Volume 5, Issue 3 (Summer 2017)                   Iran J Health Sci 2017, 5(3): 35-48 | Back to browse issues page


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Kerman University of Medical Sciences, School of Health, Kerman, IRAN.
Abstract:   (4541 Views)

Background and Purpose: Gastric cancer is the third leading cause of mortality in Iran after cardiovascular diseases and accidents. The aim of the present study was to assess survival and it’s affecting factors in gastric cancer patients through using Cox and parametric models along with frailty.

Materials and Methods: In this study, the medical records of gastric cancer patients treated from 2008 to late 2010 were collected in Afzalipour and Bahonar Hospitals in Kerman and Imam Khomeini Hospital in Sari. 383 patients entered the study and were followed up for at least five years. The survival of patients was assessed by using Cox proportional hazard, log-normal and log-logistic models under gamma and inverse-Gaussian distributions, as two special models for frailty. Models efficiency comparison criteria were Akaike information criterion and Cox-Snell residuals.

Results: Out of 196 patients in Kerman, 132(67.3%) were males and 64(32.7%) were females. The average age of the patient was 61yr and 59 yr for the males and females, respectively. Also, the survival rates after 1, 3, and 5 years of the diagnosis were 62%, 50%, and 45%, respectively. In the city of Sari, 69% (129 people) of the patients were male and 31% were female. The mean ages of male and female were 66 and 62 yr, respectively. At the same time, 1, 3, and 5 year survival rates of patients were 58%, 36%, and 30%, respectively. Based on Akaike information criterion, Cox-Snell residuals, and non-monotonic failure rate, log-logistic model along with gamma frailty was more fitted in comparison with other models. Using this model, radiotherapy, heartburn, and tumor grade were found as significant predictors.

Conclusion: Radiotherapy, heartburn, and tumor grade could be considered as more affected factors. According to rejection of the proportional hazard assumption, assessments of residual figures, and according to non-significant frailty effect by log-normal model, log-logistic model along with gamma frailty was found to be the best fitted model.

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Type of Study: Original Article | Subject: Environmental Health

