Modeling for Competing Risk Regression in Survival Analysis with Application in Breast Cancer Disease

Abstract

Competing risks regression is an essential component of survival analysis, particularly when there are several possible event types that prevent additional events from being seen. This paper investigates the modelling and analysis of competing risks in time-to-event data. The study was applied to a sample size of (4420) patients with Breast cancer. The data was obtained from Rizgari Hospital in the period from 1^st of January 2019 to 31^st of August 2024.

In survival analysis, a competing risk occurs when an event (such as death from a cause other than breast cancer) precludes the occurrence of the primary event of interest (e.g., breast cancer-specific mortality).

The aimf this study is to model the Competing Risk Regression, which treatment effect or risk on our patients which classic survival models such as the Kaplan-Meier estimator may not sufficiently address, in order to assess the likelihood of a particular occurrence in the presence of conflicting hazards. Based on the opinions of the doctors the Age and Family History factors that the risk variable on breast cancer disease, our results show that laterality, family history  as a significant predictor in two models ( sub distribution hazard model and cause specific hazard model)  age is significant effects in sub distribution hazard and hormone presents as a significant predictor in cause specific hazard model. Furthermore, we compare sub-distribution hazard models with cause-specific hazard models by using AIC and BIC measures so as to determine which model better matches with our data, and we find that the cause-specific hazard models a more reliable match to the data. This study emphasizes how crucial it is to choose the right models for analyzing Competing risks in order to agreement precise predictor and outcomes that are easy to understand,  especially in biomedical research. To support practical application, packages that implement competing risks analysis like STATA and NCSS.