Applying Cox Regression in Time to Event Data

Survival analysis is normally used to describe the analysis of data that depend on the time between constant starting point until the occurrence of specific event or endpoint. Censoring and non-normality are important aspects of the survival analysis data, these aspects generate difficulties when trying to analyze data using traditional statistical models. In this study, the Kaplan-Meier estimator is used to estimate the survival function. Also, Log-Rank test is used to compare between groups using the stages of the illness as comparing factors. Finally, multivariable Cox's regression is used to determine the factors that affect the survival time and assess the relative risks. The results show that there is non-significant difference in the survival curves of male and female patients and patients in stage IV had 4.53 times hazard than patients in stage I.