Application of Illness-Death Model for Semi-Competing Risks in Analysis Gastric Cancer Data

Faraji, MA; Rahgozar, M; Roushan, N; Biglarian, A

Application of Illness-Death Model for Semi-Competing Risks in Analysis Gastric Cancer Data

Document Type : Original Article

Authors

MA Faraji ¹

M Rahgozar ²

N Roushan ³

A Biglarian ⁴

¹ Master of Science, Department of Biostatistics, Student Research Committee, University of Welfare and Rehabilitation Sciences

² Associate Professor, Department of Biostatistics, University of Welfare and Rehabilitation Sciences

³ Associate Professor of Adult Gastroenterology and Liver Diseases, Department of Internal Medicine, Imam Khomeini Hospital, Faculty of Medicine, Tehran University of Medical Sciences

⁴ Associate Professor of Biostatistics, Department of Biostatistics, Social Determinants of Health Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran

Abstract

Introduction & Objective: In cancer studies, sometimes events are semi-competing. In this case, each patient experiences more than one event, and only one of those is a terminal event (for example, death), and others are non-terminal events (for example, relapse of the disease after treatment) where the risk of death competes with the risk of a non-terminal event of relapse but not vice versa. The aim of this study was to determine the effect of risk factors on relapse time, death time without relapse, and death time after relapse in gastric cancer patients using an illness-death model. Materials & Methods: In this historical cohort study, 200 patients with gastric cancer were studied. These patients had referred to Imam Khomeini hospital 2003 - 2007 and were followed up to October 2009. The data was analyzed by illness-death model, with and without frailty by considering semi-Markov assumption in R 3.4.3 software. Results: Of the patients, 61 (30.5%) had relapses, 71 (35.5%) deaths, 27 (13%) relapsed and death, 34 (17%) relapsed without death and 44 (22%) was died without relapsed. In multivariate analysis, based on confidence intervals, the lymph nodes had a significant effect on the relapse time; metastasis had a significant effect on the time of death without relapse and stage of disease had a significant effect on the death time after relapse. Conclusions: Estimates of the effect of variables were different at the stages in the model. In other words, the effect of risk factors on the time of death was different before and after of relapse occurrence. Therefore, the relapse event as an intermediate event is very important and should be considered by therapists.

Keywords

Semi-Competing Risks

Illness-Death Model

Relapse

Gastric Cancer

1. Biglarian A, Hajizadeh E, Kazemnejad A, Zali M. Survival analysis of gastric cancer patients using Cox model&58; a fiveyear study. Tehran University Medical Journal. 2009; 67(5): 317-25.

2. Saberi M, Minuchehr Z, Shahlaei M, Kheitan S. An Insilico method to identify key proteins involved in the development of gastric cancer. Research in Medicine. 2017; 41(3): 199-209.

3. Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer. 2015; 136(5): E359-E86.

4. Akhondzadeh E, Yavari P, Mehrabi Y, Kabir A. Estimates of one, three,and five year survival rate of patients with Gastric cancer in Iran using the
meta-analysis method. Iranian Journal of Epidemiology. 2015; 11(1): 1-12.

5. Bazyar M, Akbari F, Mahmoudi M. Medical and
non-medical direct costs of cancers in patients hospitalized in Imam Khomeini cancer
institution-2010. Journal of Hospital. 2012; 11(1):
39-50.

6. Mardani Hamooleh M, Ebrahimi E, M Mostaghaci M. The effect of psycho educational program on social anxiety among cancer patients. Modern Care Journal. 2013; 9(3): 181-9.

7. Kahrazei F, Danesh E, Hydarzadegan A. The effect of cognitive-behavioral therapy (CBT) on reduction of psychological symptoms among patients with cancer. 2012.

8. Mahmudvand H, Vahedian Ardakani J, Zargarani M. ,Nadri S, Obeidavi Z. Comparative study of sextuple dimensions of Quality of Life in patients with Gastric cancer after surgery with the control group. Iranian Journal of Surgery, 2016; 4: 53-61.

9. Kazemnejad A, Roshanaei G, Sedighi S. Postoperative survival estimation of gastric cancer patients in cancer institute of Tehran, Imam Khomeini hospital and its relative factors. Scientific Journal of Hamadan University of Medical Sciences. 2010; 17(3): 13-8.

10. Lin Y, Liu J, Jin L, Jiang Y. Polymorphisms in matrix metalloproteinases 2, 3, and 8 increase recurrences and mortality risk by regulating enzyme activity in gastric adenocarcinoma. Oncotarget. 2017; 8(62): 105971.

11. Yoo C, Noh S, Shin D, Choi S, Min J. Recurrence following curative resection for gastric carcinoma. British Journal of Surgery. 2000; 87(2): 236-42.

12. Uña E. Gastric cancer: predictors of recurrence when lymph-node dissection is inadequate. World journal of surgical oncology. 2009; 7(1): 69.