References
1. Alireza S, Mehdi N, Ali M. Cancer occurrence in Iran in 2002, an international perspective. Asian Pac J Cancer Prev 2005; 6(3): 359-363.
2. Ghadimi MR, Mahmoodi M, Mohammad K, Zeraati H, Rasouli M, Sheikhfathollahi M. Survival Analysis of the patients with gastrointestinal cancer and compare in parametrics and cox models. Sjsph 2010; 8(2): 1-14.
3. Berrino F. Survival of cancer patiante in Europe: the Eurocare-2 study1999.
4. Inoue M, Tsugane S. Epidemiology of gastric cancer in Japan. Postgrad Med J 2005; 81(957): 419-424. [DOI:10.1136/pgmj.2004.029330] [PMID] [PMCID]
5. Sadjadi A, Malekzadeh R, Derakhshan MH, Sepehr A, Nouraie M, Sotoudeh M, et al. Cancer occurrence in Ardabil: Results of a population‐based Cancer Registry from Iran. Int J Cancer 2003; 107(1): 113-118. [DOI:10.1002/ijc.11359] [PMID]
6. Malekzadeh R, Derakhshan MH, Malekzadeh Z. Gastric cancer in Iran: epidemiology and risk factors. Arch Iran Med 2009; 12(6): 576-583, PMID: 19877751. [PMID]
7. Roder DM. The epidemiology of gastric cancer. Gastric cancer 2002; 5(1): 5-11. [DOI:10.1007/s10120-002-0203-6] [PMID]
8. Harrison, Fauci, Braunwail ea. Harrison's principle of internal medicine. 14th, editor: New York, McGraw Hill; 1998.
9. Adachi Y, Tsuchihashi J, Shiraishi N, Yasuda K, Etoh T, Kitano S. AFP-producing gastric carcinoma: multivariate analysis of prognostic factors in 270 patients. Oncology 2003; 65(2): 95-101. [DOI:10.1159/000072332] [PMID]
10. Ding Y-B, Chen G-Y, Xia J-G, Zang X-W, Yang H-Y, Yang L, et al. Correlation of tumor-positive ratio and number of perigastric lymph nodes with prognosis of patients with surgically-removed gastric carcinoma. World J Gastroenterol 2004; 10(2): 182-185. [DOI:10.3748/wjg.v10.i2.182] [PMCID]
11. Heise K, Bertran E, Andia ME, Ferreccio C. Incidence and survival of stomach cancer in a high-risk population of Chile. World J Gastroenterol 2009; 15(15): 1854-1862. [DOI:10.3748/wjg.15.1854] [PMID] [PMCID]
12. Schwarz RE, Zagala-Nevarez K. Recurrence patterns after radical gastrectomy for gastric cancer: prognostic factors and implications for postoperative adjuvant therapy. Ann Surg Oncol 2002; 9(4): 394-400. [DOI:10.1007/BF02573875] [PMID]
13. Thong-Ngam D, Tangkijvanich P, Mahachai V, Kullavanijaya P. Current status of gastric cancer in Thai patients. J Med Assoc Thai 2001; 84(4): 475-482. [PMID]
14. Mohebbi M, Mahmoodi M, Wolfe R, Nourijelyani K, Mohammad K, Zeraati H, et al. Geographical spread of gastrointestinal tract cancer incidence in the Caspian Sea region of Iran: spatial analysis of cancer registry data. BMC cancer 2008; 8(1): 137. [DOI:10.1186/1471-2407-8-137] [PMID] [PMCID]
15. Sadjadi A, Zahedi M, Nouraie M, Alimohammadian M, Ghorbani A, Bahmanyar S, et al. The first population-based cancer survey in Kerman Province of Iran. Iran J Public Health 2007; 36(4): 26-34.
16. Efron B. The efficiency of Cox's likelihood function for censored data. J Am Stat Assoc 1977; 72(359): 557-565. [DOI:10.1080/01621459.1977.10480613]
17. Oakes D. The asymptotic information in censored survival data. Biometrika 1977; 64(3): 441-448. [DOI:10.1093/biomet/64.3.441]
18. Saki Malhi A, Hajizade E, Ahmadi K. Weibull Frailty Model in Survival Anlysis of the Patients with Colorectal Cancer. J Appl Statist Sci 2012; 6(1): 82-96.
19. Hougaard P. Modeling heterogeneity in survival data. J Appl Probab 1991; 28(3): 695-701. https://doi.org/10.2307/3214503 [DOI:10.1017/S0021900200042534]
20. Hougaard P. Analysis of multivariate survival data: Springer New York; 2000.p. 215-262.
21. Moeschberger ML, Klein J. Survival analysis: techniques for censored and truncated data.1th ed. New York: Springer-Verlag; 2003. P. 91-105.
22. Hosmer D, Lemeshow S, May S. Applied survival analysis: regression modeling of time to event data. 1th ed. John Wiley & Sons New York; 1999. P. 200-225.
