The use of registry data to extrapolate overall survival results from randomised controlled trials

Abstract

Background: Pre-marketing authorisation estimates of survival are generally restricted to those observed directly in randomised controlled trials (RCTs). However, for regulatory and Health Technology Assessment (HTA) decision-making a longer time horizon is often required than is studied in RCTs. Therefore, extrapolation is required to estimate long-term treatment effect. Registry data can provide evidence to support extrapolation of treatment effects from RCTs, which are considered the main sources of evidence of effect for new drug applications. A number of methods are available to extrapolate survival data, such as Exponential, Weibull, Gompertz, log-logistic or log-normal parametric models. The different methods have varying functional forms and can result in different survival estimates. Methods: The aim of this paper was to use registry data to supplement the relatively short term RCT data to obtain long term estimates of effect. No formal hypotheses were tested. We explore the above parametric regression models as well as a nonparametric regression model based on local linear (parametric) regression. We also explore a Bayesian model constrained to the long term estimate of survival reported in literature, a Bayesian power prior approach on the variability observed from published literature, and a Bayesian Model Averaging (BMA) approach. The methods were applied to extrapolate overall survival of a RCT in metastatic melanoma. Results: The results showed that the BMA approach was able to fit the RCT data well, with the lowest variability of the area under the curve up to 72 months with or without the SEER Medicare registry. Conclusion: the BMA approach is a viable approach to extrapolate overall survival in the absence of long term data.