The objective of deterministic sensitivity analysis is to assess how cost is sensitive to parameter values. Parameter values are changed individually (usually through upper and lower bounds) and the results on cost are reported.
Sensitivity analysis is distinct from probabilistic incertitude analysis: whereas in incertitude analysis the objective is to account for incertitude on parameter values in estimating model results, in sensitivity analysis the objective is to assess the sensitivity of results to variations of individual parameters.
Both analyses are complementary. Incertitude analysis is essential to decision making, while sensitivity analysis is useful to better understand model behaviour.
This example uses the HIV drug model defined in vignette("homogeneous", package = "heemod")
. Read this vignette for an explanation of the model.
In this example we will study the sensitivity of cost to 4 parameters:
rr
, the relative risk associated with the new treatment.cost_zido
and cost_lami
, the drug costs.dr
, the discount rate.Upper and lower values for the paramters are given to define_sensitivity()
.
se <- define_sensitivity(
rr = c(.4, .6),
cost_zido = c(1500, 3000),
cost_lami = c(1500, 3000),
dr = c(.04, .08)
)
We then run the sensitivity analysis with run_sensitivity()
, using res_mod
the result from run_models()
as input.
res <- run_sensitivity(
model = res_mod,
sensitivity = se
)
Two distinct plot are available. The basic plot displays cost variations for each model, around the base cost.
As expected the mono
model is not senstive to cost_lami
, since this drug was not given to this group. Similarly it is not sensitive to rr
, because this parameters only modifies transition probabilities in the other model.
plot(res, model = "mono")
On the other hand the comb
model is sensitive to all 4 parameters.
plot(res, model = "comb")
The difference plot displays the cost difference between the specified model comb
and the reference model mono
.
plot(res, model = "comb", type = "diff")