Marginal analysis of multivariate failure time data with a surviving fraction based on semiparametric transformation cure models

In biomedical, genetic and social studies, there may exist a fraction of individuals not experiencing the event of interest such that the survival curves eventually level off to nonzero proportions. These people are referred to as “cured” or “nonsusceptible” individuals. Models that have been developed to address this issue are known as cured models. The mixture model, which consists of a model for the binary cure status and a survival model for the event times of the noncured individuals, is one of the widely used cure models. In this paper, we propose a class of semiparametric transformation cure models for multivariate survival data with a surviving fraction by fitting a logistic regression model to the cure status and a semiparametric transformation model to the event time of the noncured individual. Both models allow incorporating covariates and do not require any assumption of the association structure. The statistical inference is based on the marginal approach by constructing a system of estimating equations. The asymptotic properties of the proposed estimators are proved, and the performance of the estimation is demonstrated via simulations. In addition, the approach is illustrated by analyzing the smoking cessation data.