期刊论文详细信息
BMC Gastroenterology
Family history of the cancer on the survival of the patients with gastrointestinal cancer in northern Iran, using frailty models
Mahmood Sheikhfathollahi1  Mahboobeh Rasouli1  Hojjat Zeraati1  Kazem Mohammad1  Mahmood Mahmoodi1  Mahmoodreza Ghadimi1 
[1] Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
关键词: Frailty models;    AIC;    Parametric models;    Survival analysis;    Gastrointestinal tract cancer;   
Others  :  1113247
DOI  :  10.1186/1471-230X-11-104
 received in 2010-11-03, accepted in 2011-10-01,  发布年份 2011
PDF
【 摘 要 】

Background

Gastrointestinal (GI) tract cancer is one of the common causes of the mortality due to cancer in most developing countries such as Iran. The digestive tract is the major organ involved in the cancer. The northern part of the country, surrounded the Caspian Sea coast, is well known and the region with highest regional incidence of the GI tract cancer. In this paper our aim is to study the most common risk factors affecting the survival of the patients suffering from GI tract cancer using parametric models with frailty.

Methods

This research was a prospective study. Information of 484 cases with GI cancer was collected from Babol Cancer Registration Center during 1990-1991. The risk factors we studied are age, sex, family history of cancer, marital status, smoking status, occupation, race, medication status, education, residence (urban, rural), type of cancer, migration status (indigenous, non-native). The studied cases were followed up until 2006 for 15 years. Hazard ratio was used to interpret the death risk. The effect of the factors in the study on the patients survival are studied under a family of parametric models including Weibull, Exponential, Log-normal, and the Log-logistic model. The models are fitted using with and without frailty. The Akaike information criterion (AIC) was considered to compare between competing models.

Results

Out of 484 patients in the study, 321 (66.3%) were males and 163 (33.7%) were females. The average age of the patient at the time of the diagnosis was 59 yr and 55 yr for the males and females respectively. Furthermore, 359 (74.2%) patients suffered from esophageal, 110 (22.7%) patients recognized with gastric, and 15 (3.1%) patients with colon cancer. Survival rates after 1, 3, and 5 years of the diagnosis were 24%, 16%, and 15%, respectively. We found that the family history of the cancer is a significant factor on the death risk under all statistical models in the study. The comparison of AIC using the Cox and parametric models showed that the overall fitting was improved under parametric models (with and without frailty). Among parametric models, we found better performance for the log-logistic model with gamma frailty than the others. Using this model, gender and the family history of the cancer were found as significant predictors.

Conclusions

Results suggested that the early preventative care for patients with family history of the cancer may decrease the risk of the death in the patients with GI cancer. The gender appeared to be an important factor as well so that men experiencing lower risk of death than the women in the study. Since the proportionality assumption of the Cox model was not held (p = 0.0014), the Cox regression model was not an appropriate choice for analysing our data.

