Leveraging cumulative network meta-analysis (NMA) and value of information (VOI) analysis, this article aims to understand the evolving value of medical research and to identify gaps in the evidence for future research.
As an illustration, we identified 31 randomized controlled trials (RCT) from 1980 to 2013 that examined a network of 3 interventions for coronary artery disease: medical therapy (MED), percutaneous coronary intervention (PCI), and coronary artery bypass graft (CABG) surgery. We conducted Bayesian NMA to combine evidence from a new RCT with existing knowledge. Then, using the Duke Databank for Cardiovascular Diseases database, we developed an accelerated failure time model to estimate the joint effects of patient characteristics and treatment choices on survival outcomes. With the estimated coefficients and covariance matrices, we projected survival benefits and its surrounding uncertainty among 50,000 simulated patients treated with MED, PCI, or CABG. The value of resolving residual uncertainty from future trials was quantified through the VOI analysis. We repeated these steps for each published RCT to estimate dynamic changes in VOI estimates.
Our cumulative NMA found that CABG conferred a lower, but not statistically significant, mortality than PCI (hazard ratio [HR], 0.90; 95% uncertainty interval, 0.80-1.05). MED had a nonsignificantly higher long-term mortality than PCI (HR, 1.11; 0.98-1.31) but significantly higher than CABG (HR, 1.07; 1.23-1.41). The greatest potential gains from future research would come from additional head-to-head trials between CABG v. PCI with the value of future research equaling 0.27 life years per patient.
The combination of cumulative NMA and VOI approaches can improve the efficiency of comparative effectiveness research by using all of the available evidence to determine future research priorities.