OBJECTIVES: The relative value of an oncology therapy, computed in terms of net health benefit (NHB) in the ASCO value framework, depends on the weighted combination of underlying clinical endpoint scores. As such, the objective of this study was to assess the sensitivity of those clinical endpoints, with an ultimate aim to identify a combination of endpoints that optimizes the value outcome.   METHODS: Two different analytical assessments of the advanced disease value framework were conducted: Monte Carlo simulation to develop a baseline distribution of NHB scores, and sensitivity analysis to determine the optimal combination of clinical endpoints. Six endpoints were considered: Median Overall Survival (mOS), median Progression-free Survival (mPFS), Response Rate (RR), Toxicity (T), Palliation (P), and Treatment-free Interval (TFI). The Monte Carlo simulation was based on 10000 iterations for each endpoint and an assumption of linear probability distribution across all endpoints. The sensitivity analysis was based on 750 total possible combinations of the six endpoints.  RESULTS: While the NHB scores trace a bell curve within -4 to 130 range, the median scores in considering one of the three clinical benefits, mOS, mPFS, or RR, were 60, 44, and 36, respectively. Furthermore, to achieve the same NHB score, multiple combinations of clinical endpoints were possible, typically in the 1-14 range. The lever with the highest sensitivity was mOS. Most importantly, holding clinical benefit score constant, Toxicity has higher sensitivity vs Palliation or Treatment-free Interval.  CONCLUSIONS: While clinical benefit, specifically mOS, is the primary driver for the Net Health Benefit (NHB) score, we found Toxicity to be more influential than Palliation or Treatment-free Interval, suggesting certain clinical endpoints may be prioritized in oncology clinical trial design if the probability of success includes optimizing NHB. Future studies would assign cost centers to the endpoints to assess the balance between time vs cost or risk vs NHB.