calculate_ceac_mult: Calculate cost-effectiveness probabilities.
| calculate_ceac_mult | R Documentation |
Calculate cost-effectiveness probabilities.
Description
This function calculates the probabilities that each strategy is the cost effective at different willingness to pay thresholds, for an infinite amount of strategies.
Usage
calculate_ceac_mult(
df,
outcomes,
costs,
v_wtp = seq(from = 0, to = 1e+05, by = 1000)
)
Arguments
df |
a dataframe. |
outcomes |
character. Vector of variable names containing the outcomes to be plotted on the x-axis. The variable names should be structured as follows: 't_qaly_d_' followed by the name of the strategy: e.g. 't_qaly_d_intervention'. |
costs |
character. Vector of variable names containing the costs to be plotted on the y-axis. The variable names should be structured as follows: 't_costs_d_' followed by the name of the strategy: e.g. 't_costs_d_intervention'. |
v_wtp |
vector of numerical values. Vector 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 = The willingness-to-pay thresholds at which the probability of cost effectiveness has been calculated for both strategies
Prob_int = The probability that the intervention strategy is cost effective at a given willingness-to-pay threshold
Prob_comp = 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,
data("df_pa")
df_pa$t_qaly_d_int2 <- df_pa$t_qaly_d_int * 1.5 # creating additional outcome variable
df_pa$t_costs_d_int2 <- df_pa$t_costs_d_int * 1.5 # creating additional cost variable
calculate_ceac_mult(df = df_pa,
outcomes = c("t_qaly_d_int", "t_qaly_d_comp", "t_qaly_d_int2"),
costs = c("t_costs_d_int","t_costs_d_comp", "t_costs_d_int2")
)
