Research in Medicine

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osmani F, hajizadeh E, Rasekhi A, Akbari M E. Determination of Effective Factors on Recurrences in Patients with Breast Cancer by Joint Modeling of Recurrences and Death. Research in Medicine 2020; 44 (1) :276-281
URL: http://pejouhesh.sbmu.ac.ir/article-1-1901-en.html

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.