The choice of health care modeling approaches is driven by trade-offs between various modeling techniques. This study evaluates cohort (CH) versus patient-level (PL) Markov modeling techniques within a cost-effectiveness analysis framework to understand the practical decisions analysts face. Both the CH and PL models were constructed using identical datasets and similar assumptions. Each model included eight health states to capture disease severity and symptom types and allowed switching from first-line to second-line treatment. We assessed model outcomes and performance using various quantitative and qualitative techniques. The CH and PL models yielded very similar base case results; only minor differences in functionality and outcome consistency were detected. The CH model offered greater stability and easier parameter testing, while the PL model provided superior flexibility for structural adjustments and detailed patient pathway and subgroup analysis. However, the PL model required substantially more computational time for sensitivity analyses and more technical skills to understand and interpret patient pathways and model results. CH modeling faced more challenges when extensive structural changes were initiated. Choosing between CH and PL modeling techniques involves the careful assessment of trade-offs between the need for a flexible and informed model and the optimization of human and computational resources.

Tradeoffs When Building and Running Cohort and Patient-Level Markov Simulation Models