Volume 31, Issue 10 (January 2020)
Studies in Medical Sciences 2020, 31(10): 802-812 |
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MohammadSharifi A, Hajizadeh E, baghestani A, Haji Fathali A. MIXTURE AND NON-MIXTURE CURED MODELS IN SURVIVAL ANALYSIS OF LEUKEMIA PATIENTS: A COHORT STUDY. Studies in Medical Sciences 2020; 31 (10) :802-812
URL: http://umj.umsu.ac.ir/article-1-5288-en.html
URL: http://umj.umsu.ac.ir/article-1-5288-en.html
Professor of Statistics, Department of Statistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran. (Corresponding Author) , hajizadeh@modares.ac.ir
Abstract: (5016 Views)
Background & Aims: The present study analyzed the factors affecting the survival time of patients with leukemia from diagnosis to death, taking into account the cure rate. The aim of the present study was to apply two models of mixed and unmixed healing in the data of patients with leukemia who received bone marrow transplantation.
Materials & Methods: In this descriptive-analytical (Cohort) research, the data of 351 patients who were referred to Taleghani Hospital in Tehran affiliated to Shahid Beheshti University of Medical Sciences and received bone marrow transplantation due to leukemia were examined. Patients received bone marrow transplantation between 2007 and 2014 and were followed up until 2016. In this study, the cured models of Bernoulli-Weibull blended (taking into account the Bernoulli distribution for latent variables and the Weibull distribution for survival time) and the Poisson -Weibull blended cured model (considering the Poisson distribution for latent time variables for hiding and distribution and distribution for survival time) were fitted to the data.
Results: In this study, 351 patients, 197 males (56.1%) and 154 females (43.9%), were studied in which 67 patients (19.1%) died. Among the significant variables of recurrence after transplantation, recurrence before transplantation, hemoglobin, type of transplant, age, body mass, blood type and type of diagnosis, variables age (p=0.01), recurrence after transplantation (0.03) and type of transplant (p=0.03) are among the variables affecting the survival time of leukemia patients. In the mixture cured model of Bernoulli-Weibull and the variables of age (p=0.004), recurrence after transplantation (p=0.013) and type of diagnosis (p<0.008) were variables affecting the survival time of leukemia patients in the non-mixture cured model Poisson -Weibull.
Conclusion: Patients with autologous bone marrow transplantation under the age of 30 have a better chance of survival, and also the non-mixture cured model has a better outcome than the mixture cured model.
Materials & Methods: In this descriptive-analytical (Cohort) research, the data of 351 patients who were referred to Taleghani Hospital in Tehran affiliated to Shahid Beheshti University of Medical Sciences and received bone marrow transplantation due to leukemia were examined. Patients received bone marrow transplantation between 2007 and 2014 and were followed up until 2016. In this study, the cured models of Bernoulli-Weibull blended (taking into account the Bernoulli distribution for latent variables and the Weibull distribution for survival time) and the Poisson -Weibull blended cured model (considering the Poisson distribution for latent time variables for hiding and distribution and distribution for survival time) were fitted to the data.
Results: In this study, 351 patients, 197 males (56.1%) and 154 females (43.9%), were studied in which 67 patients (19.1%) died. Among the significant variables of recurrence after transplantation, recurrence before transplantation, hemoglobin, type of transplant, age, body mass, blood type and type of diagnosis, variables age (p=0.01), recurrence after transplantation (0.03) and type of transplant (p=0.03) are among the variables affecting the survival time of leukemia patients. In the mixture cured model of Bernoulli-Weibull and the variables of age (p=0.004), recurrence after transplantation (p=0.013) and type of diagnosis (p<0.008) were variables affecting the survival time of leukemia patients in the non-mixture cured model Poisson -Weibull.
Conclusion: Patients with autologous bone marrow transplantation under the age of 30 have a better chance of survival, and also the non-mixture cured model has a better outcome than the mixture cured model.
