/* Alignment Calibration for Microsimulation Models in Stata Jinjing Li version 0.98 Last Updated: 2 July 2013 Ref: Li, J., & Oâ€™Donoghue, C. (2014). Evaluating Binary Alignment Methods in Dynamic Microsimulation Models. Journal of Artificial Society and Simulation, 17(1). Syntax: alignmicro prob if a==1, target(0.8) outcome(variable) method(1) alignmicro prob if a==1, target(523) outcome(variable) method(sbp) prob: input probability target: percentage of positive outcome outcome: save aligned variable as a new one method: which method to use outprob: save the aligned probability (under development) [if]: alignmicro supports if conditions */ *! version 0.9.8 2Jul2013 program define alignmicro, rclass version 9.0 syntax varlist(min=1 max=1) [if], target(real) outcome(name) [method(string) outprob(name) est(string) weight(name) reserve(name)] * set trace on confirm new variable `outcome' if "`outprob'" !="" { confirm new variable `outprob' } if (`target'<0) { display as error "Invalid target ratio or target number" exit 198 } else if (`target' >1 ){ local totalalign=int(`target') if (`target' != `totalalign') { display as error "The number of events must be an integer" exit 198 } qui sum `varlist' `if' local target = `target' / r(N) } else if (`target' <=1) { qui sum `varlist' `if' local totalalign=round(`target'*r(N)) } timer clear 99 timer on 99 * method 1 : Multiplicative scaling if ("`method'" == "1" | "`method'" == "ms") { tempname np qui sum `varlist' `if' qui gen `np' = `varlist'*`target'/r(mean) `if' qui gen `outcome'=`np'>uniform() `if' if ("`outprob'"!="") { qui gen `outprob'=`np' `if' } local methodname "multiplicative scaling" } * method 2: sidewalk else if ("`method'" == "2" | "`method'" == "sidewalk") { tempname np npc npcd qui sum `varlist' `if' qui gen `np' = `varlist'*`target'/r(mean) `if' qui gen `npc'=sum(`np') `if' qui gen `npcd'=`npc' qui replace `npcd'=`npcd'[_n-1] if `npcd'==. qui gen `outcome'=(int(`npc')-int(`npcd'[_n-1]))==1 `if' local methodname "sidewalk basic" } * Sidewalk Hybrid Method with nonlinear adjustment else if ("`method'" == "3" | "`method'" == "sidewalknl") { tempname np order o u nlv qui gen `u'=uniform() `if' qui sum `varlist' `if' qui gen `np' =`varlist' `if' qui gen `nlv' = . qui sum `np' local diff=abs(`target'-r(mean)) while (`diff'>0){ local gp = ln(`target'/(1-`target'))-ln(r(mean)/(1-r(mean))) //extra adjustment in logit qui replace `nlv'=ln(`np'/(1-`np'))+ `gp' `if' qui replace `np' = exp(`nlv')/ (1+exp(`nlv')) `if' qui sum `np' `if' if (`diff' <= abs(`target'-r(mean))) { local diff = 0 qui replace `np' = exp(`nlv'-`gp')/(1+exp(`nlv'-`gp')) `if' //revert to optimal } else { local diff = abs(`target'-r(mean)) } } qui sum `np' `if' local out=0 local sidevar=0 qui gen `outcome'=. qui gen `order' = _n // move the observations under if condition first while save the orginal order in "order" qui egen `o' = rank(`order') `if', track qui sum `o' local obs=r(max) sort `o' forvalues i=1/`obs'{ local sidevar = `sidevar' + `np'[`i'] if ((`sidevar' - `out') > 0.5) { qui replace `outcome'=(`u'[`i'] - min(`np'[`i']/2,(1-`np'[`i'])/2,0.03))< `np'[`i'] in `i' } else { qui replace `outcome'=(`u'[`i'] + min(`np'[`i']/2,(1-`np'[`i'])/2,0.03))< `np'[`i'] in `i' } local out = `out' + `outcome'[`i'] } sort `order' local methodname "sidewalk hybrid" } * Central Limit Theorem else if ("`method'" == "4" | "`method'" == "clt") { tempname order o psum qui gen `outcome'=. local out=0 gen `order' = _n qui egen `o' = rank(`order') `if', unique qui sum `o' local obs=r(max) if ("`outprob'"!="") { local totalalign=`target'*`obs' } sort `o' //need a total probabilty and left probabilty qui egen `psum'=sum(`varlist') `if' local leftp=`psum'[1] forvalues i=1/`obs'{ qui replace `outcome' = (`varlist'[`i']/`leftp'*(`totalalign'-`out'))> uniform() in `i' local out = `out' + `outcome'[`i'] local leftp = `leftp' - `varlist'[`i'] } sort `order' local methodname "central limit" } * method 5: Sidewalk Shuffle without wraparound and zero setting Morrison (2006) (require m1) else if ("`method'" == "5" | "`method'" == "sidewalks") { tempname np npc qui sum `varlist' `if' qui gen `np' = `varlist'*`target'/r(mean) `if' tempname npcd qui gen `npc'=sum(`np')+uniform() `if' qui gen `npcd'=`npc' qui replace `npcd'=`npcd'[_n-1] if `npcd'==. qui gen `outcome'=(int(`npc')-int(`npcd'[_n-1]))==1 `if' local methodname "sidewalk" } * method 6 : Alignment by sorting (simple sorting) else if ("`method'" == "6" | "`method'" == "sbp") { tempname sortrank qui egen `sortrank'=rank(-`varlist') `if', unique qui sum `sortrank' `if' local obs=r(max) if ("`outprob'"!="") { local totalalign=`target'*`obs' } qui gen `outcome'=(`sortrank'<=`totalalign') `if' local methodname "SBP" } * method 7: Alignment by sorting (difference of p adjusted sorting) else if ("`method'" == "7" | "`method'" == "sbd") { tempname sortrank qui egen `sortrank'=rank(uniform()-`varlist') `if', unique qui sum `sortrank' `if' local obs=r(max) if ("`outprob'"!="") { local totalalign=`target'*`obs' } qui gen `outcome'=(`sortrank'<=`totalalign') `if' local methodname "SBD" } * method 8: Alignment by sorting (difference of logistic p adjusted sorting) else if ("`method'" == "8" | "`method'" == "sbdl") { tempname sortrank logitu qui gen `logitu'=-ln(1/uniform()-1) `if' qui egen `sortrank'=rank(`logitu'+ln((1-`varlist')/`varlist')) `if', unique qui sum `sortrank' `if' local obs=r(max) if ("`totalalign'"=="") { local totalalign=`target'*`obs' } qui gen `outcome'=(`sortrank'<=`totalalign') `if' local methodname "SBDL" } else { display as error "Invalid alignment method specified" exit 198 } * display "Alignment finished (`methodname'), output variable: `outcome'" timer off 99 qui timer list 99 return scalar ctime=r(t99) return local method "`method'" end