In the standard survival model, the risk of failure is non-zero for all cases. A split-population (or cure) survival model relaxes this assumption and allows an (estimable) fraction of cases never to experience the event. This presentation reports on an implementation of a discrete time (or grouped survival data) version of this model, using ml method d0, and the problems with implementing a 'robust' option.
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