/*WAY1：pearson χ2或deviance调整尺度参数，尺度参数*模型估计方差*/
proc genmod data=indt;
        class catevar;
        model depvar(EVENT='1')=indepvar catevar contvar / link=log dist=poisson scale=deviance type3;
        estimate "PR for indepvar" indepvar 1/EXP;
run;
       
/*WAY2：稳健Possion回归，引入sandwich方差，预测概率可能大于1，无法用于预测*/
proc genmod data=indt;
        class SUBJID;
        class catevar;
        model depvar(EVENT='1')=indepvar catevar contvar / link=log dist=poisson type3;
        repeated subject=SUBJID;
        estimate 'PR for indepvar' indepvar 1/EXP;
run;

/*WAY3：log-binomial回归COPY法：加权，唯一且收敛最大似然估计，无法用于预测*/
data logbin1;set indt;w=0.999;run;
data logbin2;set indt;depvar=1-depvar;w=0.001;run; /*raw depvar (0,1)；若depvar (1,2) 则depvar=3-depvar*/
data logbin;set logbin1 logbin2;run;
proc genmod data=logbin DESCENDING;
        weight w;
        class catevar;
        model depvar=indepvar catevar contvar / link=log dist=bin intercept=-1lrci;
        estimate 'PR for indepvar' indepvar 1/EXP;
run;