I tried to calculate the risk difference using a multivariable logistic model with the macro NLmeans. The following variables were included: group (binary) sex (binary) weight (continuous) year (continuous) treatment1 (binary) treatment2 (binary) treatment3 (binary) town (5 categories) However, when I run the code as shown below, it produces missing values.  proc logistic data = dat;    class group(ref="0") sex treatment1 treatment2 treatment3 town / param = glm;    model style(event="1") = group sex weight year treatment1 treatment2 treatment3 town / link = logit;    lsmeans group / e ilink;    ods output coef=coef1;    store clog1; run; %nlmeans(instore=clog1, coef=coef1, link=logit)   When I exclude the two continuous variables from the model, it works well. proc logistic data = dat;    class group(ref="0") sex treatment1 treatment2 treatment3 town / param = glm;    model style(event="1") = group sex treatment1 treatment2 treatment3 town / link = logit;    lsmeans group / e ilink;    ods output coef=coef2;    store clog2; run; %nlmeans(instore=clog2, coef=coef2, link=logit)   Is there any reason why the model fails to work when the continuous variables are included? ... View more

I found that the macro, nlmeans, is commonly used to calculate the risk difference along with its 95% confidence intervall. Is it appropriate to use this macro with the conditional logistic model (PROC LOGISTIC with the STRATA option) and the weighted model (PROC LOGISTIC with the WEIGHT option)?   The conditional model is used because the data is matched, while the weighted model is applied for the Inverse Probability of Treatment Weighting (IPTW) model. ... View more