calculate_icers: Calculate incremental cost-effectiveness ratios (ICERs)
In dampack: Decision-Analytic Modeling Package

Description Usage Arguments Value See Also Examples

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.

1	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.

plot.icers

## 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")