Value of Information Analyses of Economic Randomized Controlled Trials: The Treatment of Intermittent Claudication

Objective: The aim of this study is to design the optimal study comparing endovascular revascularization and supervised exercise training for patients with intermittent claudication and to demonstrate value of information (VOI) analysis of patient-level data from an economic randomized controlled trial to guide future research. Methods: We applied a net benefit framework to patient-level data on costs and quality-of-life of a previous randomized controlled trial. VOI analyses were performed using Monte Carlo simulation. We estimated the total expected value of perfect information (total EVPI), the total expected value of sample information (total EVSI), the partial expected value of perfect information (partial EVPI), and the partial expected value of sample information (partial EVSI). These VOI analyses identified the key parameters and the optimal sample size of future study designs. Sensitivity analyses were performed to explore the robustness of our assumptions about the population to benefit, the willingness-to-pay threshold, and the study costs. The VOI analyses are demonstrated in statistical software (R) and a spreadsheet (Excel) allowing other investigators to apply VOI analysis to their patient-level data. Results: The optimal study design for the treatment of intermittent claudication involves a randomized controlled trial collecting data on the quality-adjusted life expectancy and additional admission costs for 525 patients per treatment arm. The optimal sample size remained between 400 and 600 patients for a willingness-to-pay threshold between 30,000 and 100,000/quality-adjusted life-years, for even extreme assumptions about the study costs, and for a range of 3 to 7 years that future patients will benefit from the results of the proposed study. Conclusions: 1) The optimal study for patients with intermittent claudication collects data on two key parameters for 525 patients per trial arm; and 2) we have shown that value of information analysis provides an explicit framework to determine the optimal sample size and identify key parameters for the design of future clinical trials.