Run Sensitivity Analysis

run_dsa(model, dsa)

Arguments

model	An evaluated Markov model.
dsa	An object returned by `define_dsa()`.

Value

A data.frame with one row per model and parameter value.

Examples

param <- define_parameters(
  p1 = .5,
  p2 = .2,
  r = .05
)
mod1 <- define_strategy(
  transition = define_transition(
    C, p1,
    p2, C
  ),
  define_state(
    cost = discount(543, r),
    ly = 1
  ),
  define_state(
    cost = discount(432, r),
    ly = .5
  )
)
#> No named state -> generating names.
#> No named state -> generating names.

mod2 <- define_strategy(
  transition = define_transition(
    C, p1,
    p2, C
  ),
  define_state(
    cost = 789,
    ly = 1
  ),
  define_state(
    cost = 456,
    ly = .8
  )
)
#> No named state -> generating names.
#> No named state -> generating names.

res2 <- run_model(
  mod1, mod2,
  parameters = param,
  init = c(100, 0),
  cycles = 10,
  cost = cost,
  effect = ly
)
#> No named model -> generating names.

ds <- define_dsa(
  p1, .1, .9,
  p2, .1, .3,
  r, .05, .1
)
print(ds)
#>    Low  High
#> p1 0.1  0.9 
#> p2 0.1  0.3 
#> r  0.05 0.1 

x <- run_dsa(res2, ds)
#> Running DSA on strategy 'I'...
#> Running DSA on strategy 'II'...

plot(x, value = "cost")
#> Joining, by = ".strategy_names"

# can be specified as a function of other parameters


ds2 <- define_dsa(
  p2, p1 - .1, p1 + .1
)

run_dsa(res2, ds2)
#> Running DSA on strategy 'I'...
#> Running DSA on strategy 'II'...
#> A sensitivity analysis on 1 parameters.
#> 
#> Parameters:
#>   -p2
#> 
#> Sensitivity analysis:
#> 
#>                   cost     ly       .n_indiv .par_value_eval Cost     Effect  
#> I, p2 = p1 - 0.1  394005.2 739.1975 100      0.4                0.000 0.000000
#> II, p2 = p1 - 0.1 615305.6 895.6790 100      0.4             2213.004 1.564815
#> I, p2 = p1 + 0.1  401420.1 782.0248 100      0.6                0.000 0.000000
#> II, p2 = p1 + 0.1 643828.5 912.8099 100      0.6             2424.084 1.307851
#>                   ICER     Cost Diff. Effect Diff. Ref. .nmb     .dnmb   
#> I, p2 = p1 - 0.1  -        -          -            -        0.00 -       
#> II, p2 = p1 - 0.1 1414.227 2213.004   1.564815     I    44731.44 44731.44
#> I, p2 = p1 + 0.1  -        -          -            -        0.00 -       
#> II, p2 = p1 + 0.1 1853.486 2424.084   1.307851     I    36811.45 36811.45