23. Duchateau L, Janssen P. The frailty model. Statistics for biology and health.1th ed. Springer, New York; 2008. P. 145-152.
24. Habil R. Frailty models in survival analysis. Halle-Wittenberg:Wittenberg ed. 1th, editor2007. [PMID]
25. Hosseini-Teshnizi S, Zare S, Tazhibi M. The evaluation of Cox and Weibull proportional hazards models and their applications to identify factors influencing survival time in acute leukemia. Hormozgan Med J 2010; 15(4): 269-278.
26. Lawless J. Statistical Models and Methods for Lifetime Data. 1th ed. New York, Wiley; 1982. P. 95-100.
27. Puorhoseingholi MA, Hajizade E, Abdi AR, Safaee A, Moghimi-Dehkordi B, Zali MR. Comparison of cox regression and parametric models in survival analysis of the patients with gastric cancer. Iran Epidemiology Journal 2007; 3(1): 25-29.
28. Clayton DG. A Monte Carlo method for Bayesian inference in frailty models. Biometrics 1991; 47(2): 467-85. [DOI:10.2307/2532139] [PMID]
29. Aalen OO. Two examples of modelling heterogeneity in survival analysis. Scand J Statist 1987; 14(1): 19-25.
30. Hougaard P. Survival models for heterogeneous populations derived from stable distributions. Biometrika 1986; 73(2): 387-396. [DOI:10.1093/biomet/73.2.387]
31. Lancaster T. Econometric methods for the duration of unemployment. Econometrica 1979; 47(4): 939-956. [DOI:10.2307/1914140]
32. Lindstrom DP. Economic opportunity in Mexico and return migration from the United States. Demography 1996; 33(3): 357-374. [DOI:10.2307/2061767] [PMID]
33. McCall BP. Testing the proportional hazards assumption in the presence of unmeasured heterogeneity. J Appl Econometr 1994; 9(3): 321-334. [DOI:10.1002/jae.3950090307]
34. Vaupel JW, Manton KG, Stallard E. The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 1979; 16(3): 439-454. [DOI:10.2307/2061224] [PMID]
35. Zelterman D. A statistical distribution with an unbounded hazard function and its application to a theory from demography. Biometrics 1992; 48(3): 807-818. [DOI:10.2307/2532346] [PMID]
36. Akaike H. A new look at the statistical model identification. Automatic Control, IEEE Transactions on. 1974; 19(6): 716-23. [DOI:10.1109/TAC.1974.1100705]
37. Moghimi-Dehkordi B, Rajaee fard AR, Tabatabaee SHR, Zeighami B, Safaei A. Modelling survival analusis with Cox model in patients with gastric cancer. Iran Epidemiology Journal. 2007; 3(1,2): 19-24.
38. Parkin DM, Bray F, Ferlay J, Pisani P. Global cancer statistics, 2002. CA Cancer J Clin 2005; 55(2): 74-108. [DOI:10.3322/canjclin.55.2.74] [PMID]
39. Eloubeidi MA, Desmond R, Arguedas MR, Reed CE, Wilcox CM. Prognostic factors for the survival of patients with esophageal carcinoma in the US. Cancer 2002; 95(7): 1434-1443. [DOI:10.1002/cncr.10868] [PMID]
40. Haugstvedt T, Viste A, Eide GE, Söreide O. The survival benefit of resection in patients with advanced stomach cancer: the Norwegian multicenter experience. World J Surg 1989; 13(5): 617-621. [DOI:10.1007/BF01658884] [PMID]
41. Correa P. Clinical implications of recent developments in gastric cancer pathology and epidemiology. Semin Oncol 1985; 12(1): 2-10. [PMID]
42. Altman D, De Stavola B, Love S, Stepniewska K. Review of survival analyses published in cancer journals. Br J Cancer 1995; 72(2): 511-518. [DOI:10.1038/bjc.1995.364] [PMID] [PMCID]
43. David CR. Regression models and life tables (with discussion). J R Stat Soc Series B Stat Methodol 1972; 34(2): 187-220.
44. Nardi A, Schemper M. Comparing Cox and parametric models in clinical studies. Stat Med 2003; 22(23): 3597-3610. [DOI:10.1002/sim.1592] [PMID]
45. Biglarian A, Hajizadeh E, Kazemnejad A, Zali M. Survival analysis of gastric cancer patients using Cox model: a five year study. Tehran Univ Med J 2009; 67(5): 317-325.
46. Moghimi-Dehkordi B, Safaee A, Ghiasi S, Zali M. Survival in gastric cancer patients: univariate and multivariate analysis. East Afr J Public Health 2009; 6(1): 41-44. [DOI:10.4314/eajph.v6i3.45773]
