On the Importance of Evaluating Cure Model Appropriateness: Motivation and Methodology

Music City Center 

Mixture cure models are a class of time-to-event data models for populations with long-term survivors. They have been applied in diverse areas of clinical research (e.g., pediatric oncology) and non-clinical research (e.g., credit risk and recidivism). These models require important assumptions beyond those for conventional time-to-event analysis methods: Two key assumptions are the existence of a non-zero proportion of long-term survivors (or "cure fraction") in the population and sufficient follow-up in the data to identify the cure fraction. Researchers have shown that violations to these assumptions can lead to inappropriate conclusions, so the valid application of these models requires evaluating the appropriateness of cure models for each intended analysis. Historically, methods for evaluating cure model appropriateness have been criticized for poor operating characteristics, but recently, several novel methods and improvements have been proposed to address historical weaknesses. In this talk, we will motivate the necessity for evaluating cure model appropriateness, discuss recent methodological developments, and suggest future work.