Clinical Epidemiology (Nov 2022)
Oncology Simulation Model: A Comprehensive and Innovative Approach to Estimate and Project Prevalence and Survival in Oncology
Abstract
Brian Bloudek,1 Heidi S Wirtz,2 Zsolt Hepp,2 Jack Timmons,1 Lisa Bloudek,1 Caroline McKay,3 Matthew D Galsky4 1Curta Inc., Seattle, WA, USA; 2Seagen Inc., Bothell, WA, USA; 3Astellas Pharma Global Development, Inc., Northbrook, IL, USA; 4Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USACorrespondence: Brian Bloudek, Curta Inc, 113 Cherry St, PMB 45802, Seattle, WA, 98104-2205, USA, Tel +1 206-456-3633, Email [email protected]: We demonstrate a new model framework as an innovative approach to more accurately estimate and project prevalence and survival outcomes in oncology.Methods: We developed an oncology simulation model (OSM) framework that offers a customizable, dynamic simulation model to generate population-level, country-specific estimates of prevalence, incidence of patients progressing from earlier stages (progression-based incidence), and survival in oncology. The framework, a continuous dynamic Markov cohort model, was implemented in Microsoft Excel. The simulation runs continuously through a prespecified calendar time range. Time-varying incidence, treatment patterns, treatment rates, and treatment pathways are specified by year to account for guideline-directed changes in standard of care and real-world trends, as well as newly approved clinical treatments. Patient cohorts transition between defined health states, with transitions informed by progression-free survival and overall survival as reported in published literature.Results: Model outputs include point prevalence and period prevalence, with options for highly granular prevalence predictions by disease stage, treatment pathway, or time of diagnosis. As a use case, we leveraged the OSM framework to estimate the prevalence of bladder cancer in the United States.Conclusion: The OSM is a robust model that builds upon existing modeling practices to offer an innovative, transparent approach in estimating prevalence, progression-based incidence, and survival for oncologic conditions. The OSM combines and extends the capabilities of other common health-economic modeling approaches to provide a detailed and comprehensive modeling framework to estimate prevalence in oncology using simulation modeling and to assess the impacts of new treatments on prevalence over time.Keywords: epidemiology, Markov, modeling, oncology, OSM, prevalence