Keywords: Survival analysis, Mixture and non-mixture cured model, Leukemia, Bone marrow transplantation
Type of Study: Research |
Subject:
Hematology
References
1. Nasseri Q. Cancers and Prevention strategies. Iran JEpidemiol 2005;1(1):1-8. [URL]
2. cancercenter. Blood cancers - types and treatmentoptions [Internet]. Cancer Treatment Centers ofAmerica. 2018 [cited 2021 Jan 28]. Available from:https://www.cancercenter.com/blood-cancers [URL]
3. janssen. About Blood Cancer [Internet]. JanssenEMEA. [cited 2021 Jan 28]. Available from:https://www.janssen.com/emea/about-blood-cancer
4. Koohi F, Shamlou R, Eslami S, Ghojogh ZM, KorY, Rafiemanesh H. Leukemia in Iran: epidemiologyand morphology trends. Asian Pacific Journal ofCancer Prevention 2015;16(17):7759-63. [DOI:10.7314/APJCP.2015.16.17.7759] [PMID]
5. UPMC Hillman Cancer Center. What Is Leukemia?Facts About Blood Cancer [Internet]. APIIntegrations. 2015 [cited 2021 Jan 28]. Availablefrom: https://wordpress-harvestprod.azurewebsites.net/pixel/
6. Kim S, Chen M-H, Dey DK. A new thresholdregression model for survival data with a curefraction. Lifetime data analysis 2011;17(1):101-22. [DOI:10.1007/s10985-010-9166-9] [PMID] [PMCID]
7. Kim S, Xi Y, Chen M-H. A new latent cure ratemarker model for survival data. The Annals ofapplied statistics 2009;3(3):1124-46. [DOI:10.1214/09-AOAS238]
8. Rahimzadeh M, Hajizadeh E, Eskandari F. Nonmixturecure correlated frailty models in Bayesianapproach. Journal of Applied Statistics2011;38(8):1651-63. [DOI:10.1080/02664763.2010.515966]
9. Rahimzadeh M HE. Bivariate analysis of long-termsurvival correlated frailty model [URL]
10. Boag JW. Maximum likelihood estimates of theproportion of patients cured by cancer therapy. J RStat Soc Series B Stat Methodol 1949;11(1):15-53. [DOI:10.1111/j.2517-6161.1949.tb00020.x]
11. Tsodikov AD, Yakovlev AY, Asselain B. Stochasticmodels of tumor latency and their biostatisticalapplications. World Scientific; 1996. [DOI:10.1142/2420]
12. Akhlaghi AA, Najafi I, Mahmoodi M, Shojaee A,Yousefifard M, Hosseini M. Survival analysis ofIranian patients undergoing continuous ambulatoryperitoneal dialysis using cure model. J Res HealthSc. 2013;13(1):32-6. [PMID]
13. Atoof F, Mahmoudi M, Zeraati H, RahimiForoushani A, Moravveji SA. Survival analysis ofgastric cancer patients refering to Emam-Khomeinihospital using Weibull cure model. FEYZ2011;14(4):405-13. [URL]
14. Baghestani AR, Moghaddam SS, Majd HA, AkbariME, Nafissi N, Gohari K. Application of a nonmixturecure rate model for analyzing survival ofpatients with breast cancer. Asian Pac J Cancer Prev2015;16(16):7359-63. [DOI:10.7314/APJCP.2015.16.16.7359] [PMID]
15. Ghadimi M, Rasouli M, Mahmoodi M, MohammadK, Zeraati H. A comparative study of impact ofpersonal factors on survival of patients withEsophageal Cancer using Weibull or Log-NormalCure Models. Hakim Res J 2011; 14(1):41-9. [Google Scholar]
16. Jafari-Koshki T, Mansourian M, Mokarian F.Exploring factors related to metastasis free survivalin breast cancer patients using Bayesian curemodels. Asian Pac J Cancer Prev 2014;15(22):9673-8. [DOI:10.7314/APJCP.2014.15.22.9673] [PMID]
17. Rasouli M, Ghadimi MR, Mahmoodi M,Mohammad K, Zeraati H, Hosseini M. Survivalanalysis of patients with esophageal cancer usingparametric cure model. Asian Pac J Cancer Prev2011;12(9):2359-63. [Google Scholar]
18. Zinzani PL, Bendandi M, Visani G, Gherlinzoni F,Frezza G, Merla E, et al. Adult lymphoblasticlymphoma: clinical features and prognostic factorsin 53 patients. Leukemia & lymphoma 1996;23(5-6):577-82. [DOI:10.3109/10428199609054867] [PMID]
