dectree_expected_values: Cost-effectiveness decision tree expected values

In n8thangreen/CEdecisiontree: Cost-effectiveness decision tree analysis

Cost-effectiveness decision tree expected values

Description

Root node expected value as the weighted mean of probability and edge/node values e.g. costs or QALYS.

Usage

dectree_expected_values(model, ...)

## S3 method for class 'tree_dat'
dectree_expected_values(model, ...)

## S3 method for class 'transmat'
dectree_expected_values(model, ...)

## S3 method for class 'dat_long'
dectree_expected_values(model, ...)

Arguments

model	Object of define_model() consisting of output of type tree_dat, transmat or dat_long
...	Additional parameters

Details

The expected value at each node is calculate by

\hat{c}_i = c_i + ∑ p_{ij} \hat{c}_j

The default calculation assumes that the costs are associated with the nodes. An alternative would be to associate them with the edges. For total expected cost this doesn't matter but for the other nodes this is different to assuming the costs are assigned to the nodes. The expected value would then be

\hat{c}_i = ∑ p_{ij} (c_{ij} + \hat{c}_j)

Value

Expected value at each node

See Also

define_model

Examples

data("cost")
data("probs")

my_model <-
  define_model(
    transmat = list(vals = cost,
                    prob = probs))

dectree_expected_values(model = my_model)

n8thangreen/CEdecisiontree documentation built on Sept. 13, 2022, 5:25 a.m.