Patient-level simulation models in cancer care: a systematic review

Area of Science:

• Health economics and outcomes research
• Computational modeling in oncology
• Health services research

Background:

• Patient-level simulation (PLS) models address limitations of traditional cohort models in healthcare.
• Their application in cancer care remains underexplored, necessitating a comprehensive review.

Purpose of the Study:

• To systematically review the application areas of PLS models in cancer care.
• To identify prevalent PLS model structures and assess reporting quality.
• To critically evaluate the potential and limitations of PLS in oncology.

Main Methods:

• Systematic literature search across major databases (Web of Science, PubMed, EMBASE, EconLit).
• Inductive content analysis to identify reasons for PLS model use.
• Reporting quality assessment using an 18-item checklist based on ISPOR-SMDM guidelines.

Main Results:

• Publications on PLS in cancer care are increasing, primarily using state-transition microsimulation or discrete event simulation.
• Key applications include disease progression modeling (DPM) and health and care systems operation (HCSO).
• Average reporting quality was 65.2% and showed no improvement over time.

Conclusions:

• PLS models are valuable for simulating cancer progression and care delivery.
• Further research should compare PLS with cohort models in DPM and assess clinical impact in HCSO.
• Adherence to reporting guidelines like ISPOR-SMDM needs enhancement.