CEAE grapg

[Hiro farabi]

Hi, I have a dataset containing 50 pairs of incremental costs and incremental QALYs, along with their variances and covariances. I am going to graph the Cost-Effectiveness Acceptability Envelope (CEAE) using the following structure:

• I have 50 sets of results Using each set of results, I calculate the following 2 numbers. Then I should have 100 numbers.
  • gen NB=iqaly-TH*icost
  • gen varNB= TH^2 * varq + varc – 2*TH*cov
• Using those 100 numbers, I calculate the probability of intervention to be cost effective for each one TH value. The code is as the following:
  • egen meanNB=mean(NB)
  • egen W_NB=mean(varNB)
  • gen _Bdiff_NB=(NB-meanNB)^2
  • egen _Bsum_NB=total(_Bdiff_NB)
  • gen B_NB=(1/(50-1))*_Bsum_NB
  • gen T_NB=W_NB+(1+(1/50))*B_NB
  • gen seT_NB=sqrt(T_NB)
  • local z = meanNB/seT_NB
  • local prob = normal(`z')

My do-file is as follows, but at the end of the run, all the prob values are the same. Instead of generating a proper graph with a curve, it returns a straight line with no slope.

clear
set more off

* Define the range of thresholds (TH values)
local min_th = 0
local max_th = 100000
local step = 1000

* Create a new dataset to store probabilities at each TH
tempfile ceae_results
save `ceae_results', emptyok

* Loop over threshold values
forvalues th = `min_th'( `step' )`max_th' {

use your_original_dataset, clear

* Compute Net Benefit and its variance for each of the 50 sets
gen NB = `th'*iqaly - icost
gen varNB = (`th')^2 * varq + varc - 2*`th'*cov

* Get mean and variance components
egen meanNB = mean(NB)
egen W_NB = mean(varNB)
gen _Bdiff_NB = (NB - meanNB)^2
egen _Bsum_NB = total(_Bdiff_NB)
gen B_NB = (1/(50-1))*_Bsum_NB
gen T_NB = W_NB + (1 + (1/50))*B_NB
gen seT_NB = sqrt(T_NB)

* Calculate z-score and probability
gen z = meanNB / seT_NB
gen prob = normal(z)

* Store TH and prob in a new dataset
gen th = `th'
keep th prob
keep in 1 // only one row per TH needed
append using `ceae_results'
save `ceae_results', replace
}


Thank you

[Carlo Lazzaro]

Hiro:
you may want to take a look at the Supplemental Material of https://global.oup.com/academic/prod...?lang=en&cc=gb

[Hiro farabi]

Thank you so much Carlo!