Showing 3 results for Survival Analysis
Mitra Rahimzadeh Kiwi1, Ebrahim Hajizadeh, Sepehr Feyzi,
Volume 34, Issue 2 (7-2010)
Abstract
Abstract
Background: Although corneal graft may be rejected by the immune system of the recipient it remains as the most successful operation as compared to transplantation of other tissues. Since most patient do not reject the grafts, those who do are in the minority. This study was carried out to assess the usefulness of the cure frailty model for determining the significance of risk factors associated with the rejection of bilateral corneal transplants in patients with keratoconus.
Methods: Patients with keratoconus receiving bilateral corneal transplants were included in the study. For analysis of the time of bilateral graft rejection in the keratoconus disease we used the cure frailty model and the promotion time cure frailty and used the Cox frailty model for comparison. For estimating the parameters we used the Bayesian approach. For comparison of proposed models we used the Deviance Information Criteria (DIC).
Results: 238 individuals received corneal transplants during the study period and 22.7 percent experienced graft rejection. Mean and median of graft rejection time was 13.5±22.8 and 6.9 months respectively. Vascularization and old age were important risk factors for graft rejection. In the cure frailty model the cure rate in the patients with vascularization were 34 percent versus 75 percent without vascularization. In the time promotion cure frailty model the cure rates in cases with vascularization were 32 percent and without vascularization were 70%. The cure models that include the parameter for cure rate in the comparison to the Cox frailty model that do not have parameters for cure rate are better for data analysis.
Conclusion: For analysis of survival data in which selection of patients is highly selected using the cure model gives more accurate results.
Keywords: Bilateral corneal Graft, keratoconus, Survival analysis, Cure Frailty model.
Fahimeh Ghasemi, Aliakbar Rasekhi, Shahpar Haghighat,
Volume 42, Issue 4 (12-2018)
Abstract
Background and Aim: Therapies for many of diseases especially cancers have been improved significantly in the recent year, so there have been an increased number of patients who do not experience mortality. In analysis of these disease, cured models is used instead of usual survival models. Weibull model and its generalized version beta Weibull Poisson (BWP), are flexible models in cure models and are used in this study to analyze braset cancer patients data.
Materials and Methods: The data of this cohort study are from patients with breast cancer, were gathered during 1997 to 2006 to the Motamed Cancer Institute in Tehran, and were followed up from 2013 to 2017. A random sample of size 270 patients was selected and individual characteristics evaluated. The data were analyzed using Stata 12 and R3.4.1 software and the significance level set at 0.05.
Results: The results showed that 43 (15.9%) of patients deaths after treatment. One, three and five year Cure of the patients was 0.99, 0.87 and 0.83 respectively. The results of this study showed that PBW Non-Mixture Cure Model (AIC=427) has better fit than weibull (AIC=593). Based on this model, variables of tumor size greater than 5,(P<0.001) and, tumor grade 3,(P<0.001) are factors affecting of patients cured; and the cure fraction was estimated to be 80%.
Conclusion: By study the effect of factors affecting the occurrence of death, considering the unknown number of causes and using BWP models, the cancer trend can analyzed better and more accurate information can be available to researchers.
Freshteh Osmani, Ebrahim Hajizadeh, Aliakbar Rasekhi, Mohammad Esmaeil Akbari,
Volume 44, Issue 1 (3-2020)
Abstract
Abstract
Background: Nowadays, cancer is one of the most important medical problems. Breast cancer is also considered as the most common cancer of women: its prevalence has risen throughout the world in recent centuries and people can experience recurrent events that sometimes accompany with a terminal event. In the present study, using a frailty model we tried to model recurrent event in the precence of terminal event (death) in breast cancer patients.
Materials and Methods: In the current retrospective survival study, 342 patients with breast cancer registered in the cancer research center of Shohada-e-Tajrish Hospital were examined. The Liu model was used to joint modeling of recurrent events and terminal events, in which a shared frailty with Gamma distribution was used. Estimation of the parameters was done by the penelized maximum likelihood method and the frailtypack package in the R software version 3.4.1 was used to implement the model and analyzed the data. Significance level was set at 0.05.
Results: In our cohort study, 342 women with breast cancer were studied. A univariate and multivariate analyses was performed for these patients. Of these, 78 cases (25.4%) had recurrence events, and 225 patients (74.6%) were censored. The obtained results of joint frailty model indicated that the relative risk of relapse in patients with first-degree familial history was 36% higher than that of other people (P <0.05). Relative risk of relapse in patients with stage 3 disease was 19% more than other stages and also the relative risk of relapse in patients with chemotherapy were 2.5 times higher than those without chemotherapy.
Conclusion: It seems that by investigating the effects of factors affecting the occurrence of recurring events with death with regard to unknown factors, such as modeling, we can simultaneously simulate the probabilistic nature of the disease and provide more accurate information to the researchers.
