Matching plus regression adjustment for the estimation of the average treatment effect on survival outcomes: a case study with mosunetuzumab in relapsed/refractory follicular lymphoma.

Abstract

Background and objectives: The National Institute for Health and Care Excellence (England's health technology assessment body) recommend the use of the average treatment effect (ATE) as an estimand for economic evaluations. However there is limited literature on methods to estimate the ATE, particularly in the case of survival outcomes. Single-arm trials and real-world data are playing an increasing role in health technology assessments, particularly in oncology/rare diseases, generating a need for new ATE estimation methods. This study aimed to present the adaptation and utility of this methodology for survival outcomes.

Methods: The approach is based on a "doubly robust" method combining matching with regression adjustment (Austin 2020) using a Weibull model (lowest Akaike information criteria [AIC] specification) to estimate counterfactual event times. As a case study, we compared mosunetuzumab versus rituximab/bendamustine, as a proxy for rituximab/chemotherapy, in 3L+ relapsed/refractory follicular lymphoma. Individual patient data for mosunetuzumab (NCT02500407) and a combination of two rituximab/bendamustine 3L+ follicular lymphoma cohorts (NCT02187861/NCT02257567) were used. Endpoints included overall survival (OS) and progression-free survival (PFS). Sensitivity analyses were performed to test robustness to different distributional assumptions (log-normal, log-logistic and exponential) or model specifications (second, third and fourth lowest AIC) for event times.

Results: The case study found improved PFS (hazard ratio [HR] 0.43 [95% confidence interval (CI): 0.13, 0.91]) and OS (HR 0.30 [95% CI: 0.05, 5.28]) for mosunetuzumab. Consistent findings (HR range 0.25-0.47 and 0.21-0.50 with all CIs excluding/including 1 for PFS/OS, respectively) were observed in sensitivity analyses.

Discussion/conclusions: The proposed adaptation expands the range of available approaches for the estimation of the (local) ATE for survival outcomes in health technology assessments using "doubly robust" methods. This approach appeared relatively robust to modelling decisions in our case study.