Long-Term Survival of Patient with End-Stage Renal Disease Using Bayesian Mixture Cure Rate Frailty Models
-
Abbas Bahrampour
,
Mohammad Reza Baneshi
,
Ali Karamoozian
,
Navisa Sadat Seyedghasemi
,
Abbas Etminan
,
Mostafa Eghbalian
Abstract
Background: Along with the increasing prevalence of ESRD in developing countries, the use of more up-to-date statistical models is highly recommended. It is crucial to control potential cure pattern and heterogenicity among patients.
Methods: In this longitudinal study, the data of 170 hemodialysis patients who visited the dialysis department of Shafa Hospital in Kerman from 2006 to 2016 were collected. To provides robust estimates the time to event data (death) were analyzed with a gamma frailty mixed cure Weibull model (MC-WG) using Bayesian inference.
Results: About 49% of patients experienced the death and median survival time was 37.5 months. Older patients (0.264), female patients (0.269), and patients with higher mean serum urea levels (0.186) had a higher risk of death. Moreover, we observe a decrease in death with increase in Creatine (Cr).
Conclusion: In the MC-WG Bayesian model, the diabetes, AST, calcium, phosphorus and uric acid variables had a significant effect on the survival of hemodialysis patients, while they were not significant in the Cox PH model. The results of MC-WG Bayesian model are more consistent with other studies.
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6. Leão J, Leiva V, Saulo H, Tomazella V (2018). Incorporation of frailties into a cure rate regression model and its diagnostics and application to melanoma data. Stat Med, 37(29):4421-40.
7. Peng Y, Taylor JM (2011). Mixture cure model with random effects for the analysis of a multi‐center tonsil cancer study. Stat Med, 30(3):211-23.
8. de Souza D, Cancho VG, Rodrigues J, Balakrishnan N (2017). Bayesian cure rate models induced by frailty in survival analysis. Stat Methods Med Res, 26(5):2011-28.
9. Nikaeen R, Khalilian A, Bahrampour A (2017). Determining the effective factors on gastric cancer using frailty model in South-East and North of Iran. Iran J Health Sci, 5(3): 35-48.
10. Dokhi M, Ohtaki M, Hiyama E (2009). A cure Weibull gamma-frailty survival model and its application to exploring the prognosis factors of neuroblastoma. Hiroshima J Med Sci, 58(1):25-35.
11. Karamoozian A, Baneshi MR, Bahrampour A (2021). Bayesian mixture cure rate frailty models with an application to gastric cancer data. Stat Methods Med Res, 30(3):731-746.
12. Corbiere F, Commenges D, Taylor JM, Joly P (2009). A penalized likelihood approach for mixture cure models. Stat Med, 28(3):510-24.
13. Chib S, Greenberg E (1995). Understanding the metropolis-hastings algorithm. The American Statistician, 49(4):327-35.
14. Gamerman D, Lopes HF (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference. CRC press.
15. Hosseinnataj A, RezaBaneshi M, Bahrampour A (2020). Mortality risk factors in patients with gastric cancer using Bayesian and ordinary Lasso logistic models: a study in the Southeast of Iran. Gastroenterol Hepatol Bed Bench, 13(1):31-36.
16. Jackman S (2000). Estimation and inference via Bayesian simulation: An introduction to Markov chain Monte Carlo. American Journal of Political Science, 44(2):375-404.
17. Khazaei S, Yaseri M, Nematollahi S, et al (2018). Survival rate and predictors of mortality among hemodialysis patients in West of Iran, 1996–2015. Int J Prev Med, 9:113.
18. Bae E, Cho H-J, Shin N, et al (2016). Lower serum uric acid level predicts mortality in dialysis patients. Medicine (Baltimore), 95(24):e3701.
19. Park C, Obi Y, Streja E, et al (2017). Serum uric acid, protein intake and mortality in hemodialysis patients. Nephrol Dial Transplant, 32(10):1750-7.
20. Beberashvili I, Erlich A, Azar A, et al (2016). Longitudinal study of serum uric acid, nutritional status, and mortality in maintenance hemodialysis patients. Clin J Am Soc Nephrol, 11(6):1015-23.
21. Kim S, Molnar MZ, Fonarow GC, et al (2016). Mean platelet volume and mortality risk in a national incident hemodialysis cohort. Int J Cardiol, 220:862-70.
22. Zhao X, Niu Q, Ni Z, et al (2021). Mortality Risk Factors in the China Dialysis Outcomes and Practice Patterns Study (DOPPS). Sci Rep, 11(1):873.
23. Hur I, Choi SJ, Kalantar-Zadeh K (2017). Serum uric acid and mortality risk among maintenance hemodialysis patients. Kidney Res Clin Pract, 36(4):302-304.
24. Suliman ME, Johnson RJ, García-López E, et al (2006). J-shaped mortality relationship for uric acid in CKD. Am J Kidney Dis, 48(5):761-71.
25. Zawada AM, Carrero JJ, Wolf M, et al (2020). Serum uric acid and mortality risk among hemodialysis patients. Kidney Int Rep, 5(8):1196-206.
26. Lee SK, Lee AL, Winters TJ, et al (2009). Low serum uric acid level is a risk factor for death in incident hemodialysis patients. Am J Nephrol, 29(2):79-85.
