The iviRA package is designed to simulate the costs, health outcomes, and risks associated with sequences of RA treatments. The simulation output can be used for value assessment using any framework preferred by the analyst.

## Cost-effectiveness analysis

Cost-effectiveness analysis (CEA) is a well-established technique for value assessment. The easiest way to conduct CEAs using output from the sim_iviRA function is with the hesim package, also developed by IVI. hesim is designed for simulation modeling and the economic evaluation of health technologies. It contains functions for summarizing a PSA given general simulation output at the subgroup level. Summary measures of interest include cost-effectiveness planes, cost-effectiveness acceptability curves, and the expected value of perfect information.

To prepare the data so that it can be analyzed with hesim, mean outcomes must be calculated for each simulation number and each treatment sequence.

n.inner <- max(sim.out\$sim)
sim.out[, hc_cost := tx_cost + hosp_cost + mgmt_cost + si_cost]
sim.out.means <- sim.out[, .(grp = 1,
qalys = sum(qalys)/n.inner,
dqalys = sum(qalys * .03)/n.inner,
hc_cost = sum(hc_cost)/n.inner,
dhc_cost = sum(hc_cost * .03)/n.inner),
by = c("sim", "txseq")]

The sim.out.means object can be analyzed in hesim using the icea and icea_pw functions, which are designed for individualized (i.e., subgroup) cost-effectiveness analysis. Since these functions are designed for subgroup analyses, a group variable must be specified. The current analysis does not specify subgroups so we assume a single large group. Analysis from a health care sector perspective would compare clinical effectiveness as measured by discounted QALYs (dqalys) to costs as measured by discounted health care sector costs (dhc_cost). An analysis from a societal perspective would add productivity losses to health care sector costs.

## Multi-criteria decision analysis

Some researchers have suggested multi-criteria decision analysis (MCDA) as an alternative to CEA. MCDA can be performed by selecting criteria among the output simulated from the sim_iviRA function. We adopt this approach in our web applications, which provide examples of the use of MCDA. Further information, on our approach can be found in our model description. For an introduction to the use of MCDA in health economics, see the 2016 ISPOR task force report.