KSPM
Search
- Page Path
- HOME > Search
Brief Report
- Estimating the Survival of Patients With Lung Cancer: What Is the Best Statistical Model?
- Siavosh Abedi, Ghasem Janbabaei, Mahdi Afshari, Mahmood Moosazadeh, Masoumeh Rashidi Alashti, Akbar Hedayatizadeh-Omran, Reza Alizadeh-Navaei, Ehsan Abedini
- J Prev Med Public Health. 2019;52(2):140-144. Published online February 18, 2019
- DOI: https://doi.org/10.3961/jpmph.17.090
- 5,647 View
- 138 Download
- 6 Crossref
-
Abstract
PDF - 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. -
Summary
Citations
Citations to this article as recorded by
- Primary and Acquired Resistance against Immune Check Inhibitors in Non-Small Cell Lung Cancer
Qinying Sun, Xiangzhen Wei, Zhonglin Wang, Yan Zhu, Weiying Zhao, Yuchao Dong
Cancers.2022; 14(14): 3294. CrossRef - Impact of Residential Concentration of PM2.5 Analyzed as Time-Varying Covariate on the Survival Rate of Lung Cancer Patients: A 15-Year Hospital-Based Study in Upper Northern Thailand
Nawapon Nakharutai, Patrinee Traisathit, Natthapat Thongsak, Titaporn Supasri, Pimwarat Srikummoon, Salinee Thumronglaohapun, Phonpat Hemwan, Imjai Chitapanarux
International Journal of Environmental Research and Public Health.2022; 19(8): 4521. CrossRef - Risk factors of inability to live independently in the course of lung cancer
Marek Tradecki, Jolanta Ziółkowska, Roma Roemer-Ślimak, Grzegorz Mazur, Aleksandra Butrym
Postępy Higieny i Medycyny Doświadczalnej.2022; 76(1): 402. CrossRef - Deep learning-based tumor microenvironment segmentation is predictive of tumor mutations and patient survival in non-small-cell lung cancer
Alicja Rączkowska, Iwona Paśnik, Michał Kukiełka, Marcin Nicoś, Magdalena A. Budzinska, Tomasz Kucharczyk, Justyna Szumiło, Paweł Krawczyk, Nicola Crosetto, Ewa Szczurek
BMC Cancer.2022;[Epub] CrossRef - Biology of NSCLC: Interplay between Cancer Cells, Radiation and Tumor Immune Microenvironment
Slavisa Tubin, Mohammad K. Khan, Seema Gupta, Branislav Jeremic
Cancers.2021; 13(4): 775. CrossRef - Immune Infiltration Profiling in Nonsmall Cell Lung Cancer and Their Clinical Significance: Study Based on Gene Expression Measurements
Fangyao Chen, Yuhui Yang, Yaling Zhao, Leilei Pei, Hong Yan
DNA and Cell Biology.2019; 38(11): 1387. CrossRef
First
Prev
