Supplementary materials: Novel and existing flexible survival methods for network meta-analyses

<b>These are peer-reviewed supplementary materials for the article '</b><b>Novel and existing flexible survival </b><b>methods for network meta-analyses</b><b>' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><b>Supplementary data</b><b>Figure 1: </b>Overall survival hazard plots in evidence network for the pooled Checkmate 017-057 trials comparing nivolumab with docetaxel<b>Figure 2: </b>Network of evidence<b>Figure 3: </b>Log cumulative hazard plots and Schoenfeld plots<b>Figure 4: </b>Predicted survival best fitting reduced models and corresponding 95% credible interval on therapy with longest predicted survival<b>Table 1: </b>Overview of the percentiles related to placements of knots and the corresponding uncensored survival times in the considered NMA dataset<b>Aim:</b> Technical Support Document 21 discusses trial-based, flexible relative survival models. The authors generalized flexible relative survival models to the network meta-analysis (NMA) setting while accounting for different treatment-effect specifications. <b>Methods:</b> The authors compared the standard parametric model with mixture, mixture cure and nonmixture cure, piecewise, splines and fractional polynomial models. The optimal treatment-effect parametrization was defined in two steps. First, all models were run with treatment effects on all parameters and subsequently the optimal model was defined by removing uncertain treatment effects, for which the parameter was smaller than its standard deviation. The authors used a network in previously treated advanced non-small-cell lung cancer. <b>Results:</b> Flexible model based NMAs impact fit and incremental mean survival and they increase corresponding uncertainty. Treatment-effect specification impacts incremental survival, reduces uncertainty and improves the fit statistic. Conclusion: Extrapolation techniques already available for individual trials can now be used for NMAs to ensure that the most plausible extrapolations are being used for health technology assessment submissions.