Prognostic Factors Breast Cancer survival

Introduction: Quantile regression models quantile function of failure time and investigates the covariate effects in different quantiles. In this model, the covariate effects can be changed for patients with different risks and is a flexible model for controlling the heterogeneity of covariate effects. Therefore it is a flexible model for controlling the heterogeneity of covariates. We aimed to investigate the role of prognostic factors on breast cancer survival with quantile regression model. Methods: In this historical cohort study, the data set was collected as a secondary data and it contains information on ۴۱۳ breast cancer patients, between ۱۹۹۷ – ۲۰۱۳ who completed the follow-up period. Age, size of tumor, the number of lymph nodes involved, grade of malignancy and type of surgery, chemotherapy, radiotherapy, recurrence and metastasis were prognosis factors considered in this study. The data of this study were analyzed using quantile regression in ۲۰th percentile of survival time. Results: During the follow-up, ۱۱۲(۲۱.۷%) patients died due to breast cancer. The mean (standard deviation) of age at diagnosis is ۴۶.۷۳ (۱۲.۶۳). ۳۳۸ (۸۱.۸%) of patients were operated by the MRM, and ۷۵ (۱۸.۲%) by the BCS method, respectively. In ۲۰th percentile of survival time, age, recurrence and metastasis size of tumor, lymph nodes involved, grade and chemotherapy, radiotherapy were significantly prognostic factors on breast cancer survival. Conclusion: This study showed that quantile regression model with more straightforward interpretation can overcome complexity of interpreting hazard ratio in cox models.

کلیدواژه ها:

breast cancer ، quantile regression ، survival