47. Rajaeefard AR, Moghimi-Dehkordi B, Tabatabaee SHR, Zeighami B, Safaee A, Pourhoseingholi MA, et al. Application of parametric models in survival analysis of gastric cancer. Feiz 2009; 13(2): 83-88.
48. Larizadeh M. Survival in Nonmetastatic Gastric Cancer Patients. J Kerman Univ Med Sci 2013; 20(5): 470-480.
49. Yazdani J, Sadeghi S, Janbabaei Q, Haghighi F. Applying Survival Analysis to Estimate Survival Time in Gastric Cancer Patients. J Mazand Univ Med Sci 2011; 21(85): 28-36.
50. Biglarian A, Hajizade E, Gohari MR, Khodabakhshi R. survival analysis and factors affecting of patients with gastric cancer. kosar 2007; 12(4): 355-345.
51. Ghadimi MR, Mahmoodi M, Mohammad K, Hoseini K, Rasouli M. Factors affecting survival of patients with gastric cancer using frailty models. Payesh 2011; 10(4):515-524.
52. Ghadimi MR, Mahmoodi M, Mohammad K, Zeraati H, Rasouli M, Sheikhfathollahi M. Family history of the cancer on the survival of the patients with gastrointestinal cancer in northern Iran, using frailty models. BMC Gastroenterol 2011; 11(1): 104. [DOI:10.1186/1471-230X-11-104] [PMID] [PMCID]
53. Larson P. Patients with family history of cancer a guide to primary care. Sussex Cancer Network 2007; 2: 1-15.
54. Pourhoseingholi MA, Hajizadeh E, Moghimi Dehkordi B, Safaee A, Abadi A, Zali MR. Comparing Cox regression and parametric models for survival of patients with gastric carcinoma. Asian Pac J Cancer Prev 2007; 8(3): 412-416. [PMID]
55. Yazdan Band A, Samadi F, Malekzadeh R, Babaee M, Iranparvar M, Aazami A. 4-years survival rate for gastrointestinal tract cancer in ardebil province. J Med Sci Ardebil 2005; 5(2): 172-178.
56. Baeradeh N, Lotfi M, Fallahzadeh H, Kargar S, Salman Roghani H. Survival rate of patients with stomach cancer and its effective factors in Yazd Province. JCHR 2015; 3(4): 278–287.
57. Moghimi-Dehkordi B, Safaee A, Pourhoseingholi MA, Fatemi R, Tabeie Z, Zali MR. Statistical comparison of survival models for analysis of cancer data. Asian Pacific J Cancer Prev 2008; 9(3): 417-420. [PMID]
58. Zhu HP, Xia X, Chuan HY, Adnan A, Liu SF, Du YK. Application of Weibull model for survival of patients with gastric cancer. BMC Gastroenterol 2011; 11(1): 1-6. [DOI:10.1186/1471-230X-11-1] [PMID] [PMCID]
59. Biglarian A, Hajizadeh E, Kazemnejad A, Zali MR. Postoperative Survival Prediction in Patients with Gastric Cancer. Daneshvarmed. 2009; 16 (81): 55-62.
60. Zeraati H. Life expectancy and risk factors for patients with gastric cancer surgery. Archive of SID 2004;3(4):21-30.
61. Lai JF, Kim S, Kim K, Li C, Oh SJ, Hyung WJ, et al. Prediction of recurrence of early gastric cancer after curative resection. Ann Surg Oncol 2009; 16(7): 1896-1902. [DOI:10.1245/s10434-009-0473-x] [PMID]
62. Barfei F, Abbasi M, Khodabakhshi R, Gohari M. Survival analysis of patients with adenocarcinoma gastric cancer in Fayazkhsh hospital, Tehran. RJMS 2014; 21(123): 1–9.
63. Whitson BA, Groth SS, Li Z, Kratzke RA, Maddaus MA. Survival of patients with distal esophageal and gastric cardia tumors: a population-based analysis of gastroesophageal junction carcinomas. J Thorac Cardiovasc Surg 2010; 139(1): 43-48. [DOI:10.1016/j.jtcvs.2009.04.011] [PMID]
64. Zare A, Mahmoodi M, Mohammad K, Zeraati H, Hosseini M, Naieni KH. Comparison between Parametric and Semi-parametric Cox Models in Modeling Transition Rates of a Multi-state Model: Application in Patients with Gastric Cancer Undergoing Surgery at the Iran Cancer Institute. Asian Pac J Cancer Prev 2013; 14(11): 6751-6755. https://doi.org/10.7314/APJCP.2013.14.11.6751 [DOI:10.7314/APJCP.2013.14.11.6369]
65. Orbe J, Ferreira E, Nú-ez‐Antón V. Comparing proportional hazards and accelerated failure time models for survival analysis. Stat Med 2002; 21(22): 3493-3510. [DOI:10.1002/sim.1251] [PMID]
66. 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(5): 336-343. [PMID]

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