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Cost-effectiveness decision tree expected values

Source: R/dectree_expected_values_S3.R
dectree_expected_values.Rd

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 tree_dat
dectree_expected_values(model, ...)

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

# S3 method for 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

Value

Expected value at each node

Details

The expected value at each node is calculate by

$$\hat{c}_i = c_i + \sum p_{ij} \hat{c}_j$$

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

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

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)
#>    1    2    3    4    5    6    7 
#>  5.6 12.8  3.8 10.0  1.0 10.0  1.0