OBJECTIVES: Long term effectiveness data on progression-free survival (PFS) and overall survival (OS) are rarely available from efficacy trials. Methods to estimate the expected PFS and OS using a range of survival models were explored up to 30 years for patients receiving axitinib, cabozantinib, everolimus, nivolumab, and BSC for second-line amRCC.

METHODS: Several parametric survival models, including cubic splines, were fitted to the everolimus and nivolumab groups of the CheckMate 025 trial to provide baseline curves for PFS and OS. Model fit was assessed using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), as well as clinical expert opinion to assess the plausibility of extrapolations. Hazard ratios (HRs), derived from a mixed treatment comparison (MTC), were applied to the everolimus curves to produce estimates for cabozantinib and BSC. A reliable HR could not be derived for axitinib due to a violation of proportional hazards (PH), as well as patient cross-over in the TARGET trial. Therefore, it was assumed that axitinib was equivalent to everolimus.

RESULTS: For OS, there were slight differences in the AIC and BIC statistics across the treatment groups, but the best fitting model was considered to be the Weibull. For PFS, the best fitting model was the 2-knot spline, clearly indicated by the lowest AIC and BIC for both everolimus and nivolumab. The Weibull models produced a mean OS of 26.5, 38.1, 26.5, 30.3 and 15.0 months, for axitinib, cabozantinib, everolimus, nivolumab and BSC, respectively. The equivalent values for the 2-knot spline PFS models were 7.9, 15.9, 7.9, 10.8 and 2.8 months, respectively.

CONCLUSIONS: The assumption of PH can be a limitation when comparing multiple treatments across different trials using an MTC. Practical solutions such as choosing the appropriate “baseline” trial and assuming clinical equivalence, where plausible, can help mitigate concerns in some cases.