Data analysis

The cost-utility of ranibizumab PRN and ranibizumab T&E compared to aflibercept was evaluated by calculating the incremental cost per QALYs gained and using a willingness-to-pay (WTP) threshold of €25,000. Health Technology Assessment is not mandatory locally and as such there is no pre-determined WTP threshold generally applied by the Greek Authorities to make decisions on the reimbursement of healthcare interventions. In this analysis, the WTP threshold was assumed to equal the equivalent willingness to pay threshold value set by NICE in euros (approx. €25 k at the time). In addition, the net monetary benefit (NMB) of ranibizumab was estimated; the NMB is equal to the incremental QALY gained multiplied by the WTP minus the incremental costs. A NMB, greater than zero, indicates that a treatment is cost-effective in health care setting.

One-way sensitivity analysis (OWSA) was performed to test the robustness of the results. The independent variables were varied within plausible pre-specified ranges in order to ascertain the key drivers of cost-effectiveness and check for the impact of uncertainty on the NMB. The following parameters have been altered: discount rate; time horizon; BSE Utilities; risk ratio of DME mortality; WSE utilities; starting age; Odds ratio Ranibizumab vs Aflibercept, rebates and costing data. The values used in the sensitivity analysis are presented in Table 3. The results are presented in the form of Tornado diagrams.

Model inputs updated to the Greek healthcare setting used in sensitivity analysis

^a Lucentis® acquisition cost of €781.52 incorporated into the model using EOPYY rebate discount of 8%

^b Eylea® acquisition cost of €718.52 incorporated into the model using EOPYY rebate discount of 6.5%

BSE better-seeing eye; WSE worse-seeing eye; DME diabetic macular edema; BCVA best corrected visual acuity

The majority of input data used in the current model are subjected to variation. Therefore, in order to deal with uncertainty, a probabilistic sensitivity analysis (PSA) was performed using a second-order Monte Carlo simulation. In this analysis, a distribution was assigned around each parameter (i.e. costs, transition probabilities etc.) and the aforementioned economic and health outcomes associated with simultaneously selecting random values from those distributions were generated. Distributions were selected based on the nature of variables [47]. A cost-effectiveness acceptability curve (CEAC) was plotted, showing the proportion of simulations that are considered cost-effective at different levels of WTP per QALY gained.