Cost-effectiveness analysis (CEA) is a systematic method of comparing two or more alternative interventions by calculating their costs and health outcomes, ultimately expressing costs in relation to their effectiveness. Because the health outcomes, such as life years gained or improvements in animal welfare, remain in their natural units rather than being monetized, CEA is well suited to decision-making across all animal health settings, including companion animals. Results are often expressed as incremental cost-effectiveness ratio, such as the cost per additional life year gained with a new therapy compared to current standard treatment. CEA often uses decision trees and Markov models to simulate outcomes over time. Both models allow the consideration of data and information from many sources and can model the outcomes over a longer time period as considered in clinical studies A decision tree is much simpler than a Markov model, but it is an intuitive tool to display simple disease and treatment processes. A Markov model is especially useful for approximating long-term outcomes when animals progress through different health states, e.g., progress over time through states of varying severity.