Calculate the cost-effectiveness acceptability curve (CEAC) for a DES model with a PSA result

Usage

ceac_des(wtp, results, interventions = NULL, sensitivity_used = 1)

Arguments

wtp: Vector of length >=1 with the willingness to pay
results: The list object returned by run_sim()
interventions: A character vector with the names of the interventions to be used for the analysis
sensitivity_used: Integer signaling which sensitivity analysis to use

Value

A data frame with the CEAC results

Examples

res <- list(list(list(sensitivity_name = "", arm_list = c("int", "noint"
), total_lys = c(int = 9.04687362556945, noint = 9.04687362556945
), total_qalys = c(int = 6.20743830697466, noint = 6.18115138126336
), total_costs = c(int = 49921.6357486899, noint = 41225.2544659378
), total_lys_undisc = c(int = 10.8986618377039, noint = 10.8986618377039
), total_qalys_undisc = c(int = 7.50117621700097, noint = 7.47414569286751
), total_costs_undisc = c(int = 59831.3573929783, noint = 49293.1025437205
), c_default = c(int = 49921.6357486899, noint = 41225.2544659378
), c_default_undisc = c(int = 59831.3573929783, noint = 49293.1025437205
), q_default = c(int = 6.20743830697466, noint = 6.18115138126336
), q_default_undisc = c(int = 7.50117621700097, noint = 7.47414569286751
), merged_df = list(simulation = 1L, sensitivity = 1L))))

ceac_des(seq(from=10000,to=500000,by=10000),res)
#> # A tibble: 100 × 3
#> # Groups:   wtp [50]
#>      wtp comparator prob_best
#>    <dbl> <chr>          <dbl>
#>  1 10000 int                0
#>  2 10000 noint              1
#>  3 20000 int                0
#>  4 20000 noint              1
#>  5 30000 int                0
#>  6 30000 noint              1
#>  7 40000 int                0
#>  8 40000 noint              1
#>  9 50000 int                0
#> 10 50000 noint              1
#> # ℹ 90 more rows