Volume 27, Issue 120 (January & February 2019)                   J Adv Med Biomed Res 2019, 27(120): 43-50 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Zaeran E, Azizmohammad Looha M, Amini P, Azimi T, Mahmoudi M. Evaluating Long-term survival of patients with esophageal cancer using parametric non-mixture cure rate models. J Adv Med Biomed Res 2019; 27 (120) :43-50
URL: http://journal.zums.ac.ir/article-1-5339-en.html
Evaluating Long-term survival of patients with esophageal cancer using parametric non-mixture cure rate models
Elahe Zaeran *1 , Mehdi Azizmohammad Looha2 , Payam Amini3 , Tara Azimi4 , Mahmoud Mahmoudi5
1- Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran , elahe.zarean.ez@gmail.com
2- Dept. of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Science, Tehran, Iran.
3- Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
4- Faculty of Public Health, University of Alberta, Edmonton, Alberta, Canada
5- Dept. of Epidemiology and Biostatistics, Faculty of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Abstract:   (149008 Views)

Background and Objective: Esophageal cancer (EC) has been identified as one of the most common cancers in the northeastern regions of Iran.  In our study, parametric non-mixture cure rate model was applied to evaluate the effects of risk factors on the long-term survival of patients with EC in East Azarbaijan, Northeastern Iran.
Materials and Methods: This retrospective cohort study of 127 patients with EC registered at Iran National Cancer Registry office in the period 2009-2010. These patients were followed up for 5 years in East Azarbaijan, Iran until 2015. The best parametric cure rate model was identified and the risk factors of survival in patients with EC were measured by Akaike Information Criteria and parametric non-mixture cure rate model, respectively.
Results: The survival time of EC patients ranged 0.10-69.03 months. Male sex (Odds Ratio (OR) =0.08, 95% confidence interval (CI):0.02-0.32, p-value<0.001), patients who underwent esophagectomy surgery (OR=6.11, 95%CI: 1.46-25.55, p-value=0.013) had a significant effect on the survival and the cure fraction of EC patients. Population cure rate was 0.11 (95%CI: 0.07-0.19) and cure fraction was estimated 4.9%.
Conclusion: The Weibull non- mixture cure rate model was the most appropriate statistical tool to identify potential risk factors that affect both survival and cure fraction of EC patients. It is a recommended tool for analyzing the long-term survival of patients with EC.

Keywords: Esophageal neoplasms, Survival nnalysis, Non-mixture Cure Rate models
Full-Text [PDF 719 kb]   (157073 Downloads) |   |   Full-Text (HTML)  (5260 Views)  

✅ The Weibull non- mixture cure rate model was the most appropriate statistical tool to identify potential risk factors that affect both survival and cure fraction of EC patients. It is a recommended tool for analyzing the long-term survival of patients with EC.


