Oncology Simulation Model: A Comprehensive and Innovative Approach to Estimate and Project Prevalence and Survival in Oncology.
Clin Epidemiol
; 14: 1375-1386, 2022.
Article
in En
| MEDLINE
| ID: mdl-36404878
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Guideline
/
Prevalence_studies
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Clin Epidemiol
Year:
2022
Document type:
Article
Affiliation country:
United States