You could also use the NAMELEN= option on the PHREG statement to keep it from truncating the names of the effects. ... View more

If you are going to use the MCMC statement then you will need to create dummy variables for the categorical variables.  Unless you have a specific reason for doing so, you could avoid having to impute in two steps by simply using the FCS statement.  This would enable you to use the CLASS statement and avoid having to dummy code. ... View more

There isn't a way to combine F-statistics directly in SAS, although the process of doing so would be rather easy to program yourself basd on the rules in the paper below. https://www.sciencedirect.com/science/article/abs/pii/S2452306221001076  ... View more

You could use CONTRAST statements to test the nested models.  There are some good examples in the usage note linked below. 24447 - Examples of writing CONTRAST and ESTIMATE statements (sas.com)   ... View more

If you mean that it does not allow treatment effects that are more than 2 levels, then that is correct.  It does however allow multi-level predictors in the COVAR statement.   All that being said, if you are looking for a way in SAS to fit a causal mediation model with a treatment variable with more than 2 levels, then I am afraid that is not possible.   ... View more

From within SAS Viya there is a BOOTSTRAP option in CAUSALMED that uses bootstrap resampling to compute standard errors and confidence intervals for causal mediation effects and decompositions.  Below is a link to the documentation. SAS Help Center: Bootstrap Methods ... View more

To be clear, aren't you assuming that there is some relationship between PREF and both of the variables when you created your analysis model?  In other words when you fit this model: model pref (event='1')=race_eth2 d_year GestWeeks2 agegroup PrePriorARVStatusc2 TrimesterFst2 Mat_payor2 ROS_NYC2  tot_supp2 first_sup STI hep; you are assuming that there is some sort of relationship between PREF and each of the independent variables.  Your model is an attempt to quantify and in some sense qualify that relationship (predictive, linear in the logit, etc.).  It would be a contradiction of sorts to start out assuming they do not have any relationship (in the imputation model) and then test to see if there is a relationship (in the analysis model).   ... View more

Yes, that is what I was suggesting.   Schafer 1997 p.143 has an extended explanation, but the gist is that the imputation model should also include variables that are potentially related to the imputed variable and variables that are potentially related to the missingness of the imputed variable to produce the best imputation distributions. ... View more

If you look at the models for the two FCS LOGISTIC models, then you will see that PREF is not included as a predictor. fcs logistic ( Mat_payor2= d_year PrePriorARVStatusc2 ROS_NYC2 first_sup sup_preg); fcs logistic (TrimesterFst2= d_year PrePriorARVStatusc2 ROS_NYC2 first_sup sup_preg STI);   It does look like because you put it on the VAR statement and did not explicitly define the model for d_year that it was used in that model only. ... View more

By response variable I was referring to the outcome variable pref.  Most statisticians using multiple imputation would argue that it should be included in any imputation models in order to avoid biasing your results. ... View more

It might be helpful to read the section in the MI documentation related to the imputer's model versus the analyst's model.  The fact that you do not include the response variable in the imputation model for either of the variables may be biasing your results. SAS Help Center: Imputer’s Model Versus Analyst’s Model   The WARNING message you are receiving can have a number of possible causes.  The first is that there is some problem with the imputation model itself usually by leaving off an important variable (like the response variable).  In general, though, it occurs when the missing data has no influence on the sampling error of a parameter estimate. There is no fix or adjustment for that, but it does require some further investigation on your part.  Usually, it is an indication of a problem with the imputation model with possible causes ranging from a poor imputation model to no real need to impute due to a very small fraction of missing information.     Without fixing the problem, when the between imputation variance is zero then the number of Degrees of Freedom is undefined so you cannot get a confidence interval or p-values.  If you are unable to determine the cause then one suggestion would be that, if there are only minimum differences among these within-imputation variances, then look at the results for that variable for one of the imputations.   ... View more

This usage note should also be helpful in regards to your question.  It contains the full citation information for the Johnson and Young reference that @SteveDenham mentioned as well. 24475 - Handling the ERROR: "An imputed variable value is not in the specified range after 100 tries" (sas.com) ... View more

Please see the link to the usage note below.  While Proc GEE is not used directly, the examples at the bottom can all accommodate a model that uses Proc GEE.  23003 - Estimating a relative risk (also called risk ratio, prevalence ratio) (sas.com) ... View more

There is an INCLUDE= option on the MODEL statement in HPGENSELECT. SAS Help Center: MODEL Statement ... View more