Presentation (CTI)

OBJECTIVES: Optimization of treatment sequences is central to oncology decision-making, yet sequence modelling requires detailed survival data that are unavailable in early development. We evaluated whether a simplified treatment-sequencing model approach, parameterized using only median progression-free survival (PFS) and overall survival (OS), reflecting an early data situation, could preserve comparative effectiveness conclusions relative to a published cohort state-transition model.
METHODS: The simplified approach employed a cohort-based continuous-time structure. Transitions to subsequent treatment lines occurred at the estimated mean PFS, derived from the median PFS assuming an exponential distribution. The proportion of patients reaching the next treatment line was estimated from OS at the transition time. A peer-reviewed treatment-sequencing model (Mason, 2023) in favorable-risk advanced renal cell carcinoma was used for comparison. Three treatment sequences were evaluated: pembrolizumab+lenvatinib→cabozantinib, pembrolizumab+axitinib→cabozantinib, and nivolumab+cabozantinib→lenvatinib+everolimus. The primary outcome was consistency of treatment-sequence rankings based on discounted life-years (LYs) and quality-adjusted life-years (QALYs).
RESULTS: The published model ranked pembrolizumab+lenvatinib→cabozantinib highest, followed by pembrolizumab+axitinib→cabozantinib and nivolumab+cabozantinib→lenvatinib+everolimus for both LYs and QALYs (4.92/3.33, 4.43/3.05, and 3.06/2.21, respectively). The simplified model reproduced the same ranking (4.44/3.16, 4.00/2.79, and 3.35/2.39 LYs/QALYs, respectively).LY and QALY estimates differed by less than 10% across all treatment sequences, with lower estimates for pembrolizumab-containing sequences and higher estimates for the nivolumab+cabozantinib sequence.
CONCLUSIONS: A simplified survival-based treatment-sequencing model parameterized from median survival estimates alone preserved comparative effectiveness rankings, demonstrating feasibility as an early, transparent benchmarking tool. This approach may inform early scenario testing, sequence prioritization, and evidence-generation planning, but should complement rather than replace full cost-effectiveness modelling.