Designing cancer prevention trials: a stochastic model approach.
There is growing interest in the design and implementation of cancer prevention trials. The key idea is to have agents which interfere with carcinogenesis and/or the preclinical stage. In this article we develop multi-stage stochastic models for the planning of cancer prevention trials. For known inputs it is possible to calculate the incidence of disease for the control and intervention groups. Consequently we find designs that balance the required sample size and follow-up time while guaranteeing prespecified error probabilities. Moreover such models can incorporate the mode of action of the intervention as well as compliance. The model has been applied to breast cancer to determine the implications for planning breast cancer intervention trials. Although the model addresses issues in cancer prevention, it is quite general and may be suitable for other chronic diseases.
Davidov
O
O
Department of Statistics, University of Haifa, Mount Carmel, Haifa 31905, Israel.
Zelen
M
M
D000367:Age Factors; D001943:Breast Neoplasms / Q000517:prevention & control*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015994:Incidence; D008390:Markov Chains; D008875:Middle Aged; D016012:Poisson Distribution; D011322:Primary Prevention; D011336:Probability; D012307:Risk Factors; D018401:Sample Size; D013269:Stochastic Processes; D013997:Time Factors