Supplementary materials: Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity
These are peer-reviewed supplementary materials for the article 'Bayesian hierarchical model-based network meta-analysis to overcome survival extrapolation challenges caused by data immaturity' published in the Journal of Comparative Effectiveness Research.
- Figure S1: Network of evidence
- Figure S2: Observed survival by reference trial vs predicted survival with standard Weibull mixture-cure model network meta-analysis
- Figure S3: Observed survival by reference trial vs predicted survival with BH WCM NMA with τ' = σ’ = 0.01
- Table S1: Overview of parameters, confidence intervals, effective sample size and Rhat by approach tested
- Figure S4: Density plots with the numbers corresponding to the parameter numbers in supplementary Table S1
- Figure S5: Autocorrelation plots on the cure parameters
Aim: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH)WMCNMAto inform long-term survival of therapies. Materials & methods: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. Results: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively,with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). Conclusion: BH-WMC-NMA impacts incremental mean LYs and cost–effectiveness ratios, potentially affecting reimbursement decisions.