Journal Article

Oncology Simulation Model: A Comprehensive and Innovative Approach to Estimate and Project Prevalence and Survival in Oncology

Bloudek, Lisa, Galsky, Matthew D, Timmons, Jack, Wirtz, Heidi S, Hepp, Zsolt, Bloudek, Brian, McKay, Caroline

2022

Overview

Objective: 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 prespecifed calendar time range. Time-varying incidence, treatment patterns, treatment rates, and treatment pathways are specifed 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

• Publisher: Dove Medical Press Limited,Taylor & Francis Ltd,Dove Press,Dove,Dove Medical Press
• Subject: Bladder cancer / Cancer therapies / Chronic illnesses / Clinical medicine / Computer simulation / Computer-generated environments / Disease / Epidemiology / Estimates / Markov / Medical research / Medicine, Experimental / modeling / Mortality / Oncology / Original Research / OSM / Patients / Population / prevalence / Public health / Reimbursement / Simulation / Trends