OBJECTIVES: Summary survival data are available from published trials on first line metastatic renal cell cancer (1LmRCC) treatments. Survival on oncological treatments in pharmacoeconomics is mainly estimated by fitting common parametric distributions over Kaplan Meier (KM) curves, assuming proportional hazards over time. Use of fractional polynomials (FP) allows for change of hazards over time and offers more freedom in distribution selection. This study aims to analyse existing survival data of 1LmRCC treatments through a network meta-analysis (NMA) and FP application. METHODS: A systematic literature review was performed to identify randomized clinical trials (RCT) of 1LmRCC treatments with progression free survival (PFS) and/or overall survival (OS) as reported outcomes and to create a RCT network accordingly. Fixed and random effects FP models of first/second order were applied on these data and tested for goodness of fit using deviance information criteria. Finally, the best fitting model was used to estimate the hazard function, median PFS, median OS and uncertainty of treatment effect. RESULTS: Literature review found 8 RCTs and 5 RCTs which reported PFS and OS respectively, for 7 different mRCC treatments (interferon-alfa (IFN), bevacizumab+IFN, temisrolimus+bevacizumab, sunitinib, pazopanib, cediranib, placebo). The best fitting FP model was second order random effect model for both, PFS and OS NMA. Hazard functions varied significantly. Estimated median PFS was the longest with sunitinib (10.8 months; 95% credible interval (CI): 9.5-11.8), followed by pazopanib and temsirolimus+bevacizumab. Similarly, sunitinib was estimated with the longest median OS (28.8 months; 95% CI: 25.7-31.0) followed by pazopanib and bevacizumab+IFN. CONCLUSIONS: Synthesis of NMA evidence for 1LmRCC treatments identified sunitinib to be the treatment with favourable PFS and OS. When dealing with multiple sources, hazards proportionality assumption is violated, and proposed method should be the method of choice.