Calculate incremental cost-effectiveness ratios (ICERs)
This function takes in strategies and their associated cost and effect, assigns them one of three statuses (non-dominated, extended dominated, or dominated), and calculates the incremental cost-effectiveness ratios for the non-dominated strategies
The cost-effectiveness frontier can be visualized with plot, which calls plot.icers.
An efficent way to get from a probabilistic sensitivity analysis to an ICER table
is by using summary on the PSA object and then using its columns as
inputs to calculate_icers.
calculate_icers(cost, effect, strategies)
cost |
vector of cost for each strategy |
effect |
vector of effect for each strategy |
strategies |
string vector of strategy names With the default (NULL), there is no reference strategy, and the strategies are ranked in ascending order of cost. |
A data frame and icers object of strategies and their associated
status, incremental cost, incremental effect, and ICER.
## Base Case
# if you have a base case analysis, can use calculate_icers on that
data(hund_strat)
hund_icers <- calculate_icers(hund_strat$Cost,
hund_strat$QALYs,
hund_strat$Strategy)
plot(hund_icers)
# we have so many strategies that we may just want to plot the frontier
plot(hund_icers, plot_frontier_only = TRUE)
# see ?plot.icers for more options
## Using a PSA object
data(psa_cdiff)
# summary() gives mean cost and effect for each strategy
sum_cdiff <- summary(psa_cdiff)
# calculate icers
icers <- calculate_icers(sum_cdiff$meanCost,
sum_cdiff$meanEffect,
sum_cdiff$Strategy)
icers
# visualize
plot(icers)
# by default, only the frontier is labeled
# if using a small number of strategies, you can label all the points
# note that longer strategy names will get truncated
plot(icers, label = "all")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.