PRIMCAT-CRC: a data-driven whole disease simulation model for predicting the number of colorectal cancer patients by stage and treatment line in Australia
- Author(s)
- Degeling, K; To, YH; Trapani, K; Athan, S; Gibbs, P; IJzerman MJ; Franchini, F;
- Details
- Publication Year 2024-07-06,Volume 27,Issue #10,Page 1382-1392
- Journal Title
- Value in Health
- Abstract
- OBJECTIVES: Effective healthcare planning, resource allocation, and budgeting require accurate predictions of the number of patients needing treatment at specific cancer stages and treatment lines. The PRedicting the population health economic IMpact of current and new Cancer Treatments Colorectal Cancer (PRIMCAT-CRC) simulation model was developed to meet this requirement for all CRC stages and relevant molecular profiles in Australia. METHODS: Real-world data was used to estimate treatment utilisation and time-to-event distributions. This populated a discrete-event simulation, projecting the number of patients receiving treatment across all disease stages and treatment lines for CRC and forecasting the number of patients likely to utilise future treatments. Illustrative analyses were undertaken, estimating treatments across disease stages and treatment lines over a 5-year period (2022-2026). We demonstrated the model's applicability through a case study introducing pembrolizumab as a first-line treatment for mismatch-repair deficient stage IV. RESULTS: Clinical registry data from 7,163 patients informed the model. The model forecasts 15,738 incident and 2,821 prevalent cases requiring treatment in 2022, rising to 15,921 and 2,871 respectively by 2026. Projections show that over 2022-2026, there will be a total of 116,752 treatments initiated, with 43% intended for stage IV disease. The introduction of pembrolizumab is projected for 706 patients annually, totalling 3,530 individuals starting treatment with pembrolizumab over the forecasted period, without significantly altering downstream utilisation of subsequent treatments. CONCLUSIONS: PRIMCAT-CRC is a versatile tool that can be used to estimate the eligible patient populations for novel cancer therapies, thereby reducing uncertainty for policymakers in decisions to publicly reimburse new treatments.
- Publisher
- Elsevier
- Keywords
- colorectal cancer; disease projection; health technology assessment; real-world data; treatment utilisation; whole-disease model
- Research Division(s)
- Personalised Oncology
- PubMed ID
- 38977190
- Publisher's Version
- https://doi.org/10.1016/j.jval.2024.06.006
- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2024-07-10 09:21:00
Last Modified: 2024-10-04 10:50:17