Distinguishing effects on tumor multiplicity and growth rate in chemoprevention experiments

In some types of cancer chemoprevention experiments and short-term carcinog enicity bioassays, the data consist of the number of observed tumors per an imal and the times at which these tumors were first detected. In such studi es, there is interest in distinguishing between treatment effects on the nu mber of tumors induced by a known carcinogen and treatment effects on the t umor growth rate. Since animals may die before all induced tumors reach a d etectable size, separation of these effects can be difficult. This paper de scribes a flexible parametric model for data of this type. Under our model, the tumor detection times are realizations of a delayed Poisson process th at is characterized by the age-specific tumor induction rate and a random l atency interval between tumor induction and detection. The model accommodat es distinct treatment and animal-specific effects on the number of induced tumors (multiplicity) and the time to tumor detection (growth rate). A Gibb s sampler is developed for estimation of the posterior distributions of the parameters. The methods are illustrated through application to data from a breast cancer chemoprevention experiment.