Calculate cost-effectiveness probabilities.
calculate_ceac.RdThis function calculates the probabilities that each strategy is the most cost effective.
Usage
calculate_ceac(
df,
e_int,
e_comp,
c_int,
c_comp,
v_wtp = seq(from = 0, to = 1e+05, by = 1000)
)Arguments
- df
a dataframe.
- e_int
character. Name of variable of the dataframe containing total effects of the intervention strategy.
- e_comp
character. Name of variable of the dataframe containing total effects of the comparator strategy.
- c_int
character. Name of variable of the dataframe containing total costs of the intervention strategy.
- c_comp
character. Name of variable of the dataframe containing total costs of the comparator strategy.
- v_wtp
vector of numerical values. Vectors of willingness-to-pay threshold for which the probabilities of cost effectiveness have to be defined. Default is 0:100,000 by increments of 1,000.
Value
A dataframe with three columns: "WTP_threshold", "Prob_int", "Prob_comp". "WTP_threshold" contains the willingness-to-pay thresholds at which the probability of cost effectiveness has been calculated for both strategies. "Prob_int" contains the probability that the intervention strategy is cost effective at a given willingness-to-pay threshold. "Prob_comp" contains the probability that the comparator strategy is cost effective at a given willingness-to-pay threshold.
Examples
# Calculate probabilities of cost effectiveness using the example dataframe, for willlingness-to-pay thresholds of 0 to 50,0000 euros.
data("df_pa")
calculate_ceac(df_pa,
e_int = "t_qaly_d_int",
e_comp = "t_qaly_d_comp",
c_int = "t_costs_d_int",
c_comp = "t_costs_d_comp",
v_wtp = seq(from = 0, to = 50000, by = 1000))
#> WTP_threshold Prob_int Prob_comp
#> 1 0 0.0000 1.0000
#> 2 1000 0.0000 1.0000
#> 3 2000 0.0000 1.0000
#> 4 3000 0.0000 1.0000
#> 5 4000 0.0000 1.0000
#> 6 5000 0.0000 1.0000
#> 7 6000 0.0000 1.0000
#> 8 7000 0.0000 1.0000
#> 9 8000 0.0000 1.0000
#> 10 9000 0.0000 1.0000
#> 11 10000 0.0000 1.0000
#> 12 11000 0.0000 1.0000
#> 13 12000 0.0000 1.0000
#> 14 13000 0.0000 1.0000
#> 15 14000 0.0000 1.0000
#> 16 15000 0.0000 1.0000
#> 17 16000 0.0000 1.0000
#> 18 17000 0.0000 1.0000
#> 19 18000 0.0000 1.0000
#> 20 19000 0.0000 1.0000
#> 21 20000 0.0000 1.0000
#> 22 21000 0.0000 1.0000
#> 23 22000 0.0000 1.0000
#> 24 23000 0.0000 1.0000
#> 25 24000 0.0000 1.0000
#> 26 25000 0.0000 1.0000
#> 27 26000 0.0000 1.0000
#> 28 27000 0.0000 1.0000
#> 29 28000 0.0000 1.0000
#> 30 29000 0.0000 1.0000
#> 31 30000 0.0000 1.0000
#> 32 31000 0.0000 1.0000
#> 33 32000 0.0000 1.0000
#> 34 33000 0.0000 1.0000
#> 35 34000 0.0000 1.0000
#> 36 35000 0.0000 1.0000
#> 37 36000 0.0000 1.0000
#> 38 37000 0.0000 1.0000
#> 39 38000 0.0000 1.0000
#> 40 39000 0.0000 1.0000
#> 41 40000 0.0001 0.9999
#> 42 41000 0.0005 0.9995
#> 43 42000 0.0007 0.9993
#> 44 43000 0.0007 0.9993
#> 45 44000 0.0011 0.9989
#> 46 45000 0.0015 0.9985
#> 47 46000 0.0017 0.9983
#> 48 47000 0.0025 0.9975
#> 49 48000 0.0029 0.9971
#> 50 49000 0.0043 0.9957
#> 51 50000 0.0051 0.9949