dampack (version 1.0.0)

calculate_icers: Calculate incremental cost-effectiveness ratios (ICERs)

Description

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.

Usage

calculate_icers(cost, effect, strategies)

Arguments

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.

Value

A data frame and icers object of strategies and their associated status, incremental cost, incremental effect, and ICER.

See Also

plot.icers

Examples

# NOT RUN {
## 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")
# }