【 授权许可】

   
2011 Ghadimi et al; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150204020320670.pdf 535KB PDF download
Figure 3. 34KB Image download
Figure 2. 49KB Image download
Figure 1. 35KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】
  • [1]Yazdanbod A, Nasseri S, Malekzadeh R: Upper gastrointestinal cancer in Ardabil, North West of Iran: A review. Arch Iranian Med 2004, 7(3):173-7.
  • [2]Parkin DM, Pisani P, Ferlay J: Estimates of the worldwide incidence of 25 major cancers in 1990. Int J Cancer 1999, 80:827-41.
  • [3]Zali M, et al.: Indices related to gastric cancer in Tehran and seven city provinces in the years 1999 to 2002. J Islamic Azad Univ Med 2005, 15(1):15-8.
  • [4]Ferlay J, Bray F, Pisani P, Parkin DM: GLOBOCAN 2002: cancer incidence. Mortality and prevalence worldwide. In IARC cancer base no 5, version 20. Lyon (France): IARC Press; 2004.
  • [5]Iranian Annual of Cancer Registration: 2001-2002. Tehran, Iran: Cancer Office, Center for Disease Control, Deputy for Health, Ministry of Health and Medical Education. (Persian). 2002.
  • [6]Azizi F: The Epidemiology of common Diseases in Iran. Tehran: Eshtiagh 1999.
  • [7]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, 8:137. BioMed Central Full Text
  • [8]Cancer Control Office of Ministry of Health: Iranian annual cancer registration report: 2003. Tehran: Ministry of Health publication; 2005.
  • [9]Sadjadi A, Nouraie M, Mohagheghi MA, Mousavi-Jarrahi A, Malekzadeh R, Parkin DM: Cancer occurrence in Iran in 2002, an International perspective. Asian Pacific J Cancer Prev 2005, 6:359-63.
  • [10]Naghavi M: Iranian annual of national death registration report. Tehran; Ministry of Health and Medical Education. (Persian) 2005.
  • [11]Naghavi N: Death report from 23 provinces in Iran. 1st edition. Tehran: Ministry of Health; 2004.
  • [12]Hougaard p: Analysis of Multivariate Survival Data. New York: Springer-Verlag, Inc; 2000.
  • [13]Kleinbaum DG, Klein M: Survival Analysis A Self-Learning Text. New York: Springer-verlag, Inc; 2005.
  • [14]Cox DR: Regression models and life tables(with Discussion). J R statist soc B 1972, 34(2):187-220.
  • [15]Kalbfleisch JD, Prentice RL: The Statistical Analysis of Failure Time Data. 2nd edition. Wiely, New York; 2002.
  • [16]Lawless JF: Parametric models in survival analysis. In Encyclopaedia of Biostatistics. Edited by Armitage P, Colton T. Wiley: New York; 1998:3254-64.
  • [17]Efron B: The efficiency of Cox's likelihood function for censored data. J Am Statist Ass 1977, 72:557-65.
  • [18]Oakes D: Comparision of Models For Survival Data. Statist Med 1983, 2:305-11.
  • [19]Duchateau L, Janssen P: The Frailty Model. New York, Inc.: Springer-Verlag; 2008.
  • [20]Hougaard p: Modeling Heterogeneity in Survival Data. J Appl probab 1991, 28:695-701.
  • [21]O'Quigley J, Stare J: Proportional hazards models with frailties and random effects. Statist Med 2002, 21:3219-33.
  • [22]Aalen OO: Effects of frailty in survival analysis. Stat Methods Med Res 1994, 3:227-43.
  • [23]Akaike H: A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974, 19:716-23.
  • [24]klein JP, Moeschberger ML: Survival Analysis: Techniques for Censored and Truncated Data. New York, Inc.: Springer-verlag; 2003.
  • [25]Urba SG, Orringer MB, Turrisi A, Iannettoni M, Forastiere A, Strawderman M: Randomized trial of preoperative chemoradiation versus surgery alone in patients with locoregional esophageal carcinoma. J Clin Oncol 2001, 19:305-13.
  • [26]Chen HS, Sheen Chen SM: Obstruction and perforation in colorectal adenocarcinoma: an analysis of prognosis and current trends. Surgery 2000, 127:370-6.
  • [27]Yoshida Y, Okamura T, Ezaki T, Kawahara H, Shirakusa T: An evaluation of prognostic factors in patients with esophageal carcinoma. J UOEH 1993, 15:155-60.
