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

Figures & data

Figure 1 Methods of estimating population size.

Abbreviations: MIAMOD, Mortality and Incidence Analysis MODel; OSM, oncology simulation model; PIAMOD, Prevalence and Incidence Analysis MODel.

Table 1 Comparison of the OSM with Common Approaches to Health-Economic Modeling

	Budget-Impact Model	Cost-Effectiveness Model	Oncology Simulation Model (OSM)
Scope	All patients (in a health plan or specific population)	1 patient	Population
Treatments	Single line of therapy	Single line of therapy	Full disease landscape
Time	Time-varying market share and static annual incidence	No impact of time	Time-varying efficacy Time-varying pathways Continuous incidence
Historical	Prevalence not typically included; incidence-only perspective	Single patient treatment trajectory	Aggregated cohort trajectories including historical treatment pathways
Outputs	Population estimates for single therapy based on annual incidence alone	Efficacy of treatments for single patient	Prevalence of all lines of therapy and population treatment pathway composition
Benefits	Incidence data are typically readily available Easy to interpret and follow calculations Fairly quick model-development time	Detailed treatment trajectory of a single patient Methods well understood Treatment efficacy can usually be informed from literature	Full disease landscape prevalence Predicted disease prevalence and impact of new treatment Detailed population-level output representing a multitude of patient journeys and treatment pathways
Challenges	Lacks precision to estimate prevalence Limited to single line of therapy Difficult to capture dynamic effects in a changing treatment environment	Calculations can be difficult to follow “memoryless” (Markov assumption) Difficult to track treatment pathways and sequencingDifficulty finding data for more complex modelsLonger model-development time	Calculations can be difficult to follow Difficulty finding data for efficacy and treatment % for the full disease landscape Model-development time based on complexity or data availability

Figure 2 (A) Multi-stage model structure and (B) Markov structure within individual treatments.

Figure 3 Treatment patterns for bladder cancer in the United States.

Abbreviations: 1L/2L/3L, first-/second-/third-line; chemo, chemotherapy; EV, enfortumab vedotin; la/mUC, locally advanced or metastatic urothelial carcinoma; MIBC, muscle-invasive bladder cancer; NMIBC, non-muscle-invasive bladder cancer.

Figure 4 US 2022 la/mUC patient flow and annual treated prevalence.

Note: The estimated 1-year period prevalence in 2022 for each population is as follows: total la/mUC treated prevalence = 33,000; 1L la/mUC treated prevalence = 28,530; 2L la/mUC treated prevalence = 7,290; and 3L la/mUC treated prevalence = 2,070.

Abbreviations: 1L/2L/3L, first-/second-/third-line; EPP, estimated 1-year period prevalence; la/mUC, locally advanced or metastatic urothelial carcinoma; MIBC, muscle-invasive bladder cancer.

Figure 5 Sensitivity analysis: contour plots of US 2022 la/mUC annual treated prevalence.

Abbreviations: 1L/2L, first-/second-line; la/mUC, locally advanced or metastatic urothelial carcinoma.