RW3 Quantitative Bias Analysis (QBA) for Comparative Effectiveness of Alectinib versus Ceritinib in Non-Small Cell Lung Cancer (NSCLC)

Abstract

German and Canadian HTAs rejected reimbursement of alectinib for second-line (2L) ALK+ NSCLC citing possible biases from (i) unmeasured confounding and (ii) missing ECOG performance status (PS) data in real-world evidence (RWE) for 2L ceritinib submitted as comparator to single-arm alectinib trials. We sought to evaluate this HTA decision using post-launch real-world data from the United States, where alectinib was approved. Quantitative bias analysis was used to compare the effectiveness of alectinib versus ceritinib in 2L post-crizotinib ALK+ NSCLC using treatment arms derived from pooled single-arm alectinib trials (n=183) and records from a US nationwide Flatiron Health electronic health record (EHR)-derived de-identified database (n=87 and 102 for alectinib and ceritinib). Propensity score weighting was used to balance baseline characteristics between treatment arms. Large imbalances were observed in age, CNS metastases and prior chemotherapy. After adjustment for baseline covariates, alectinib-treated patients (n=55) had significantly longer median overall survival (OS) of 28 months (95% CI: 20-37) than ceritinib (11 months, 95% CI: 8-27; n=44) using complete case analysis (hazard ratio (HR): 0.64, 0.43-0.91). An unmeasured confounder associated with OS and alectinib exposure by a risk ratio >2.1-fold was needed to nullify the observed treatment effect. Under a missing-at-random assumption (MAR) for ECOG PS scores (49% missing for ceritinib arm) using multiple imputation, the pooled HR was 0.64 (0.5-0.8). The observed treatment effect was robust to all deviations from MAR where balance in baseline characteristics could be achieved. Alectinib maintained a significantly longer median OS (28 months versus 13 months, HR: 0.70, 0.54-0.85) after shifting an additional 10-40% patients to a poorer ECOG PS than expected under MAR. Only implausible levels of bias reversed our conclusions. These methods could provide a framework to explore uncertainty in RWE and aid decision-making for HTAs to enable patient access to innovative therapies.