OBJECTIVE: Estimating budget impact (BI) is relatively simple for chronic conditions and isolated acute conditions. However, estimating BI for oncology treatments is complicated by episodic treatment patterns and variable treatment times. We present cohort analysis methods to estimate the BI of oncology treatments by line of therapy. METHOD: BI analyses are based on the size of the treated patient population, cost of treatment per patient, and potential market share shifts over a given time horizon. In oncology, duration of treatment can vary based on differential progression-free survival by treatment regimen and line of therapy, with patients receiving initial treatment shortly after diagnosis until disease progression followed by additional lines of therapy. To model these treatment patterns, BI analyses may utilize treatment sequencing among individual patients to estimate treatment costs and BI, resulting in greater complexity and additional data requirements. We present simple cohort analysis methods to estimate the cost and BI of oncology treatments. Incidence of disease and the proportion of patients receiving each line of therapy are used to estimate the size of the annual target population. Utilizing a few plausible assumptions, the target population and average duration of treatment are used to estimate the annual steady-state treatment cost in the form of a diagonal treatment vector with an area equivalent to the incidence multiplied by the duration of treatment. We show how these cohort analysis methods relate to an individual patient level analysis and provide some worked examples to illustrate how they are used to calculate treatment cost and BI. CONCLUSION: Cohort analysis methods can be used to estimate the cost and BI of oncology treatments despite episodic treatment and variable duration of therapy. Use of cohort analysis methods can reduce the complexity of oncology BI models and make it easier to communicate the results to healthcare payers.