cea: A suite of commands for trial-based cost-effectiveness analysis

Cost-effectiveness analysis (CEA) is often conducted alongside a randomized clinical trial to establish whether the new therapy is likely to have a favorable value for its cost. One common approach is to estimate an incremental cost-effectiveness ratio (ICER), the marginal health benefit relative to the marginal cost, and compare the point estimate with a prespecified “willingness to pay”. Alternatively, net monetary benefit (NMB) may be used to keep benefits and costs linear and on the same scale. Costs and benefits may be modeled separately and often assume different distributions, especially for the ICER, where benefits are generally constrained to the -1 to 1 interval. CEA also involves use of graphs to assess uncertainty about the decision being made. The first, the cost-effectiveness plane, plots bootstrapped replicates of incremental cost against incremental benefits with confidence ellipses. The second, the cost-effectiveness acceptability curve (CEAC), is a plot showing the probability that a new treatment will be cost-effective at different willingness-to-pay values. In this presentation, we will introduce a new suite of Stata CEA commands. They use standard Stata command syntax to fit models and obtain the ICER or NMB and then provide comprehensive postestimation support and graphing.