Parametrik hızlandırılmış başarısızlık (ölüm) zamanı (HBZ) modelleri ile erken evre meme kanseri hastalarında uygulama

Abstract

Purpose: Cox Proportional Hazard (CPH) model is the most commonly used multivariate regression model for survival analysis. However, it is not always possible to obtain the proportional hazard (PH) assumption. In this case, Parametric Accelerated Failure Time (AFT) models may be applied. In this study, AFT and CPH models were applied to patients with early stage breast cancer and the results were compared. Materials and Methods: Retrospective survival data of 697 patients with early stage breast cancer were analyzed in this study. 13 independent variables and overall survival time as the dependent variable were tested. Multiple CPH regression analysis were performed for significant variables . For the AFT model, hazard functions of the data belonging to log-normal, log-logistic, Weibull and of exponential distributions were examined. Results: Although age groups, tumor grade, neural invasion and extra capsule involvement did not provide the assumption of COH, when statistically significant 9 independent variables were applied to the multivariate COX model, metastatic lymph nodes and menopausal status were found to be significant. According to AIC value and hazard function distributions, the most appropriate AFT model was with log-logistic. AFT model with log-logistic regression, number of metastatic lymph nodes, menopausal status and tumor size were found to be significant. Conclusion: In the literature, the CPH model is one of the most commonly used survival models. In cases where the assumption of the proportionality of hazards were violated; it may be more appropriate to use alternative models such as the AFT regression model.