Sensitivity analyses
To explore parameter uncertainty of the model inputs, a probabilistic sensitivity analysis was conducted by randomly sampling from each of the parameter distributions (beta distribution in the case of relative risk, and utilities, Dirichlet distribution for multinomial data in the case of transition probabilities, and gamma distribution in the case of costs). The expected costs and expected QALYs for each treatment strategy were calculated using that combination of parameter values in the model. This process was replicated one thousand times (i.e., second-order Monte Carlo simulation) for each treatment option resulting in the expected cost-utility. Decision uncertainty is represented in the cost-effectiveness acceptability frontiers, which plot the probability that the treatment strategy with the maximum expected net monetary benefit is the most cost-effective over a range of willingness-to-pay threshold values. Net monetary benefit was calculated by multiplying effect by societal willingness to pay and subtracting cost, with willingness to pay set at a ratio of US$ 20,000 per QALY. We estimated the expected value of perfect information (EVPI). The EVPI is the maximum value that the health care system would be willing to pay for additional evidence to inform the reimbursement decision in the future. The population expected value of perfect information (PEVPI) was calculated to inform the expected cost of uncertainty (expected opportunity loss surrounding the decision) (17). Microsoft Exel ®was used in all analyzes.