input_mats class now contains an element
TRUE, then time intervals reset each time a patient enters a new health state. In other words, state values can depend on time since entering a health state.
To illustrate, consider an oncology application with three health states (stable disease, progressed disease, and death). In these models it is common to assume that patients begin second line treatment after disease progression. Suppose the second line treatment is a chemotherapy that patients take for 12 cycles (or approximately 1 year). Then drug costs would accrue for the first year but not afterwards.
State values like this can be specified by setting
time_reset = TRUE in
hesim_dat <- hesim_data(strategies = data.frame(strategy_id = c(1, 2)), patients = data.frame(patient_id = seq(1, 3)), states = data.frame(state_id = c(1, 2))) drugcosts <- stateval_tbl(tbl = data.frame(state_id = rep(c(1, 2), each = 2), time_start = c(0, 1, 0, 1), est = c(10000, 0, 12500, 0)), dist = "fixed", hesim_data = hesim_dat) drugcostsmod <- create_StateVals(drugcosts, time_reset = TRUE)
hesim now provides a general framework for integrating statistical models with economic evaluation. Users build a decision model by specifying a model structure, which consists of a set of statistical models for disease progression, utilities, and costs. Each statistical model is used to simulate outcomes as a function of estimated parameters and input data. N-state partitioned survival models (PSMs) and individual-level continuous time state transition models (iCTSTMs) are now supported.
Economic models—which combine the disease, utility, and cost models—are constructed with the following classes:
Disease models are constructed using the classes:
PsmCurves()to simulate survival curves for each endpoint of interest
IndivCtstmTrans()to simulate health state transitions with a iCTSTM
Utility and cost models are constructed with the
The economic models are used to simulate disease progression (
sim_stateprobs()), quality-adjusted life-years (QALYs) (
sim_qalys()), and costs (
sim_costs()). Parameter uncertainty is propagated to model outcomes using probabilistic sensitivity analysis. Summaries of the simulated costs and QALYs are used to perform model-based cost-effectiveness analyses (CEAs) and represent decision uncertainty with
The initial CRAN submission containing support for CEA but not for model development. Decision uncertainty is represented using cost-effectiveness planes, cost-effectiveness acceptability curves, cost-effectiveness acceptability frontiers, and the expected value of perfect information. CEAs by subgroup (i.e., individualized CEAs) are performed with