<p>Survival analysis is a set of statistical methods or tools for analyzing time-to-event data. The Cox proportional hazards model is widely employed in survival analysis to explore the relationship between covariates and the hazard rate. Covariates can influence the hazard function, causing it to vary, which may lead to the occurrence of a change point. This study centers around the estimation of changepoint in the Cox proportional hazard model, involving an examination of three distinct hazard models. We utilize the maximum likelihood method to estimate change points within these proposed hazard models. Through a thorough Monte Carlo simulation study, we assess the consistency and performance of our methods as sample size and censorship percentages vary. The chronic granulomatous disease (CGD) dataset is used as a practical application and results showed that the proposed models are effective in estimating the change point.</p>