2. System USRD (2018). 2018 USRDS annual data report: epidemiology of kidney disease in the United States. https://www.niddk.nih.gov/about-niddk/strategic-plans-reports/usrds
3. Seyedghasemi NS BA, Etminan A, Haghdoost A, Baneshi MR (2020). Estimating the Loss in Expectation of Life and Relative Survival Rate among Hemodialysis Patients in Iran. J Res Health Sci, 20(3):e00487.
4. Robinson BM, Akizawa T, Jager KJ, et al (2016) . Factors affecting outcomes in patients reaching end-stage kidney disease worldwide: differences in access to renal replacement therapy, modality use, and haemodialysis practices. Lancet, 388(10041):294-306.
5. Martinez EZ, Achcar JA, Jácome AA, Santos JS (2013). Mixture and non-mixture cure fraction models based on the generalized modified Weibull distribution with an application to gastric cancer data. Comput Methods Pro-grams Biomed,112(3):343-55.
6. Leão J, Leiva V, Saulo H, Tomazella V (2018). Incorporation of frailties into a cure rate regression model and its diagnostics and application to melanoma data. Stat Med, 37(29):4421-40.
7. Peng Y, Taylor JM (2011). Mixture cure model with random effects for the analysis of a multi‐center tonsil cancer study. Stat Med, 30(3):211-23.
8. de Souza D, Cancho VG, Rodrigues J, Balakrishnan N (2017). Bayesian cure rate models induced by frailty in survival analysis. Stat Methods Med Res, 26(5):2011-28.
9. Nikaeen R, Khalilian A, Bahrampour A (2017). Determining the effective factors on gastric cancer using frailty model in South-East and North of Iran. Iran J Health Sci, 5(3): 35-48.
10. Dokhi M, Ohtaki M, Hiyama E (2009). A cure Weibull gamma-frailty survival model and its application to exploring the prognosis factors of neuroblastoma. Hiroshima J Med Sci, 58(1):25-35.
11. Karamoozian A, Baneshi MR, Bahrampour A (2021). Bayesian mixture cure rate frailty models with an application to gastric cancer data. Stat Methods Med Res, 30(3):731-746.
12. Corbiere F, Commenges D, Taylor JM, Joly P (2009). A penalized likelihood approach for mixture cure models. Stat Med, 28(3):510-24.
13. Chib S, Greenberg E (1995). Understanding the metropolis-hastings algorithm. The American Statistician, 49(4):327-35.
14. Gamerman D, Lopes HF (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference. CRC press.
15. Hosseinnataj A, RezaBaneshi M, Bahrampour A (2020). Mortality risk factors in patients with gastric cancer using Bayesian and ordinary Lasso logistic models: a study in the Southeast of Iran. Gastroenterol Hepatol Bed Bench, 13(1):31-36.
16. Jackman S (2000). Estimation and inference via Bayesian simulation: An introduction to Markov chain Monte Carlo. American Journal of Political Science, 44(2):375-404.
17. Khazaei S, Yaseri M, Nematollahi S, et al (2018). Survival rate and predictors of mortality among hemodialysis patients in West of Iran, 1996–2015. Int J Prev Med, 9:113.
18. Bae E, Cho H-J, Shin N, et al (2016). Lower serum uric acid level predicts mortality in dialysis patients. Medicine (Baltimore), 95(24):e3701.
19. Park C, Obi Y, Streja E, et al (2017). Serum uric acid, protein intake and mortality in hemodialysis patients. Nephrol Dial Transplant, 32(10):1750-7.
20. Beberashvili I, Erlich A, Azar A, et al (2016). Longitudinal study of serum uric acid, nutritional status, and mortality in maintenance hemodialysis patients. Clin J Am Soc Nephrol, 11(6):1015-23.
21. Kim S, Molnar MZ, Fonarow GC, et al (2016). Mean platelet volume and mortality risk in a national incident hemodialysis cohort. Int J Cardiol, 220:862-70.
22. Zhao X, Niu Q, Ni Z, et al (2021). Mortality Risk Factors in the China Dialysis Outcomes and Practice Patterns Study (DOPPS). Sci Rep, 11(1):873.
23. Hur I, Choi SJ, Kalantar-Zadeh K (2017). Serum uric acid and mortality risk among maintenance hemodialysis patients. Kidney Res Clin Pract, 36(4):302-304.
24. Suliman ME, Johnson RJ, García-López E, et al (2006). J-shaped mortality relationship for uric acid in CKD. Am J Kidney Dis, 48(5):761-71.
25. Zawada AM, Carrero JJ, Wolf M, et al (2020). Serum uric acid and mortality risk among hemodialysis patients. Kidney Int Rep, 5(8):1196-206.
26. Lee SK, Lee AL, Winters TJ, et al (2009). Low serum uric acid level is a risk factor for death in incident hemodialysis patients. Am J Nephrol, 29(2):79-85.
| Files | ||
| Issue | Vol 53 No 9 (2024) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/ijph.v53i9.16464 | |
| Keywords | ||
| Weibull distribution Long-term survival Mixture cure Gamma frailty Bayesian inference | ||
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How to Cite
1.
Bahrampour A, Baneshi MR, Karamoozian A, Sadat Seyedghasemi N, Etminan A, Eghbalian M. Long-Term Survival of Patient with End-Stage Renal Disease Using Bayesian Mixture Cure Rate Frailty Models. Iran J Public Health. 2024;53(9):2113-2120.
