{"success":1,"msg":"","color":"rgb(28, 35, 49)","title":"On a Semiparametric Estimation Method for AFT Mixture Cure Models <\/b>","description":"webinar","title2":"","start":"2021-02-12 13:00","end":"2021-02-12 14:30","responsable":"Vladimir Rodr\u00edguez-Caballero <\/i><\/a>","speaker":"Ingrid Van Keilegom, KU Leuven","id":"24","type":"webinar","timezone":"America\/Mexico_City","activity":"Zoom Meeting link:\r\nhttps:\/\/itam.zoom.us\/j\/95346511465?pwd=WWx6YVlqQWpQcTFwakNZNlJ6OVpNZz09\r\n\r\nID Meeting: 953 4651 1465\r\nAccess code: 742346","abstract":"When studying survival data in the presence of right censoring, it often happens that a \r\ncertain proportion of the individuals under study do not experience the event of interest and are \r\nconsidered as cured. The mixture cure model is one of the common models that take this feature \r\ninto account. It depends on a model for the conditional probability of being cured (called the \r\nincidence) and a model for the conditional survival function of the uncured individuals (called the \r\nlatency). \r\nThis work considers a logistic model for the incidence and a semiparametric accelerated failure \r\ntime model for the latency part. The estimation of this model is obtained via the maximization of \r\nthe semiparametric likelihood, in which the unknown error density is replaced by a kernel estimator \r\nbased on the Kaplan-Meier estimator of the error distribution. Asymptotic theory for consistency \r\nand asymptotic normality of the parameter estimators is provided. Moreover, the proposed \r\nestimation method is compared with a method proposed by Lu (2010), which uses a kernel \r\napproach based on the EM algorithm to estimate the model parameters. Finally, the new method is \r\napplied to data coming from a cancer clinical trial."}