A Unified Competing Risks Cure Model

Music City Center 

Cancer remains the second most prevalent cause of death in the United States, claiming 605,213 lives in 2021, surpassing COVID-19 fatalities. The mortality rate from cancer saw a continual decline from 2019 to 2020, dropping by 1.5%, marking a significant 33% decrease since 1991. This ongoing improvement primarily mirrors advancements in treatment, enabling patients to achieve clinical remission and recovery. Now, a cancer patient is simultaneously exposed to the risk of primary cancer as well as other risks, such as other cancer(s) or other disease, leading to a competing risks scenario. Analysis of survival data under competing risks and presence of cured patients both have been extensively studied individually, but there is limited work in the current literature that models the possibility of cure from one risk in the presence of competing risks. Moreover, such a model should allow for the possibility of cure from the cause-specific risk of the primary cancer, however, the overall survival probability should eventually approach zero, thereby incorporating the prevalent belief of eventual failure with certainty. In this talk, I will present a novel unified competing risks cure model, based on the cause-specific hazard approach, that satisfies the aforementioned desired properties. To find the maximum likelihood estimates of the model parameters, I will discuss a computationally efficient expectation maximization algorithm. To demonstrate the performance of the proposed model and estimation method, I will present results of a comprehensive simulation study. Finally, I will illustrate an application using a breast cancer data from the SEER cancer database.