*statistics & numerical data Female Humans Iran/epidemiology Kaplan-Meier Estimate Lung Neoplasms/*mortality Male Middle Aged Models, Statistical Proportional Hazards Models Regression Analysis Retrospective Studies Risk Factors Sex Factors Survival Analysis Time Factors Iran Lung neoplasms Pulmonary cancer Statistical models Survival
Divisions:
Page Range: pp. 140-144
Journal or Publication Title: Journal of preventive medicine and public health = Yebang Uihakhoe chi
Volume: 52
Number: 2
Identification Number: 10.3961/jpmph.17.090
ISSN: 2233-4521 (Electronic) 1975-8375 (Linking)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.zbmu.ac.ir/id/eprint/3999

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