A Generalized Mixture Cure Model Incorporating Known Cured Individuals

Abstract: The Mixture Cure (MC) models constitute an appropriate and easily interpretable method when studying a time-to-event variable in a population comprised of both susceptible and cured individuals. In literature, those models usually assume that the latter are unobservable. However, there are cases in which a cured individual may be identified. For example, when studying the distant metastasis during the lifetime or the miscarriage during pregnancy, individuals that have died without a metastasis or have given birth are certainly non-susceptible. The same also holds when studying the x-year overall survival or the death during hospital stay. Common MC models ignore this information and consider them all censored, thus yielding in risk of assigning low immune probabilities to cured individuals. In this study, we consider a MC model that incorporates known information on cured individuals, with the time to cure identification being either deterministic or stochastic. We use the expectation-maximization algorithm to derive the maximum likelihood estimators. Furthermore, we compare different strategies that account for cure information such as (1) assigning infinite times to event for known cured cases and adjusting the traditional model and (2) considering only the probability of cure identification but ignoring the time until that happens. Theoretical results and simulations demonstrate the value of the proposed model especially when the time to cure identification is stochastic, increasing precision and decreasing the mean squared error. On the other hand, the traditional models that ignore the known cured information perform well when the curation is achieved after a known cutoff point. Moreover, through simulations the comparisons of the different strategies are examined, as possible alternatives to the complete-information model.