Hello @Clg,   Assuming that you mean NULLPROPORTIONDIFF, this is the negative non-inferiority margin in your example, i.e., the −d in section Farrington-Manning (Score) Test of the PROC FREQ documentation (under "Risks and Risk Differences").   Example: Suppose the standard therapy has a response rate of 32% (refproportion). The response rate of the new (experimental) therapy is assumed to be 6 percentage points higher (proportiondiff), i.e., 38%. The aim of the clinical trial is just to show that it's less than 2 percentage points lower (if not higher, of course) because that would suffice to claim that the new therapy is "not inferior" to the standard therapy -- in the sense that a response rate of, say, 30.1% would still be acceptable (perhaps in view of advantages regarding adverse events, costs, etc.).   Your PROC POWER step yields: Farrington-Manning Score Test for Proportion Difference Fixed Scenario Elements Distribution Asymptotic normal Method Normal approximation Number of Sides U Null Proportion Difference -0.02 Reference (Group 1) Proportion 0.32 Proportion Difference 0.06 Nominal Power 0.85 Alpha 0.05 Group 1 Weight 1 Group 2 Weight 1 Computed N Total Actual N Power Total 0.850 1022 That is, with n=1022/2=511 patients per group we would expect about 85% of a large number of hypothetical non-inferiority trials to confirm (at the alpha=5% level) that the experimental therapy is not inferior to the standard therapy.   Let's simulate 100,000 of those trials "Experimental vs. Standard": %let nsim=100000; %let n=%eval(1022/2); %let p1=0.32; %let pd=0.06; %let m=0.02; /* PROC FREQ needs the absolute value of the null proportion difference. */ data sim; call streaminit(27182818); do i=1 to &nsim; do g='E','S'; resp=1; k=rand('binom', &p1+(g='E')*&pd, &n); output; resp=0; k=&n-k; output; end; end; run; ods select none; ods noresults; ods output pdiffnoninf=pdni; proc freq data=sim; by i; weight k; tables g*resp / riskdiff(column=2 method=fm noninf margin=&m); run; ods select all; ods results; proc format; value pv low-0.05='significant' 0.05<-high='not significant'; run; ods exclude binomialtest; proc freq data=pdni; format pvalue pv.; tables pvalue / binomial; run; Result: Pr > Z Cumulative Cumulative PValue Frequency Percent Frequency Percent -------------------------------------------------------------------- significant 85197 85.20 85197 85.20 not significant 14803 14.80 100000 100.00 Binomial Proportion PValue = significant Proportion 0.8520 ASE 0.0011 95% Lower Conf Limit 0.8498 95% Upper Conf Limit 0.8542 Exact Conf Limits 95% Lower Conf Limit 0.8498 95% Upper Conf Limit 0.8542 Sample Size = 100000 So, the power (0.85) for the sample size obtained by PROC POWER has been confirmed.       ... View more