19. Gupta V, Eapen M, Brazauskas R, Carreras J, AljurfM, Gale RP, et al. Impact of age on outcomes afterbone marrow transplantation for acquired aplasticanemia using HLA-matched sibling donors.Haematologica 2010;95(12):2119-25. [DOI:10.3324/haematol.2010.026682] [PMID] [PMCID]
20. Popplewell L, Forman S. Is there an upper age limitfor bone marrow transplantation? Bone MarrowTransplant 2002;29(4):277-84. [DOI:10.1038/sj.bmt.1703382] [PMID]
21. Saffar A, Rahgozar M, Shahi F, Biglarian A.Survival analysis of acute myeloid leukemia. RaziJournal of Medical Sciences 2015; 22 (134):41-8. [URL]
22. Breccia M, Mazzarella L, Bagnardi V, DisalvatoreD, Loglisci G, Cimino G, et al. Increased BMIcorrelates with higher risk of disease relapse anddifferentiation syndrome in patients with acutepromyelocytic leukemia treated with the AIDAprotocols. Blood 2012;119(1):49-54. [DOI:10.1182/blood-2011-07-369595] [PMID]
23. Reshef R, Hexner EO, Loren AW, Frey NV,Stadtmauer EA, Luger SM, et al. Early donorchimerism levels predict relapse and survival afterallogeneic stem cell transplantation with reducedintensityconditioning. Biol Blood MarrowTransplant 2014;20(11):1758-66. [DOI:10.1016/j.bbmt.2014.07.003] [PMID] [PMCID]
24. Wingard JR, Majhail NS, Brazauskas R, Wang Z,Sobocinski KA, Jacobsohn D, et al. Long-termsurvival and late deaths after allogeneichematopoietic cell transplantation. J Clin Oncol2011;29(16):2230. [DOI:10.1200/JCO.2010.33.7212] [PMID] [PMCID]
25. Cornell R, Kassim A. Evolving paradigms in thetreatment of relapsed/refractory multiple myeloma:increased options and increased complexity. BoneMarrow Transplant 2016;51(4):479-91. [DOI:10.1038/bmt.2015.307] [PMID] [PMCID]
26. Kumar SK, Dispenzieri A, Lacy MQ, Gertz MA,Buadi FK, Pandey S, et al. Continued improvementin survival in multiple myeloma: changes in earlymortality and outcomes in older patients. Leukemia2014;28(5):1122-8. [DOI:10.1038/leu.2013.313] [PMID] [PMCID]
27. Shokouhi S, Bray S, Bakhtiyari S, Sayehmiri K,Alimoghadam K, Ghavamzadeh A. Effects ofaGVHD and cGVHD on survival rate in patientswith acute myeloid leukemia after allogeneic stemcell transplantation. Int J Hematol Oncol Stem CellRes 2015;9(3):112. [URL]
28. Lazarus H, Loberiza F, Zhang M, Armitage J, BallenK, Bashey A, et al. Autotransplants for Hodgkin'sdisease in first relapse or second remission: a reportfrom the autologous blood and marrow transplantregistry (ABMTR). Bone Marrow Transplant2001;27(4):387-96. [DOI:10.1038/sj.bmt.1702796] [PMID]
29. Barrett AJ, Horowitz MM, Gale RP, Biggs JC,Camitta BM, Dicke KA, et al. Marrowtransplantation for acute lymphoblastic leukemia:factors affecting relapse and survival. Blood1989;74(2):862-71. [DOI:10.1182/blood.V74.2.862.bloodjournal742862] [PMID]
30. Bishop M, Logan B, Gandham S, Bolwell B, CahnJ, Lazarus H, et al. Long-term outcomes of adultswith acute lymphoblastic leukemia after autologousor unrelated donor bone marrow transplantation: acomparative analysis by the National Marrow DonorProgram and Center for International Blood andMarrow Transplant Research. Bone MarrowTransplant 2008;41(7):635-42. [DOI:10.1038/sj.bmt.1705952] [PMID] [PMCID]
31. Gonçalves TL, Benvegnú DM, Bonfanti G. Specificfactors influence the success of autologous andallogeneic hematopoietic stem cell transplantation.Oxidative medicine and cellular longevity2009;2(2):82-7. [DOI:10.4161/oxim.2.2.8355] [PMID] [PMCID]
32. Kim HT, Zhang M-J, Woolfrey AE, Martin AS,Chen J, Saber W, et al. Donor and recipient sex inallogeneic stem cell transplantation: what reallymatters. Haematologica 2016;101(10):1260-6. [DOI:10.3324/haematol.2016.147645] [PMID] [PMCID]
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