Making Apples-to-Apples Comparisons Between Internal and External Studies Using Propensity Scores (3)

 

Examples using SAS:

SAS code 1:

title 'PSMATCH: ATT Weighting';

proc psmatch data=t1 region=treated;

class trt genotype;

psmodel trt(Treated='study treatment')= b_age genotype b_var;

where vsn=0;

assess lps var=(b_age genotype b_var)/ weight=attwgt;

output out(OBS=region)=Outex;

run;


SAS code 2:

title 'PSMATCH: ATT Weighting';

proc psmatch data=t1 region=allobs(psmin=0.05 psmax=0.95);

class trt genotype;

psmodel trt(Treated='study treatment')= b_age genotype b_var;

where vsn=0;

assess lps var=(b_age genotype b_var)/ weight=attwgt;

output out(OBS=region)=Outex1;








Aspect region=treated region=allobs(psmin=0.05 psmax=0.95)
Who gets dropped Controls outside treated PS range Both groups outside [0.05,0.95]
Treated retention All treated kept Some treated may be dropped
Target estimand ATT in full treated population
ATT in trimmed treated population
Weight stability / ESS Potentially lower ESS (more extreme weights) Typically higher ESS (fewer extremes)
Bias–variance trade‑off Better generalizability to all treated; more variance Better precision; less generalizable



When to use which

  • Use region=treated if your primary estimand is ATT in all treated and you prefer not to discard any treated subjects. Be prepared to assess weight extremes and ESS.

  • Use region=allobs(psmin=…, psmax=…) if you want to stabilize the analysis (trim tails), accepting a narrower target population for better precision.

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