Use the following program to generate the summary 95% confidence intervals
cap program drop clustercibyRF
program define clustercibyRF , rclass
args outcome byVar
return clear
return local indicator = "`outcome'"
return local byVar = "`byVar'"
qui svy linearized : proportion `outcome', over(`byVar')
cap mat drop all
mat all = r(table)
mat all = all' // mat list all
qui svy linearized : proportion `outcome'
cap mat drop all2
mat all2 = r(table)
mat all2 = all2'
qui levelsof `outcome', local(outcomeLevels)
qui levelsof `byVar', local(byVarLevels)
local x = 1 // COUNTER FOR OUTCOME RESULTS IN ByVar-WISE ANALYSIS - ROWS PER OUTCOME LEVEL (ONE FOR EACH SEX)
foreach o of local outcomeLevels { // of local levels in 1
local outcomeName`o' : label(`outcome') `o' // local outcome = ustrregexra(`"`outcomeName'"', " ", "_")
local outcomeCode`o' = `o' //di "`outcomeName' `outcomeCode'"
foreach byVarLevel of local byVarLevels {
local byVarLevelName`byVarLevel' : label(`byVar') `byVarLevel' // local outcome = ustrregexra(`"`outcomeName'"', " ", "_")
local byVarLevelCode`byVarLevel' = `byVarLevel' //di "`outcomeName' `outcomeCode'"
qui count if `byVar'==`byVarLevel' & `outcome'!=. //male
local denom`byVarLevel' = r(N)
qui count if `byVar'==`byVarLevel' //male
local sample`byVarLevel' = r(N)
local prop_`o'_`byVarLevel' : di %4.1f all[`x',1]*100 // Cluster-adjusted prevalence outcome level
local lb_`o'_`byVarLevel' : di %4.1f all[`x',5]*100 // Cluster-adjusted prevalence lower CI
local ub_`o'_`byVarLevel' : di %4.1f all[`x',6]*100 // Cluster-adjusted prevalence lower CI
local x = `x' + 1
}
local y = `y' + 1
}
local levelsOfOutcome = `y'
local levelsOfbyVar = (`x' - 1 )/ `y'
local headings ""
foreach o of local outcomeLevels {
local headings "`headings' `outcomeName`o'' "
}
di "`headings'"
foreach byVarLevel of local byVarLevels {
local result "`denom`byVarLevel'' "
foreach o of local outcomeLevels {
local result "`result' `prop_`o'_`byVarLevel'' (`lb_`o'_`byVarLevel'', `ub_`o'_`byVarLevel'') "
}
local result " `result' - `byVarLevelName`byVarLevel'' "
di "`result'"
}
endCode language: Stata (stata)Usage
use "http://www.stata-press.com/data/r15/nhanes2b", clear
svyset psuid [pweight=finalwgt], strata(stratid)
cap drop bmi
gen bmi = weight / ((height/100) * (height/100))
cap drop bmicat
egen bmicat = cut(bmi), at(0 18.5 25 30 100) lab
tab bmicat race
clustercibyRF bmicat race
Code language: JavaScript (javascript)RESULTS
. clustercibyRF bmicat race
0- 18.5- 25- 30-
9065 3.3 ( 2.9, 3.7) 51.5 (49.9, 53.1) 31.8 (30.5, 33.1) 13.4 (12.6, 14.4) - White
1086 2.6 ( 1.6, 4.4) 43.5 (38.6, 48.4) 30.9 (26.9, 35.2) 23.0 (19.0, 27.5) - Black
200 6.8 ( 3.7, 12.1) 59.7 (55.0, 64.2) 23.8 (19.5, 28.7) 9.8 ( 7.1, 13.2) - Other
Code language: CSS (css)You get the N for each category of race, and the Percent and 95% CI for each category of BMI. The First row depicts the order of the results
