Volume 8, Issue 3 (Summer 2020)                   Iran J Health Sci 2020, 8(3): 29-36 | Back to browse issues page

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Pahlevani V, Fallahzadeh H, Pahlevani N, Nikpour A, Mohammadzadeh M. The Effect of Time-dependent Prognostic Factors on Survival of Non-Small Cell Lung Cancer using Bayesian Extended Cox Model. Iran J Health Sci 2020; 8 (3) :29-36
URL: http://jhs.mazums.ac.ir/article-1-728-en.html
Tarbiat Modares University, Tehran, Iran. , m_morteza@modares.ac.ir
Abstract:   (1788 Views)
Background: Lung cancer is one of the most common cancers around the world. The aim of this study was to use Extended Cox Model (ECM) with Bayesian approach to survey the behavior of potential time-varying prognostic factors of Non-small cell lung cancer.
Materials and Methods: Survival status of all 190 patients diagnosed with Non-Small Cell lung cancer referring to hospitals in Yazd were recorded from 2009 to 2013 by phone call. We fitted conventional Cox proportional hazards (Cox PH) as well as Bayesian ECM. Inference for estimated risk ratios was based on 90% credible intervals. Log pseudo marginal likelihood criteria (LMPL) was used for model comparison. Statistical computations were based on R language.
Results: In this study, 190 patients with non-small cell lung cancer were followed, of whom 160 died because of the disease (84.2%). Median of survival time was 8 ± 0.076 month. After fitting the Cox PH Model, it was determined that the PH assumption was not satisfied for the type of treatment, the disease stage, and pathology status variables (p <0.001). LPML for Cox PH and Bayesian ECM was -431.593 and -401.01, respectively. Estimated hazard ratio curves based on Bayesian ECM showed that the risk ratio for these variables exhibited significant time varying behavior on hazard of lung cancer through follow up time.
Conclusion: Based on LMPL, Bayesian ECM was found to have a better fit than Cox PH Model which declares, results from Cox PH should be interpreted with care. Especially, from beginning of the study to about 20 month after, very high risk ratio was estimated for variables whose PH was not satisfying for them.
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1. Seyfried TN, Shelton LM. Cancer as a metabolic disease. Nutrition & metabolism. 2010;7(1):7. [DOI:10.1186/1743-7075-7-7] [PMID] [PMCID]
2. Torre LA, Siegel RL, Jemal A. Lung cancer statistics. In: Lung cancer and personalized medicine. Springer; 2016. p. 1-19. [DOI:10.1007/978-3-319-24223-1_1] [PMID]
3. Zhang H, Guttikonda S, Roberts L, Uziel T, Semizarov D, Elmore SW, et al. Mcl-1 is critical for survival in a subgroup of non-small-cell lung cancer cell lines. Oncogene. 2011;30(16):1963-1968. [DOI:10.1038/onc.2010.559] [PMID]
4. Tucker ZC, Laguna BA, Moon E, Singhal S. Adjuvant immunotherapy for non-small cell lung cancer. Cancer treatment reviews. 2012;38(6):650-661. [DOI:10.1016/j.ctrv.2011.11.008] [PMID]
5. Schiwitza A, Schildhaus H-U, Zwerger B, Rüschoff J, Reinhardt C, Leha A, et al. Monitoring efficacy of checkpoint inhibitor therapy in patients with non-small-cell lung cancer. Immunotherapy. 2019;11(9):769-782. [DOI:10.2217/imt-2019-0039] [PMID]
6. Cox DR. Analysis of survival data. Chapman and Hall/CRC; 2018.
7. Kleinbaum DG, Klein M. Survival analysis. Springer; 2010.
8. Kalantar-Zadeh K, Kuwae N, Regidor DL, Kovesdy CP, Kilpatrick RD, Shinaberger CS, et al. Survival predictability of time-varying indicators of bone disease in maintenance hemodialysis patients. Kidney international. 2006;70(4):771-780. [DOI:10.1038/sj.ki.5001514] [PMID]
9. Klein JP, Van Houwelingen HC, Ibrahim JG, Scheike TH. Handbook of survival analysis. CRC Press; 2016. [DOI:10.1201/b16248]
10. Ibrahim JG, Chen M-H, Sinha D. Bayesian Survival Analysis. Wiley StatsRef: Statistics Reference Online. 2014; [DOI:10.1002/9781118445112.stat06003]
11. Lee E, Zhu H, Kong D, Wang Y, Giovanello KS, Ibrahim JG. BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer's disease. The annals of applied statistics. 2015;9(4):2153. [DOI:10.1214/15-AOAS879] [PMID] [PMCID]
12. Wang W, Chen M-H, Wang X, Yan J. dynsurv: Dynamic Models for Survival Data [Internet]. 2017. Available from: https://CRAN.R-project.org/package=dynsurv
13. Wang X, Chen M-H, Yan J. Bayesian dynamic regression models for interval censored survival data with application to children dental health. Lifetime data analysis. 2013;19(3):297-316. [DOI:10.1007/s10985-013-9246-8] [PMID]
14. Yang H-X, Woo KM, Sima CS, Bains MS, Adusumilli PS, Huang J, et al. Long-Term Survival Based on the Surgical Approach to Lobectomy for Clinical Stage I Non-Small Cell Lung Cancer: Comparison of Robotic, Video Assisted Thoracic Surgery, and Thoracotomy Lobectomy. Annals of surgery. 2017;265(2):431. [DOI:10.1097/SLA.0000000000001708] [PMID] [PMCID]
15. Fernandez FG, Kosinski AS, Furnary AP, Onaitis M, Kim S, Habib RH, et al. Differential effects of operative complications on survival after surgery for primary lung cancer. The Journal of thoracic and cardiovascular surgery. 2018;155(3):1254-1264. [DOI:10.1016/j.jtcvs.2017.09.149] [PMID]
16. Baine MJ, Verma V, Schonewolf CA, Lin C, Simone II CB. Histology significantly affects recurrence and survival following SBRT for early stage non-small cell lung cancer. Lung cancer. 2018;118:20-26. [DOI:10.1016/j.lungcan.2018.01.021] [PMID]
17. Wang S, Chen A, Yang L, Cai L, Xie Y, Fujimoto J, et al. Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Scientific reports. 2018;8(1):1-9. [DOI:10.1038/s41598-018-27707-4] [PMID] [PMCID]
18. Pacheco JM, Gao D, Smith D, Purcell T, Hancock M, Bunn P, et al. Natural History and Factors Associated with Overall Survival in Stage IV ALK-Rearranged Non-Small Cell Lung Cancer. Journal of Thoracic Oncology. 2019;14(4):691-700. [DOI:10.1016/j.jtho.2018.12.014] [PMID] [PMCID]
19. Fine JP, Yan J, Kosorok MR. Temporal process regression. Biometrika. 2004; 91(3):683-703. [DOI:10.1093/biomet/91.3.683]
20. Peng L, Huang Y. Survival analysis with temporal covariate effects. Biometrika. 2007; 94(3):719-733. [DOI:10.1093/biomet/asm058]

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