Background: Therapies used in advanced oncology are now studied in earlier stages of the disease as adjuvant treatments. Demonstrating the value of these therapies is challenging. Post-surgical patients who are well, and potentially cured, are faced with immediate treatment-related costs and toxicities; however, benefits often occur much later along a complex patient pathway. The objective is to expose the challenges of estimating cost-effectiveness for adjuvant therapies in the changing landscape of cancer treatment, and to offer appropriate modelling approaches. Economic evaluations in oncology mostly rely on survival partition models, using progression-free survival and overall survival data from clinical trials. The same structure could be utilized for adjuvant therapies, using disease-free and overall survival; however, the extrapolation is “blind” to what truly happens post-recurrence. Furthermore, recurrences may be few and of multiple severity (local-regional, distant, potentially eligible to further surgical resection) presenting the patient population with a very heterogeneous prognosis. Adjuvant trials take longer to run, hence subsequent therapies received during the trial may be outdated by the time of the trial-readout. To capture the true effect of adjuvant treatment, subsequent treatment options should also be evaluated. Applying data from advanced cancer trials is however not straightforward. The relationship of the disease characteristics of patients recurring in the adjuvant trial and the population of metastatic trials should be established to evaluate treatment beyond recurrence correctly. However, risk-classification methods in an adjuvant setting may be different, and there is often no detailed information about patient characteristics at the time of recurrence. This study presents two alternative approaches to modelling adjuvant treatments of a solid tumour and outlines the conditions that warrant more complex modelling of the advanced disease stage. Recommendation: Applying a sequential model to capture the treatment pathway addresses these challenges and demonstrates the cost-effectiveness of adjuvant therapies more precisely.