Health Care Management Science

, Volume 14, Issue 1, pp 1–21

Probability model for estimating colorectal polyp progression rates


    • Department of Industrial and Management Systems EngineeringUniversity of South Florida
  • Selen Aydogan-Cremaschi
    • Department of Chemical EngineeringUniversity of Tulsa
  • Tapas K. Das
    • Department of Industrial and Management Systems EngineeringUniversity of South Florida
  • Seza Orcun
    • Discovery ParkPurdue University

DOI: 10.1007/s10729-010-9138-3

Cite this article as:
Gopalappa, C., Aydogan-Cremaschi, S., Das, T.K. et al. Health Care Manag Sci (2011) 14: 1. doi:10.1007/s10729-010-9138-3


According to the American Cancer Society, colorectal cancer (CRC) is the third most common cause of cancer related deaths in the United States. Experts estimate that about 85% of CRCs begin as precancerous polyps, early detection and treatment of which can significantly reduce the risk of CRC. Hence, it is imperative to develop population-wide intervention strategies for early detection of polyps. Development of such strategies requires precise values of population-specific rates of incidence of polyp and its progression to cancerous stage. There has been a considerable amount of research in recent years on developing screening based CRC intervention strategies. However, these are not supported by population-specific mathematical estimates of progression rates. This paper addresses this need by developing a probability model that estimates polyp progression rates considering race and family history of CRC; note that, it is ethically infeasible to obtain polyp progression rates through clinical trials. We use the estimated rates to simulate the progression of polyps in the population of the State of Indiana, and also the population of a clinical trial conducted in the State of Minnesota, which was obtained from literature. The results from the simulations are used to validate the probability model.


Colorectal cancerDisease progressionCRC interventionPolyp progressionCRC simulationApplied probability

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© Springer Science+Business Media, LLC 2010