PRM228 - EXTRAPOLATION OF SURVIVAL CURVES USING EXTERNAL INFORMATION: IMPLEMENTATION OF GUYOT’S METHOD IN PREVIOUSLY UNTREATED ADVANCED OR METASTATIC RENAL CELL CARCINOMA

Abstract

The extrapolation of survival is key when conducting cost-effectiveness analyses in oncology, especially when progression-Free (PFS) and overall survival (OS) outcomes have limited trial follow-up. Guidance from NICE suggest that the choice of distribution should be based on internal and external validation, the latter usually being undertaken by visual inspection. The aim of this study was to evaluate the impact of the use of the method developed by Guyot to include external data in the statistical extrapolation model itself, using survival outcomes from Checkmate 214 in 1L renal cell carcinoma (RCC). A systematic literature review on observational studies was conducted to identify relevant OS or PFS data for metastatic RCC patients with intermediate to poor prognosis. The assumption of the Guyot’s method is that after the trial follow-up, the conditional survival of the control arm should converge to the one of the external study. Parametric and cubic spline independent models were considered. Guyot’s method was subsequently implemented in a Bayesian framework to combine CheckMate 214 data with external information. Two studies reported long-term survival outcomes for intermediate to poor mRCC patients comparable to the ones from CheckMate 214. Based on the model with best fit, Guyot’s method was found to have a moderate impact on results: for the sunitinib arm, the predicted PFS without external data adjustment (log-normal) was 25% at 2 years, while it was 17% with external data. At 5 years, the unadjusted estimate was 9%, while it was reduced to 2% including external data. A similar impact was observed on OS. Guyot’s method is a useful tool for extrapolations of time-to-event data, as it adjusts long-term predictions rather than relying on a simple visual inspection. As the results are sensitive to the chosen external studies, choice of external data should be well justified.