| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
