OBJECTIVES: The literature on statistical methods to estimate the average treatment effect (ATE), the National Institute for Health and Care Excellence’s preferred estimand for economic evaluations, is scarce, particularly for survival outcomes. With increasing numbers of health technology assessments (HTAs) based on single-arm trial and/or real-world evidence, particularly in oncology/rare-diseases, there is a need to investigate new methods to estimate the ATE when individual patient data (IPD) are available for two treatments of interest. The aims of this study are to present the implementation and robustness of such an approach for survival outcomes.

METHODS: The approach is based on a doubly robust statistical method combining matching with regression adjustment recently published by Austin 2020, using a Weibull model (lowest Akaike information criteria [AIC]) to estimate counterfactual event times. The comparison of mosunetuzumab versus rituximab+bendamustine, as a proxy for rituximab+chemotherapy combination (R-chemo), in the 3L+ relapsed/refractory follicular lymphoma (FL) setting is used as a real-world case study. IPD for mosunetuzumab (n=90, NCT02500407, January 2021 cut-off) and a combination of 3L+ FL cohorts from two studies (n=48, NCT02187861 and NCT02257567, latest cut-offs) were used for the comparison. Endpoints included overall survival (OS) and progression-free survival (PFS). Sensitivity analyses were performed to test the robustness of the method to different distributional assumptions (log-normal, log-logistic and exponential) or statistical models (second, third and fourth lowest AIC) for event times.

RESULTS: Results showed an improved PFS (hazard ratio [HR] 0.43 [95% confidence interval (CI): 0.04, 0.70) and OS (HR 0.30 [95% CI: 0.03, 4.93]) for mosunetuzumab versus R-chemo, and the findings remained consistent across the sensitivity analyses.

CONCLUSIONS: The proposed implementation of this method is relatively robust to modelling decisions and may represent a suitable indirect treatment comparison (ITC) approach for the doubly robust estimation of the ATE/local ATE for survival outcomes in HTA submissions.