Simulate values (i.e., utility or costs) associated with health states in a state transition or partitioned survival model.
paramsParameters for simulating state values. Currently supports
objects of class tparams_mean or params_lm.
input_dataAn object of class input_mats. Only used for
params_lm objects.
methodThe method used to simulate costs and
quality-adjusted life-years (QALYs) as a function of state values.
If wlos, then costs and QALYs are
simulated by weighting state values by the length of stay in a health
state. If starting, then state values represent a one-time value
that occurs when a patient enters a health state. When starting is
used in a cohort model, the state values only accrue at time 0;
in contrast, in an individual-level model, state values
accrue each time a patient enters a new state and are discounted based on
time of entrance into that state.
time_resetIf FALSE then time intervals are based on time since
the start of the simulation. If TRUE, then time intervals reset each
time a patient enters a new health state. This is relevant if, for example,
costs vary over time within health states. Only used if method = wlos.
new()Create a new StateVals object.
sim()Simulate state values with either predicted means or random samples by
treatment strategy, patient, health state, and time t.
StateVals$sim(t, type = c("predict", "random"))check()Input validation for class. Checks that fields are the correct type.
# Simple sick-sicker example where drug costs vary by treatment strategy
# and over time. Prior to time = 5, costs are $10,000 for treatment strategy
# 1 and $5,000 for treatment strategy 2. After time = 5, costs are $2,000
# for both treatment strategies
## Setup the model
hesim_dat <- hesim_data(
strategies = data.frame(strategy_id = c(1, 2)),
patients = data.frame(patient_id = 1:3),
states = data.frame(state_id = c(1, 2), # Non-death states
state_name = c("sick", "sicker"))
)
## Drug costs vary by health state and time interval
drugcost_tbl <- stateval_tbl(
data.frame(
strategy_id = c(1, 1, 2, 2),
time_start = c(0, 5, 0, 5),
est = c(10000, 2000, 5000, 2000)
),
dist = "fixed"
)
drugcost_tbl
#> strategy_id time_id time_start time_stop est
#> <num> <int> <num> <num> <num>
#> 1: 1 1 0 5 10000
#> 2: 1 2 5 Inf 2000
#> 3: 2 1 0 5 5000
#> 4: 2 2 5 Inf 2000
## Create drug cost model
drugcostmod <- create_StateVals(drugcost_tbl, n = 1, hesim_data = hesim_dat)
## Explore predictions from the drug cost model
drugcostmod$sim(t = c(2, 6), type = "predict")
#> sample strategy_id patient_id state_id time value
#> <num> <int> <int> <int> <num> <num>
#> 1: 1 1 1 1 2 10000
#> 2: 1 1 1 1 6 2000
#> 3: 1 1 1 2 2 10000
#> 4: 1 1 1 2 6 2000
#> 5: 1 1 2 1 2 10000
#> 6: 1 1 2 1 6 2000
#> 7: 1 1 2 2 2 10000
#> 8: 1 1 2 2 6 2000
#> 9: 1 1 3 1 2 10000
#> 10: 1 1 3 1 6 2000
#> 11: 1 1 3 2 2 10000
#> 12: 1 1 3 2 6 2000
#> 13: 1 2 1 1 2 5000
#> 14: 1 2 1 1 6 2000
#> 15: 1 2 1 2 2 5000
#> 16: 1 2 1 2 6 2000
#> 17: 1 2 2 1 2 5000
#> 18: 1 2 2 1 6 2000
#> 19: 1 2 2 2 2 5000
#> 20: 1 2 2 2 6 2000
#> 21: 1 2 3 1 2 5000
#> 22: 1 2 3 1 6 2000
#> 23: 1 2 3 2 2 5000
#> 24: 1 2 3 2 6 2000
#> sample strategy_id patient_id state_id time value