proc logistic descending data=mydata outest=myoutput covout;

model d=g e g*e c1 c2 c3;

run;

data PAoutput;

Set myoutput;

array mm {*} _numeric_;

b0=lag4(mm[1]);

b1=lag4(mm[2]);

b2=lag4(mm[3]);

b3=lag4(mm[4]);

v11=lag2(mm[2]);

v12=lag(mm[2]);

v13=mm[2];

v22=lag(mm[3]);

v23=mm[3];

v33=mm[4];

f1=(exp(b1)+(exp(b2)-2)*exp(b1+b2+b3))/((exp(b1+b2+b3)-1)*(exp(b1+b2+b3)-1));

f2=(exp(b2)+(exp(b1)-2)*exp(b1+b2+b3))/((exp(b1+b2+b3)-1)*(exp(b1+b2+b3)-1));

f3=((exp(b1)+exp(b2)-2)*exp(b1+b2+b3))/((exp(b1+b2+b3)-1)*(exp(b1+b2+b3)-1);

vINT=v11*f1*f1 + v22*f2*f2 + v33*f3*f3 + 2*v12*f1*f2 + 2*v13*f1*f3 + 2*v23*f2*f3;

PaINT=(exp (b2+b1+b3)-exp(b1)-exp(b2)+1)/((exp (b1+b2+b3)-1);

se_PaINT=sqrt(vINT);

ci95_lINT=PaINT-1.96*se_PaINT;

ci95_uINT=PaINT+1.96*se_PaINT;

keep PaINT ci95_lINT ci95_uINT;

if _n_=5;

run;

proc print data=PAoutput;

Var PaINT ci95_lINT ci95_uINT;

run;

The following Stata code will estimate the alternative attributable proportion measure and its confidence interval:

generate Ige = g*e

logit d g e Ige c1 c2 c3

nlcom (exp (_b [g] +_b [e] +_b [Ige])-exp (_b [g])-exp (_b [e]) +1)/(exp (_b [g] +_b [e] +_b [Ige])-1)