Type of Study: Original Research Article | Subject: Medical Laboratory and Animal Investigation
Received: 2018/09/6 | Accepted: 2019/09/16 | Published: 2019/09/16
References
1. Jemal A, Center MM, DeSantis C, Ward EM. Global patterns of cancer incidence and mortality rates and trends. Cancer Epidemiol Prevent Biomarker. 2010; 19(8): 1893-907. [DOI:10.1158/1055-9965.EPI-10-0437] [PMID]
2. Siegel R, Ma J, Zou Z, Jemal A. Cancer statistics, 2014. CA: Cancer J Clinic. 2014; 64(1): 9-29. [DOI:10.3322/caac.21208] [PMID]
3. Mousavi SM, Gouya MM, Ramazani R, Davanlou M, Hajsadeghi N, Seddighi Z. Cancer incidence and mortality in Iran. Ann Oncol. 2009; 20(3): 556-63. [DOI:10.1093/annonc/mdn642] [PMID]
4. Napier KJ, Scheerer M, Misra S. Esophageal cancer: A Review of epidemiology, pathogenesis, staging workup and treatment modalities. World J Gastrointest Oncol. 2014; 6(5): 112-20. [DOI:10.4251/wjgo.v6.i5.112] [PMID] [PMCID]
5. Pennathur A, Gibson MK, Jobe BA, Luketich JD. Oesophageal carcinoma. The Lancet. 2013; 381(9864): 400-12. [DOI:10.1016/S0140-6736(12)60643-6]
6. Salehiniya H, Hassanipour S, Mansour-Ghanaei F, et al. The incidence of esophageal cancer in Iran: A systematic review and meta-analysis. Biomed Res Ther. 2018; 5(7): 2493-503. [DOI:10.15419/bmrat.v5i7.459]
7. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA: Cancer J Clinic. 2017; 67(1): 7-30. [DOI:10.3322/caac.21387] [PMID]
8. Lambert R, Hainaut P. The multidisciplinary management of gastrointestinal cancer. Epidemiology of oesophagogastric cancer. Best Prac Res Clin Gastroenterol. 2007; 21(6): 921-45. [DOI:10.1016/j.bpg.2007.10.001] [PMID]
9. Pennathur A, Gibson MK, Jobe BA, Luketich JD. Oesophageal carcinoma. Lancet. 2013; 381(9864): 400-12. [DOI:10.1016/S0140-6736(12)60643-6]
10. Liu J, Yue J, Xing L, Yu J. Present status and progress of neoadjuvant chemoradiotherapy for esophageal cancer. Front Med. 2013; 7(2): 172-9. [DOI:10.1007/s11684-013-0268-0] [PMID]
11. Ardalan B, Spector SA, Livingstone AS, et al. Neoadjuvant, surgery and adjuvant chemotherapy without radiation for esophageal cancer. Japan J Clin Oncol. 2007; 37(8): 590-6. [DOI:10.1093/jjco/hym076] [PMID]
12. Kmet J, Mahboubi E. Esophageal cancer in the caspian littoral of Iran: Initial Studies. Science. 1972; 175(4024): 846-53. doi:10.1126/science.175.4024.846 [DOI:10.1126/science.175.4024.846] [PMID]
13. Islami F, Kamangar F, Aghcheli K, et al. Epidemiologic features of upper gastrointestinal tract cancers in Northeastern Iran. Br J Cancer. 2004; 90(7): 1402-6. [DOI:10.1038/sj.bjc.6601737] [PMID] [PMCID]
14. Somi MH, Farhang S, Mirinezhad SK, Naghashi S, Seif-Farshad M, Golzari M. Cancer in East Azerbaijan, Iran: results of a population-based cancer registry. Asian Pac J Cancer Prev. 2008; 9(2): 327-30.
15. Ghadimi MR, Mahmoodi M, Mohammad K, Rasouli M, Zeraati H, Fotouhi A. Factors affecting survival of patients with oesophageal cancer: a study using inverse Gaussian frailty models. Singapore Med J. 2012; 53(3): 336-43.
16. Gammon MD, Ahsan H, Schoenberg JB, et al. Tobacco, alcohol, and socioeconomic status and adenocarcinomas of the esophagus and gastric cardia. J National Cancer Inst. 1997; 89(17): 1277-84. [DOI:10.1093/jnci/89.17.1277] [PMID]
17. Gao YT, McLaughlin JK, Blot WJ, Ji BT, Dai Q, Fraumeni JF. Reduced risk of esophageal cancer associated with green tea consumption. J National Cancer Inst. 1994; 86(11): 855-8. [DOI:10.1093/jnci/86.11.855] [PMID]
18. Tuyns AJ, Riboli E, Doornbos G, Péquignot G. Diet and esophageal cancer in Calvados. Nutr Cancer. 1987; 9: 81-92. [DOI:10.1080/01635588709513915] [PMID]
19. Cook M, Chow W, Devesa S. Oesophageal cancer incidence in the United States by race, sex, and histologic type, 1977-2005. Br J Cancer. 2009; 101(5): 855-9. [DOI:10.1038/sj.bjc.6605246] [PMID] [PMCID]
20. Tavani A, Bertuzzi M, Talamini R, et al. Coffee and tea intake and risk of oral, pharyngeal and esophageal cancer. Oral Oncol. 2003; 39(7): 695-700. [DOI:10.1016/S1368-8375(03)00081-2]
21. Khosravi A, Taylor R, Naghavi M, Lopez AD. Mortality in the Islamic Republic of Iran, 1964-2004. Bull World Health Org. 2007; 85: 607-14. [DOI:10.2471/BLT.06.038802] [PMID] [PMCID]
22. Zarean E, Yaseri M, Mahmoodi M, Entezar Mahdi R. Factors affecting long-term survival rate with cure fraction using the mixture cure cox model in patients with gastric cancer in east-azerbaijan province, Iran. J School Pub Health Ins Pub Health Res. 2018; 15(4): 337-50.
