Basic & Clinical Cancer Research 2017. 9(1):4-11.

Determining of the Effecting Factors on the Survival Time of Colorectal Cancer Patients Based on a Generalized Weibull Competing Risks Model
Soraya Moamer, Ahmad Reza Baghestani, Mohamad Amin Pourhoseingholi, Ali Akbar Khaden Maboudi, Seyed Hossein Seyed Agha, Mohammad Reza Zali

Abstract


Background and Objective: One of the main reasons of death around the world is Colorectal cancer. The incidence of this cancer has increased in recent years. The aim of this study was to evaluate the survival rate and to define the prognostic factors in Iranian colorectal cancer patients using parametric competing risk model. 

Materials and Methods: Data recorded from 1060 patients with colorectal cancer who registered in Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences (Tehran, Iran) from 2004 to 2015 in a retrospective study. Analysis was performed using competing risks model and based on the generalized Weibull distribution. Data analysis was carried out using R software and significance level was regarded as 0.05.

Results: At the end of follow-up, 380 (35.8%) patients died due to colorectal cancer, 49 (4.6%) patients due to other diseases and 631 (59.5%) patients survived until the end of the study. The mean survival time in studied patients was 56.96±1.46 months with median 45.5 months. According to competing-risks method, only age at diagnosis and body mass index has a significant effect on patient’s survival time.

 Conclusion: Based on parametric competing risk model, just age at diagnosis and body mass index were significant prognosis of colorectal cancer survival.

 


Keywords


Survival Analysis, Competing risk, Colorectal cancer, Weibull model

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References


Dolatkhah R, Somi MH, Kermani IA, Ghojazadeh M, Jafarabadi MA, Farassati F, et al. Increased colorectal cancer incidence in Iran: a systematic review and meta-analysis. B Journal of cancer epidemiology. 2015;15(1):997.

Chen Y-S, Xu S-X, Ding Y-B, et al (2013). Colorectal cancer screening in high-risk populations: a survey of cognition among medical professionals in Jiangsu, China. Asian Pac J Cancer Prev, 14, 6487-91.

Baghestani AR, Daneshvar T, Pourhoseingholi MA, Asadzade H. Survival of colorectal cancer patients in the presence of competing-risk. Asian Pacific Journal of Cancer Prevention. 2014;15(15):6253-5.

Ranjbar R, Saberfar E, Shamsaie A, Ghasemian E (2014). The aetiological role of human papillomavirus in colorectal carcinoma: an iranian population-based case control study. Asian Pac J Cancer Prev, 15, 1521-5

Center MM, Jemal A, Smith RA, et al (2009). Worldwide variations in colorectal cancer. CA: A Cancer J Clinicians, 59, 366-78

Ahmadi A, Mosavi-Jarrahi A, Pourhoseingholi MA. Mortality determinants in colorectal cancer patients at different grades: a prospective, cohort study in iran. Asian Pacific journal of cancer prevention: APJCP. 2015;16(3):1069.

Pourhoseingholi MA, Vahedi M, Baghestani AR. Burden of gastrointestinal cancer in Asia; an overview. Gastroenterology and Hepatology from bed to bench. 2015;8(1):19.

Kleinbaum DG. Survival Analysis: A Self-learning Text: Springer; 1996.

Shayan Z, Ayatollahi SMT, Zare N. A parametric method for cumulative incidence modeling with a new four-parameter log-logistic distribution. Theoretical Biology and Medical Modelling. 2011;8(1):43.

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;8:412-6.

- Jeong JH, Fine J. Direct parametric inference for the cumulative incidence function. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2006;55(2):187-200.

Pourhoseingholi MA, Moghimi-Dehkordi B, Safaee A, Hajizadeh E, Solhpour A, Zali MR. Prognostic factors in gastric cancer using log-normal censored regression model. Indian J Med Res 2009;129:262-7.

- Mudholkar GS, Srivastava DK, Kollia GD. A generalization of the Weibull distribution with application to the analysis of survival data. Journal of the American Statistical Association. 1996;91(436):1575-83.

Ansari R, Amjadi H, Norozbeigi N, Zamani F, Mir-Nasseri S, Khaleghnejad A, et al. Survival analysis of colorectal cancer in patients underwent surgical operation in Shariati and Mehr Hospital-Tehran, in a retrospective study. Gastroenterology. 2007;12(1):7-15. [In persian ]

- Sarhan AM, Alameri M, Al-Wasel I. Pak. J. Statist. 2013 Vol. 29 (3), 271-281 ANALYSIS OF A COMPETING RISKS MODEL WITH GENERALIZED WEIBULL DISTRIBUTIONS. Pak J Statist. 2013;29(3):271-81.