13. Roviello F, Marrelli D, De Manzoni G, Morgagni P, Di Leo A, Saragoni L, et al. Prospective study of peritoneal recurrence after curative surgery for gastric cancer. British journal of surgery. 2003; 90(9):
1113-9.

14. Li H, Jin X, Liu P, Hong W. Time to local recurrence as a predictor of survival in unrecetable gastric cancer patients after radical gastrectomy. Oncotarget. 2017; 8(51): 89203.

15. Kleinbaum DG KM. Survival analysis: A
self-learning text. Springer New York 2011; Third Edition.

16. Selle ML. Modelling of semi-competing risks using the illness-death model with shared frailty: NTNU; 2016.

17. Yu M, Yiannoutsos CT. Marginal and conditional distribution estimation from
double-sampled semi-competing risks data. Scandinavian Journal of Statistics. 2015; 42(1):
87-103.

18. Jiang H, Chappell R, Fine JP. Estimating the distribution of nonterminal event time in the presence of mortality or informative dropout. Controlled clinical trials. 2003; 24(2): 135-46.

19. Xu J, Kalbfleisch JD, Tai B. Statistical analysis of illness-death processes and semicompeting risks data. Biometrics. 2010; 66(3): 716-25.

20. Jiang F, Haneuse S. A Semi-parametric transformation frailty model for semi-competing risks survival data. Scandinavian Journal of Statistics. 2017; 44(1): 112-29.

21. Hsieh J-J, Hsu C-H. Estimation of the survival function with redistribution algorithm under
semi-competing risks data. Statistics & Probability Letters. 2018; 132:1-6.

22. Fine JP, Jiang H, Chappell R. On
semi-competing risks data. Biometrika. 2001; 88(4): 907-19.

23. Jiang H, Fine JP, Kosorok MR, Chappell R. Pseudo self-consistent estimation of a copula model with informative censoring. Scandinavian Journal of Statistics. 2005; 32(1): 1-20.

24. Lakhal L, Rivest LP, Abdous B. Estimating survival and association in a semicompeting risks model. Biometrics. 2008; 64(1):180-8.

25. Sildnes B, Lindqvist BH. Modeling of
semi-competing risks by means of first passage times of a stochastic process. Lifetime data analysis. 2018; 24(1): 153-75.

26. Han B, Yu M, Dignam JJ, Rathouz PJ. Bayesian approach for flexible modeling of semicompeting risks data. Statistics in medicine. 2014; 33(29):
5111-25.

27. Roshanaei G, Kazemnejad A, Sadighi S. Survival estimating following recurrence in gastric cancer patients and its relative factors. Koomesh. 2011; 12(3): 223-8.

28. Haneuse S, Lee KH. Semi-competing risks data analysis: Accounting for death as a competing risk when the outcome of interest is nonterminal. Circ Cardiovasc Qual Outcomes. 2016; 9(3):322-31. doi: 10.1161/CIRCOUTCOMES.115.001841.

29. Siewert JR, Bohcher K, Roder JD, Busch R. Prognostic relevance of systemic lymph node dissection in gastric carcinoma. Br J Surg. 1993; 80: 1015-1018.

30. Sadighi S, Mohagheghi MA, Haddad P, Omranipoor R, Moosavi Jarrahi AR, Meemari F, et al. Life expectancy with perioperative chemotherapy and chemoradiotherapy for locally advanced gastric adenocarcinoma. TUMJ. 2008; 66(9): 664-669.

31. Atoof F, Mahmoudi M, Zeraati H, Rahimi Foroushani A, Moravveji SA. Survival analysis of gastric cancer patients’ refering to Emam-Khomeini hospital using Weibull cure model. KAUMS Journal (FEYZ). 2011; 14(4): 405-13.

32. Barfei F, Abbasi M, Khodabakhshi R, Gohari MR. Survival analysis of patients with adenocarcinoma gastric cancer in Fayazkhsh hospital, Tehran. Razi Journal of Medical Sciences. 2014; 21(123): 1-9.

33. Moghimi Dehkordi B, Rajaeefard A, Tabatabaee H, Zeighami B, Safaee A, Tabeie Z. Modeling survival analysis in gastric cancer patients using the proportional hazards model of Cox. Iranian Journal of Epidemiology. 2007; 3(1): 19-24.

34. Takenaka R, Kawahara Y, Okada H, Hori K, Inoue M, Kawano S, et al. Risk factors associated with local recurrence of early gastric cancers after endoscopic submucosal dissection. Gastrointestinal endoscopy. 2008; 68(5): 887-94.

35. Maroufizadeh S, Hajizadeh E, Baghestani AR, Fatemi SR. Multivariate analysis of prognostic factors in gastric cancer patients using additive hazards regression models. Asian Pac J Cancer Prev. 2011; 12(7): 1697-702.

36. Roshanaei G, Kazemnejad A, Sadighi S. Determination of affected risk factors on time to recurrence and death in patients with postoperative gastric cancer using copula function. J Res Health Sci. 2014; 14(1): 52-6.