The goals of this research are to build a new and more powerful discrete-event simulation model based on an existing, validated computer model of the natural history of colorectal neoplasia and use the new model to address questions concerning the clinical outcomes, cost, effectiveness, cost- effectiveness, and resource utilization of various colorectal cancer (CRC) control strategies for patients and for complex and dynamic populations. These goal will be achieved through the translation of an existing model into C++, an object-oriented programming language that would improve the model's ease of modification, decrease the time needed to complete an analysis, and allow direct interaction between the model and other commonly available programming applications. The new model will be programmed to allow the simulation of populations whose individual members are characterized by multiple and interacting cancer risk factors, have varying degrees of adherence with established cancer control protocols, and receive their care in settings where the resources needed to provide cancer control interventions are limited. The parameterization of this model will be based on a rigorous review of the literature and the model will be validated by several methods, including existing cancer control outcome data. After completion of its programming, parameterization and validation, the applicant will demonstrate the power and versatility of this new computer model by employing it in various complex analyses concerning the expected outcome over time of implementing new cancer control strategies in both the general U.S. population and specific subpopulations representing relevant geopolitical and payer/provider groupings. In addition, the applicant will examine the impact of resource constraints (such as manpower) on the outcomes of proposed cancer control strategies and the implications of disparities in such resources across geopolitical boundaries and healthcare provider markets. The completed model will be able to predict the outcomes of any given CRC control strategy, among specific risk-defined patient subgroups, in any given population, during and over any period of time. This model could serve as a template and modeling paradigm for the examination of the cost-effectiveness of prevention and treatment strategies for other cancers and for diseases other than cancer.
