Deterministic Sensitivity Analysis

Package: heemod

Maintained by

Kevin Zarca - kevin.zarca@gmail.com ( https://cran.r-project.org/web/packages/heemod/heemod.pdf and https://cran.r-project.org/web/packages/heemod/index.html)

What does this package do?

For more information on the full package functionality – see “Markov Models” page under “State Transition Models”. This page will focus on the deterministic sensitivity functions within the heemod package. This information is taken from the “Deterministic Sensitivity Analysis” Vignette (2019-10-22): https://cran.r-project.org/web/packages/heemod/vignettes/f_sensitivity.html This functionality reruns your Markov models with specified values for individual parameters.

How do I input my data to it/what inputs does it take?

Upper and lower values for the paramters are given to define_dsa().

library(heemod)

## Registered S3 method overwritten by 'pryr':
##   method      from
##   print.bytes Rcpp

se <- define_dsa(
  rr, .4, .6,
  cost_zido, 1500, 3000,
  cost_lami, 1500, 3000,  
  dr, .04, .08
)

& run the sensitivity analysis with run_dsa(), using res_mod is the model created [see https://cran.r-project.org/web/packages/heemod/heemod.pdf for how this would be structured]

# res_dsa <- run_dsa(
#   model = res_mod,
#   dsa = se
# )
# Note code is ## to allow for inclusion without defining e.g. res_mod within this section

What outputs do I get?

Res_dsa is the output given from the above function, the example given in the vignette gives the following (cut down for space):

## A sensitivity analysis on 4 parameters.
## 
## Parameters:
##   -rr
##   -cost_zido
##   -cost_lami
##   -dr
## 
## Sensitivity analysis:
## 
##                        cost_health cost_drugs cost_total life_year
## mono, cost_lami = 1500 46725886    19279596   48417031    8463.387
## comb, cost_lami = 1500 71019861    53642502   79593035   14198.651
## mono, cost_lami = 3000 46725886    19279596   48417031    8463.387
## comb, cost_lami = 3000 71019861    74940478   93812382   14198.651
## mono, cost_zido = 1500 46725886    12695081   43363208    8463.387
#The results of this can be then plotted into a tornado diagram using:
# plot(res_dsa,
#      strategy = "mono",
#      result = "cost",
#      type = "simple")

# Although note that whatever you put under “strategy” and “result” is what would be plot, so in the above case it would be the different cost estimates for the mono strategy. If you want the different ICER values plotted this code would then be; 
# plot(res_dsa,
#      strategy = "comb",
#      result = "icer",
#      type = "difference",
#      limits_by_bars = FALSE)

Other packages

See below info

Other helpful resources

Alternatively, if you write your model using a different package or functions (i.e. not heemod). Turn that whole modelling process into a function (see: https://www.dummies.com/programming/r/how-to-move-from-a-script-to-a-function-in-r/) …. that can then be run with different input values for your variables you are performing your deterministic sensitivity analyses on. For example;

DSA.cost <-  function(LCI.x, HCI.x) {
  #  LCI.x and HCI.x  higher and upper bounds for variable altering (cost of intervention)
  DSA <- data.frame(c("low.x", "high.x"),0L)
  colnames(DSA) <- c("variable","ICER")
  DSA[1,2] <- analysis(c.ill, LCI.x)  
 # analysis is the functional version of your model c.ill  highlights the variable you are changing #and LCI.x is the value you are changing it to
  DSA[2,2] <- analysis(c.ill, HCI.x)
  return(DSA)
}