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Survival analysis of gastric cancer patients using Cox model: a five year study
Biglarian A, Hajizadeh E, Kazemnejad A, Zali M,
Volume 67, Issue 5 (8-2009)
Abstract

Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 Background: Gastric cancer is the second most common cancer and known as the second cause of death due to cancers worldwide. Adenocarcinoma is the most fatal cancer in Iran and a patient with this kind of cancer, has a lower lifetime than others. In this research, the survival of patients with gastric carcinoma who were registered at Taleghani Hospital, were studied.
Methods:  291 patients with Gastric carcinoma who had received care, chemotherapy or chemoradiotherapy, at Taleghani Hospital in Tehran from 2002 to 2007 were studied as a historical cohort. Their survival rates and its relationship with 12 risk factors were assessed.
Results:  Of the 291 patients with Gastric carcinoma, 70.1 percent were men and others (29.9%) were women. The mean age of men was 62.26 years and of women was 59.32 years at the time of diagnosis. Most of patients (93.91%) were advanced stage and metastasis. The Cox proportional hazards model showed that age at diagnosis, tumor stage and histology type with survival time had significant relationships (p=0.039, p=0.042 and p=0.032 respectively).
Conclusion: The five-year survival rate and median lifetime of gastric cancer patients who underwent chemotherapy or chemoradiotherapy are very low and seems that one of the important reasons for this situation is delayed diagnosis. The scheme of public education about the early warning signs of the disease and diagnosis and administration of periodic examinations is unavoidable.


Evaluation of risk factors of recurrence of hodgkin's lymphoma using random survival forest and comparison with cox regression model
Zeinab Asakereh, Elham Maraghi, Bijan Keikhaei, Amal Saki Malehi ,
Volume 80, Issue 7 (10-2022)
Abstract
Background: In many studies, Cox regression was used to assess the important factors that affect the survival of cancer patients based on demographic and clinical variables. The aim of this study was to determine the factors affecting the survival of patients with Hodgkin's lymphoma using the random survival forest (RSF) method and compare it with the Cox model.
Methods: In this retrospective cohort study, all patients with Hodgkin's lymphoma who were referred to the Oncology and Hematology Center of Ahvaz Shafa Hospital from March 2000 to February 2010 were included. The survival time was calculated from diagnosis to the first recurrence event date (based on month). To assess the process of the disease, demographic characteristics and disease-related variables (including disease stage, chemotherapy, site of lymph involvement, etc.) were extracted from the records of 387 patients with Hodgkin's lymphoma. To investigate the prognostic factors that affect the recurrence of disease the Cox model and RSF were implemented. Moreover, their performance based on the C-index, IBS, and predictor error rate of the two models were compared Data analysis was implemented by using R4.0.3 software (survival and RandomForestSRC packages).
Results: The results of the Cox model showed that LDH (P=0.001) and classical lymphoma classification (P<0.001) were associated with an increased risk of relapse in patients. However, the results of the RSF model showed that the important variables affecting the recurrence of disease were the stage of disease, chemotherapy, classical lymphoma classification, and hemoglobin, respectively. Also, the RSF model showed a higher (c-index=84.9) than the Cox model (c-index=57.6). Furthermore, the RSF model revealed a lower error rate predictor (0.09) and IBS index (0.175) than the Cox model. So, RSF has performed better than the Cox model in determining prognostic factors based on the suitability indicators of the model.
Conclusion: The RSF has high accuracy than the Cox model when there is a high number of predictors and there is collinearity. It can also identify the important variables that affect the patient's survival.

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