calculate_ceac: Calculate cost-effectiveness probabilities for two...

In Xa4P/pacheck: Probabilistic Analysis Check Package

Calculate cost-effectiveness probabilities for two strategies.

Description

This function calculates the probabilities that each strategy is the cost effective at different willingness to pay thresholds.

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. 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,
# for willlingness-to-pay thresholds of 0 to 50,0000 euros.
data("df_pa")
calculate_ceac(df = 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))

Xa4P/pacheck documentation built on April 14, 2025, 1:51 p.m.