VALUE OF PERFECT IMPLEMENTATION AND INFORMATION FOR ANTI-VEGF THERAPY FOR MACULAR DEGENERATION

Purpose:

   Anti-vascular endothelial growth factor (anti-VEGF) therapies can prevent blindness from age-related macular degeneration (AMD), but they consume 1/6^th of Medicare’s part B drug budget.  The FDA-approved medication, ranibizumab, costs almost 40x the off-label medication, bevacizumab.  Randomized trials have shown similar efficacy, but the trials were not powered to detect small differences in important safety outcomes.  Prior cost-effectiveness analyses suggest ranibizumab has an incremental cost-effectiveness ratio in the millions of dollars when compared to bevacizumab.  However, 1/3^rd of patients currently receive ranibizumab.  We assess the value of perfect implementation with current information and compare this to the value of perfect implementation andinformation.

Method:

   We combine population models of disease incidence and prevalence along with patient-level models of disease progression and treatment to project 10-year costs and health outcomes for AMD patients in the United States treated with ranibizumab and bevacizumab.  We examine societal costs both under current practice and with perfect implementation where all patients receive cost-effective therapy.  We synthesize uncertainty in clinical trial safety results and ascertain the expected value of perfect information (EVPI) (assuming perfect implementation) using Monte Carlo simulation. 

Result:

   With current information, bevacizumab is cost-effective assuming a willingness-to-pay (WTP) of $100,000/QALY.  Over 10 years, 2.6 million patients will receive anti-VEGF treatment for AMD in the United States.  If all treated patients were to receive bevacizumab, the value of perfect implementation would be $46 billion.  Monte Carlo simulation shows an 8% chance that the preferred therapy would be ranibizumab.  The EVPI is $4.9 billion.  These results are robust to the choice of threshold WTP.

Conclusion:

   Comparing the value of perfect implementation with the value of perfect information helps prioritize policies.  Because of the large populations involved and the vast cost differences in therapies, the value of perfect implementation of anti-VEGF therapies for AMD is about an order of magnitude larger than the value of information.  This suggests that policies should focus on ways to increase use of therapy known to be cost-effective for AMD.  The value of future studies that gather more information on the effectiveness of therapies for AMD should be thought of in terms of how the results may influence real-world therapy choices by patients and providers, improving implementation of cost-effective therapies.