Bayesian survival extrapolation for cost-effectiveness analysis: a case study of RELAY for ramucirumab in combination with erlotinib in the treatment of non-small-cell lung cancer.

Aim: Increasing trend for progression-free survival (PFS)-based primary endpoint in oncology has led to lack of mature overall survival (OS) data at the time of approval. To address this evidence gap in economic evaluations, we used a joint Bayesian approach to predict survival outcomes using immature OS data from the RELAY trial.

Methods: Patient data from RELAY and systematic literature review (SLR) of phase 3 randomized clinical trials with hazard ratio (HR) estimates of mature PFS and immature OS were considered. OS and PFS were analyzed individually using a univariate model; bivariate analysis was performed using a joint model based on modified Bayesian normal induced copula estimation model. First, a Bayesian univariate model incorporated informative priors based on predicted HR and acceleration factor for OS and PFS. Second, a Bayesian-based joint model of RELAY PFS and OS data was based on the correlation between PFS and OS established in trials of similar populations. Marginal distribution of PFS was used to estimate the same for OS.

Results: Publications ( = 122) of first-line treatments in patients with epidermal growth factor receptor ()-mutated non-small cell lung cancer were identified in the SLR, of which 36 trials were linked to RELAY. Twenty-six trials with HR data were used. The univariate model could predict OS with reduced uncertainty compared with the frequentist approach. In the joint model, the marginal OS distribution borrowed strength from the marginal PFS distribution through the established correlation coefficient.

Limitations: Bayesian approach was successfully used in RELAY analysis but may not be universally applied to oncology trials due to the different associations of OS and PFS and different trial patient populations.

Conclusions: We demonstrated that both the univariate and joint Bayesian models reduced uncertainty in predicting OS compared to frequentist method. The methodology introduced here will have potential applications in clinical decision-making for other oncology trials.