23. Bremhorst V, Lambert P. Flexible estimation in cure survival models using Bayesian P-splines. Computational Statistics & Data Analysis. 2016; 93: 270-84. [DOI:10.1016/j.csda.2014.05.009]
24. Harrell Jr FE. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis: Springer; 2015.
25. Othus M, Mitchell A, Barlogie B, Morgan G, Crowley J. Cure-rate survival models and their application to cancer clinical trials. Front Biostatistic Method Application Clin Oncol: Springer. 2017; 165-78. [DOI:10.1007/978-981-10-0126-0_11]
26. Center for Disease Control and Prevention, Ministry of Health and Medical Education (Iran). 2009-2010.
27. Andersson TM, Dickman PW, Eloranta S, Lambert PC. Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models. BMC Med Res Methodol. 2011; 11(1): 96. [DOI:10.1186/1471-2288-11-96] [PMID] [PMCID]
28. Chen MH, Ibrahim JG, Sinha D. A new Bayesian model for survival data with a surviving fraction. J Am Statistic Assoc. 1999; 94(447): 909-19. [DOI:10.1080/01621459.1999.10474196]
29. Lambert PC, Thompson JR, Weston CL, Dickman PW. Estimating and modeling the
30. cure fraction in population-based cancer survival analysis. Biostatistics. 2007; 8(3): 576-94. [DOI:10.1093/biostatistics/kxl030] [PMID]
31. Maetani S, Gamel JW. Parametric cure model versus proportional hazards model in survival analysis of breast cancer and other malignancies. Adv Breast Cancer Res. 2013; 2(04): 119. [DOI:10.4236/abcr.2013.24020]
32. Louwman W, Aarts M, Houterman S, Van Lenthe F, Coebergh J, Janssen-Heijnen M. A 50% higher prevalence of life-shortening chronic conditions among cancer patients with low socioeconomic status. Br J Cancer. 2010; 103(11): 1742-48. [DOI:10.1038/sj.bjc.6605949] [PMID] [PMCID]
33. Tran PN, Taylor TH, Klempner SJ, Zell JA. The impact of gender, race, socioeconomic status, and treatment on outcomes in esophageal cancer: A population-based analysis. J Carcinog. 2017; 16: 3. [DOI:10.4103/jcar.JCar_4_17] [PMID] [PMCID]
34. Dar NA, Shah IA, Bhat GA, et al. Socioeconomic status and esophageal squamous cell carcinoma risk in Kashmir, India. Cancer Sci. 2013; 104(9): 1231-6. [DOI:10.1111/cas.12210] [PMID]
35. Golozar A, Etemadi A, Kamangar F, et al. Food preparation methods, drinking water source and esophageal squamous cell carcinoma in the high risk area of golestan, Northeast Iran. Europ J Cancer Prev. 2016; 25(2): 123-9. [DOI:10.1097/CEJ.0000000000000156] [PMID] [PMCID]
36. Pakzad R, Mohammadian-Hafshejani A, Khosravi B, et al. The incidence and mortality of esophageal cancer and their relationship to development in Asia. Ann Transl Med. 2016; 4(2): 29. [DOI:10.1016/j.prnil.2015.09.001] [PMID] [PMCID]
37. Shokri Shirvani J, Siadati S, Imandoost F. Demographic, endoscopic and pathologic features of esophageal carcinoma. J Mazandaran Univ Med Sci. 2017; 27(150): 176-80.
38. Bohanes P, Yang D, Chhibar RS, et al. Influence of sex on the survival of patients with esophageal cancer. J Clin Oncol. 2012; 30(18): 2265-72. [DOI:10.1200/JCO.2011.38.8751] [PMID] [PMCID]
39. Mathieu LN, Kanarek N, Hooker C, Rudin CM, Brock M. Esophageal cancer gender disparity. American Society of Clinical Oncology; 2012. [DOI:10.1200/jco.2012.30.30_suppl.47]
40. Pourhoseingholi MA, Vahedi M, Baghestani AR. Burden of gastrointestinal cancer in Asia; an overview. Gastroenterol Hepatol Bed Bench. 2015; 8(1): 19-27.
41. D'Amico TA. Outcomes after surgery for esophageal cancer. Gastrointestinal cancer research: GCR. 2007; 1(5): 188.
42. Boussari O, Romain G, Remontet L, et al. A new approach to estimate time-to-cure from cancer registries data. Cancer Epidemiol. 2018; 53: 72-80. [DOI:10.1016/j.canep.2018.01.013] [PMID]
43. Yu B, Tiwari RC, Cronin KA, Feuer EJ. Cure fraction estimation from the mixture cure models for grouped survival data. Stat Med. 2004; 23: 1733-47. [DOI:10.1002/sim.1774] [PMID]
44. Swain PK, Grover G, Goel K. Mixture and non-mixture cure fraction models based on generalized gompertz distribution under Bayesian Approach. Tatra Mountains Mathematical Publications. 2016; 66(1): 121-35. [DOI:10.1515/tmmp-2016-0025]
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2025 CC BY-NC 4.0 | Journal of Advances in Medical and Biomedical Research

Designed & Developed by : Yektaweb