Baghestani AR, Gohari MR, Orooji A, Pourhoseingholi MA, Zali MR. Evaluation of parametric models by the prediction error in colorectal cancer survival analysis. Gastroenterology and Hepatology from bed to bench. 2015;8(3):183.

Hines RB, Shanmugam C, Waterbor JW, McGwin G, Funkhouser E, Coffey CS, et al. Effect of comorbidity and body mass index on the survival of African‐American and Caucasian patients with colon cancer. Cancer. 2009;115(24):5798-806.

Meyerhardt JA, Kroenke CH, Prado CM, Kwan ML, Castillo A, Weltzien E, et al. Association of weight change after colorectal cancer diagnosis and outcomes in the Kaiser Permanente Northern California Population. AACR; 2017.

Murphy TK, Calle EE, Rodriguez C, Kahn HS, Thun MJ. Body mass index and colon cancer mortality in a large prospective study. American journal of epidemiology. 2000;152(9):847-54.

Maskarinec G, Harmon BE, Little MA, Ollberding NJ, Kolonel LN, Henderson BE, et al. Excess body weight and colorectal cancer survival: the multiethnic cohort. Cancer Causes & Control. 2015;26(12):1709-18.

Cespedes Feliciano EM, Kroenke CH, Meyerhardt JA, Prado CM, Bradshaw PT, Dannenberg AJ, et al. Metabolic Dysfunction, Obesity, and Survival Among Patients With Early-Stage Colorectal Cancer. Journal of Clinical Oncology. 2016;34(30):3664-71.

Kocarnik JM, Chan AT, Slattery ML, Potter JD, Meyerhardt J, Phipps A, et al. Relationship of prediagnostic body mass index with survival after colorectal cancer: Stage‐specific associations. International journal of cancer. 2016

Møller H, Sandin F, Robinson D, Bray F, Klint Å, Linklater KM, et al. Colorectal cancer survival in socioeconomic groups in England: variation is mainly in the short term after diagnosis. European Journal of Cancer. 2012;48(1):46-53.

Mehrkhani F, Nasiri S, Donboli K, Meysamie A, Hedayat A. Prognostic factors in survival of colorectal cancer patients after surgery. Colorectal Disease. 2009;11(2):157-61.

Park YJ, Park KJ, Park J-G, Lee KU, Choe KJ, Kim J-P. Prognostic factors in 2230 Korean colorectal cancer patients: analysis of consecutively operated cases. World journal of surgery. 1999;23(7):721-6.

Siegel R, DeSantis C, Jemal A. Colorectal cancer statistics, 2014. CA: a cancer journal for clinicians. 2014;64(2):104-17.

Pourhoseingholi MA, Zali MR. Colorectal cancer screening: Time for action in Iran. World J Gastrointest Oncol. 2012;4(4):82-3.

Pourhoseingholi MA, Vahedi M, Moghimi-Dehkordi B, Pourhoseingholi A, Ghafarnejad F, Maserat E, et al. Burden of hospitalization for gastrointestinal tract cancer patients - Results from a cross-sectional study in Tehran. Asian Pac J Cancer Prev 2009;10:107-10.

Roshanaei G, Komijani A, Sadighi A,Faradmal J. Prediction of Survival in patients with colorectal cancer referred to the Hamadan MRI center using of weibull parameter model and determination of its risk factor during 2005-2013. Arak Medical Univesity Journal (AMUJ). 2014;11(80):16.[ persian ]

Shin A, Joo J, Yang H-R, Bak J, Park Y, Kim J, et al. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea. PloS one. 2014;9(2):e88079.

Moradi A, Khayamzadeh M, Guya MM, Mirzaei HR, Salmanian R, Rakhsha A, et al. Survival of colorectal cancer in Iran. Asian Pac J Cancer Prev. 2009;10(4):583-6.

Huang X, Zhang N. Regression survival analysis with an assumed copula for dependent censoring: a sensitivity analysis approach. Biometrics. 2008;64(4):1090-9.

Chin C-C, Wang J-Y, Yeh C-Y, Kuo Y-H, Huang W-S, Yeh C-H. Metastatic lymph node ratio is a more precise predictor of prognosis than number of lymph node metastases in stage III colon cancer. International journal of colorectal disease. 2009;24(11):1297-302.

Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. American journal of epidemiology. 2009:kwp107.

Moghimi-Dehkordi B, Safaee A, Pourhoseingholi MA, Fatemi R, Tabeie Z, Zali MR. Statistical comparison of survival models for analysis of cancer data. Asian Pac J Cancer Prev 2008;9:417-20.


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