Stochastic simulation of a multistage model of carcinogenesis

Abstract

Stochastic mathematical models of carcinogenesis have been used to quantify cancer risks for about 40 years. As more detailed data of the cancer process are obtained, mathematical models try to incorporate this information and as a result become more complex and sometimes analytically and numerically intractable. Simulation studies have become an important tool for examining the operating characteristics of the models of interest. The many quantities one can examine using this tool include bias in parameter estimates, adequacy of approximation methods, and the appropriateness of large sample generalizations to small studies. This manuscript describes a general method of stochastic simulation that may be carried out for arbitrarily complicated stochastic models of carcinogenesis.