Hello, I am doing a Poisson regression using multiply imputed data to try to get a pooled estimate across my imputed datasets. I was told that the easiest way is to probably use PROC PLM coding. I need to combine doing PROC PLM with then doing mianalyze to account for the 5 imputed datasets. I have seen this thread: https://communities.sas.com/t5/Statistical-Procedures/How-to-get-pooled-results-for-PROC-PLM-on-imputed-data/td-p/413755 and it seems close to what I want to do, but I cannot get the code to run corrects. proc genmod data = cancer.cancer_mi; class cryo_reason_num / descending; model oocytes_retrieved = cryo_reason_num / dist = poisson link = log; by _imputation_; where CycleCancelled = "N"; ods output ParameterEstimates = est; store stored_model; run; proc plm source = stored_model; show parameters; run; proc plm source = stored_model; lsmeans cryo_reason_num / ilink cl; run; The above code gives me 5 separate datasets (one for each imputation) that all look like exactly what I want to get one estimate for. Below is the part giving me trouble from the link posted above: proc plm restore = stored_model; class cryo_reason_num; estimate 'Cancer' cryo_reason_num 1 / category=separate; estimate 'Other Infertile' cryo_reason_num 2/ category=separate; estimate 'Other Medicale' cryo_reason_num 3 / category=separate; ods output Estimates=est_ds; run; proc sort data=est_ds; by cryo_reason_num; run; proc mianalyze data=est_ds; by cryo_reason_num; modeleffects estimate; stderr stderr; run; I would appreciate any help!
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