"A Class of Semiparametric Mixture Cure Survival
Models with Dependent
Censoring"
Modern cancer treatments have substantially improved cure rates and have generated a great interest in and need for proper statistical tools to analyze survival data with non-negligible cure fractions. Data with cure fractions are often complicated by dependent censoring, and analysis of this type of data typically involves untestable assumptions about the dependence of the censoring and the survival times. Motivated by the analysis of NCI SEER data, we propose a class of general semiparametric transformation cure models that allows for dependent censoring without making parametric assumptions on the dependence relationship and use the proposed methods to investigate potential racial disparities in prostate cancer cures. The proposed class of models encompasses a number of common models for the latency survival function, including the proportional hazards and proportional odds models.
