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Background: Pre-marketing authorisation estimates of survival are generallyrestricted to those observed directly in randomised controlled trials (RCTs).However, for regulatory and Health Technology Assessment (HTA) decision-makinga longer time horizon is often required than is studied in RCTs. Therefore,extrapolation is required to estimate long-term treatment effect. Registry datacan provide evidence to support extrapolation of treatment effects from RCTs,which are considered the main sources of evidence of effect for new drugapplications. A number of methods are available to extrapolate survival data,such as Exponential, Weibull, Gompertz, log-logistic or log-normal parametricmodels. The different methods have varying functional forms and can result indifferent survival estimates.  Methods: The aim of this paper was to use registry data to supplement therelatively short term RCT data to obtain long term estimates of effect. Noformal hypotheses were tested. We explore the above parametric regressionmodels as well as a nonparametric regression model based on local linear(parametric) regression. We also explore a Bayesian model constrained to thelong term estimate of survival reported in literature, a Bayesian power priorapproach on the variability observed from published literature, and a BayesianModel Averaging (BMA) approach. The methods were applied to extrapolate overallsurvival of a RCT in metastatic melanoma.  Results: The results showed that the BMA approach was able to fit the RCTdata well, with the lowest variability of the area under the curve up to 72months with or without the SEER Medicare registry.  Conclusion: the BMA approach is a viable approach to extrapolate overallsurvival in the absence of long term data.

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