  • [28]Holscher AH, Bollschweiler E, Schneider PM, Siewert JR: Prognosis of early esophageal cancer. Comparison between adeno- and squamous cell carcinoma. Cancer 1995, 76:178-86.
  • [29]Ikeda M, Furukawa H, Imamura H, Shimizu J, Ishida H, Masutani S, et al.: Poor prognosis associated with thrombocytosis in patients with gastric cancer. Ann Surg Oncol 2002, 9:287-91.
  • [30]Lerose R, Molinari R, Rocchi E, Manenti F, Villa E: Prognostic features and survival of hepatocellular carcinoma in Italy: impact of stage of disease. Eur J Cancer 2001, 37:239-45.
  • [31]Monreal M, Fernandez-Llamazares J, Pinol M, Julian JF, Broggi M, et al.: Platelet count and survival in patients with colorectal cancer: a preliminary study. Thromb Haemost 1998, 79:916-8.
  • [32]Alidina A, Gaffar A, Hussain F, Islam M, Vaziri I, Burney I, et al.: Survival data and prognostic factors seen in Pakistani patients with esophageal cancer. Ann Oncol 2004, 15(1):118-22.
  • [33]Petrequin P, Huguier M, Lacaine F, Houry S: Surgically treated esophageal cancers: predictive model of survival. Gastroenterol Clin Biol 1997, 21(1):12-6.
  • [34]Altman DG, Destavola BL, Love SB, Stepniewska KA: Review of survival analyses published in cancer journals. Br J Cancer 1985, 72:511-8.
  • [35]Curtis RE, Kennedy BJ, Myers MH, Hankey BF: Evaluation of AJC stomach cancer staging using the SEER population. Semin Oncol 1985, 12:21-31.
  • [36]Ries LA, Eisner MP, Kosary CL, Hankey BF, Miller BA, Clegg L, et al.: 1973-1989. NIH pub Cancer Statistics Review. No.92-2789. USA, Bethesda: National Cancer Institute; 1992, 21:1-9.
  • [37]Ries LA, Kosary CL, Hankey BF: SEER Cancer Statistics Review 1973-1995. Bethesda: U.S. In Dept. of Health and Human Services, Public Health Service. National Institutes of Health, National Cancer Institute; 1998.
  • [38]larson P: patients with a family history of cancer a guide to primary care. sussex cancer net 2007, 2:1-15.
  • [39]Munoz S, M , Vecchia C: Gastric Cancer Risk Factors in Subjects with Family History. Cancer epidemiol Biomarkers Prev 1997, 6:137-40.
  • [40]Nardi A, Schemper M: Comparing cox and parametric models in clinical studies. Statist Med 2003, 22:3597-610.
  • [41]Nardi A, Schemper M: New residuals for Cox regression and their application to outlier screening. Biometrics 1999, 55:523-9.
  • [42]HRbe J, Ferreira E, Nunez-Anton V: Comparing proportional hazards and accelerated failure time models for survival analysis. Statist Med 2002, 21:3493-510.
  • [43]Stute W: Consistent estimation under random censorship when covariables are present. J Multivariate Analysis 1993, 45:89-103.
  • [44]Bradburn MJ, Clark TG, Love SB, Altman DG, Survival Analysis Part III: Multivariate data analysis-choosing a model and assessing its adequacy and fit. Br J Cancer 2003, 89:605-11.
  • [45]Habil RN: Frailty models in survival analysis. Wittenberg: Halle-Wittenberg; 2007.
  • [46]Ghadimi R, Taheri H, Suzuki S, Kashifard M, Hosono A, Esfandiary I, Moghadamnia A, Ghadimi R, Tokudome S: Host and environmental factors for gastric cancer in Babol, the Caspian Sea Coast, Iran. Eur J Cancer Prev 2007, 16(3):192-95.
  • [47]Boccia B: Genetic Determinants of Gastric Cancer. Rome: Erasmus university Rotterdam; 2009.
  • [48]Vaupel JW, Manton KG, Stallavd E: The impact of heterogeneity in individual frailty on the dynamic of mortality. Demography 1997, 16:439-54.
  • [49]Henderson R, Oman P: Effect of frailty on marginal regression. J R Statist Soc B 1999, 61:367-79.
  • [50]Schumacher M, Olschewski M, Schmoor C: The impact of heterogeneity on the comparison of survival times. Statist Med 1987, 6:773-84.
  • [51]keiding N, Anderson PK, klein PJ: The role of frailty models and accelerated failure time models in describing heterogeneity due to omitted covariates. Statist Med 1997, 16:215-24.
  文献评价指标  
  下载次数:46次 浏览次数:26次