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. 2022 Nov 14:14:1375-1386.
doi: 10.2147/CLEP.S377093. eCollection 2022.

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

Brian Bloudek  1 , Heidi S Wirtz  2 , Zsolt Hepp  2 , Jack Timmons  1 , Lisa Bloudek  1 , Caroline McKay  3 , Matthew D Galsky  4
Affiliations

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

Brian Bloudek et al. Clin Epidemiol. .

Abstract

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 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: Markov; OSM; epidemiology; modeling; oncology; prevalence.

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Conflict of interest statement

BB, JT, and LB are employees of Curta Inc., paid consultants to Seagen Inc. in connection with this study. HSW and ZH are employees and stockholders of Seagen Inc. HSW is also stockholder of Amgen Inc. and Teva Pharmaceuticals. CM is an employee and stockholder of Astellas, Inc. and also a stockholder of Merck and J&J. MDG has served as an advisory board member and consultant for Aileron Therapeutics, Astellas, AstraZeneca, Basiliea, BioMotiv, BMS, Dendreon, Dracen, Dragonfly, EMD Serono, Genentech, GSK, Incyte, Inovio Pharmaceuticals, Janssen, Lilly, Merck, Novartis, Numab, Pfizer, Seagen Inc., and Urogen; has equity ownership in Rappta Therapeutics; holds patents/royalties for “Methods and compositions for treating cancer and related methods (20120322792)”; and has received research funding from AstraZeneca, BMS, Dendreon, Genentech, Janssen, Merck, and Novartis.

Figures

Figure 1
Figure 1
Methods of estimating population size.
Figure 2
Figure 2
(A) Multi-stage model structure and (B) Markov structure within individual treatments.
Figure 3
Figure 3
Treatment patterns for bladder cancer in the United States.
Figure 4
Figure 4
US 2022 la/mUC patient flow and annual treated prevalence.
Figure 5
Figure 5
Sensitivity analysis: contour plots of US 2022 la/mUC annual treated prevalence.
See this image and copyright information in PMC

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