Grant: Travel Awards - Institute Community Support

Novel therapies in oncology bring a promise for disrupting current medical practice and potential for cure. They also carry an eagerness for timely integration into clinical practice. Evaluation of the clinical and economic value of these therapies is however hindered by limited access to individual-level data, evidence on comparative and long-term effectiveness, and potential reporting bias in available data sources. Novel methods in economic evaluation are necessary to assess the clinical and economic value of such strategies. We illustrate the use of multi-state simulation modelling to estimate the long-term comparative effectiveness and cost-effectiveness of chimeric antigen receptor T-cell (CAR T-cell) therapy for Pediatric Acute Lymphoblastic Leukemia. To build a CAR T-cell arm, we pool digitized survival data from published single-arm clinical trial results. We then construct a broad standard care arm using individual-level data from a pediatric cancer registry. We matched this data to clinical trial inclusion/exclusion criteria and baseline patient characteristics - both treatment arms forecast movement to sequential health or treatment states and death using predicted transitions. We assumed a range of cure rates in the treatment arm given limited information on long-term effectiveness. The simulation model includes 100,000 individuals. We test uncertainty around published clinical trial results in robustness analysis. We also consider scenario analysis of novel therapy costing schemes. We estimate that CAR T-cell therapy has an incremental cost over standard care on a range of $85,478 to $347,348 per QALY gain, based on the assumed cure rates of 10% to 40%. These results indicate that the cost-effectiveness of CAR T-cell is highly dependent on a number of uncertain factors. The combined use of survival curve digitization, comparator cohort matching and MSM provides improved precision in assessing cost-effectiveness for novel therapies, however.