Threshold models of tumor recurrence

The main advantage of threshold models of tumor recurrence is that they closely parallel the real observation process in cancer post-treatment surveillance. Such models are typically based on the assumption that a recurrent tumor becomes detectable when its size attains some threshold value which may be treated as a random variable. This paper discusses various stochastic models that have been proposed for the analysis of data on tumor latency subject to censoring effects. A new model allowing for surviving clonogenic cells is presented.