13/11/2026 - 14:30 - ISBA C.115 - 
 

Kellie Archer 

(The Ohio State University) 

Will give a presentation on : 

High-Dimensional Mixture Cure Models for Time-to-Event Data 

Abstract: 
Medical breakthroughs in recent decades have led to cures for many diseases including cancer.  For example, various groups have shown that advances in therapy for leukemia and myelodysplastic syndrome have increased overall survival rates and some groups have identified factors related to long-term survival, defined as survival exceeding three years, which included that patients treated on newer treatment regimens were more likely to be long-term survivors. In fact, some argue that these improved outcomes indicate that some AML patients can be considered “potentially cured.” The mixture cure model (MCM) is a time-to-event model that is used when a cured fraction exists. MCMs assume the population consists of two subgroups, those cured will not experience the event of interest and those susceptible to the event of interest. Therefore, “cured” can be considered synonymous with attaining long-term relapse-free survival. Thus, there are two regression components in MCMs which permit identification of features associated with cure and/or latency of susceptible patients. Many researchers have sought to identify a prognostic model for a time-to-event outcome. When the covariate space is high-dimensional, as in the case with gene expression data, typically penalized Cox proportional hazards (PH) models are fit. However, when a dataset includes a cured fraction, MCMs are more appropriate than Cox PH models. In these scenarios, Cox models often yield inaccurate hazard and survival estimates due to violations of the proportional hazards assumption. Despite their utility, robust methods for fitting MCMs to high-dimensional data remain scarce. In this talk, I will present our recent work extending MCMs to effectively handle high-dimensional covariate spaces, offering a more accurate prognostic tool for modern high-dimensional datasets.