Let me put the question differently: How would one conceptualise PROC MIANALYZE for this peace of code?:   proc logistic data=mi_olink outest=outcoxreg1 covout; by _imputation_; model i_dem (event='1') = age_cat0 age_cat1 p02sex educ_cat0 educ_cat1 active_cat0 active_cat1 bmi_cat0 bmi_cat1 p_cvd p_diab depr_cat0 depr_cat1 apoe_cat0 apoe_cat1 apoe_cat2 apoe_cat4 apoe_cat5 &biom_trans.; UNITS &biom_trans.=SD; run; ... View more

Thanks for your answer Rob! I'm not sure, if I totally understood it, but here's the log which is produced by running the code: NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=1 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=2 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=3 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=4 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=5 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=6 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=7 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=8 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=9 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=10 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=11 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=12 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=13 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=14 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=15 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=16 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=17 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=18 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=19 NOTE: PROC LOGISTIC is modeling the probability that i_dem=1. NOTE: Convergence criterion (GCONV=1E-8) satisfied. NOTE: The above message was for the following BY group: Imputationsnummer=20 NOTE: There were 25160 observations read from the data set WORK.MI_OLINK. NOTE: The data set WORK.OUTCOXREG1 has 420 observations and 27 variables. NOTE: PROZEDUR LOGISTIC used (Total process time): real time 0.82 seconds cpu time 0.81 seconds NOTE: The data set WORK.PARMSDAT has 19 observations and 11 variables. NOTE: PROZEDUR MIANALYZE used (Total process time): real time 0.04 seconds cpu time 0.07 seconds NOTE: PROZEDUR SQL used (Total process time): real time 0.01 seconds cpu time 0.00 seconds ... View more

Hi Rick, thanks for your answer! I checked the BY groups again. In all of the 20 imputations I'm getting the same kind of results: for all of the variables except the biomarker, the results for the OR are "normal". Only in case of the biomarker I get these extreme ORs (see below).   Effect Point estimate 95% Wald Confidence limits   age_cat0 3.928 2.707 5.699 age_cat1 8.567 5.816 12.619 P02SEX 1.383 0.998 1.918 educ_cat0 0.848 0.521 1.381 educ_cat1 0.828 0.499 1.376 active_cat0 0.509 0.346 0.749 active_cat1 0.502 0.325 0.774 bmi_cat0 0.763 0.525 1.109 bmi_cat1 0.87 0.564 1.341 p_cvd 1.154 0.812 1.639 p_diab 1.609 1.09 2.376 depr_cat0 0.995 0.581 1.707 depr_cat1 1.503 0.674 3.35 apoe_cat0 0.54 0.097 3.012 apoe_cat1 1.524 0.974 2.386 apoe_cat2 2.831 1.222 6.56 apoe_cat4 2.05 1.429 2.941 apoe_cat5 14.017 5.24 37.497 mi2_uPA <0.001 <0.001 >999.999   One comment to the multiple imputation: The biomarker data had no missings! The dataset was imputed because of missings in other variables.   To your other comments: I agree with you that an extreme OR is not intrinsically wrong, but after I got "normal" ORs in the analysis based on a dataset including only one of the 20 imputations and I'm getting such extreme values for the whole dataset, this sets off my alarm bells. There was no special reason for using dummy variables instead of the class statment.   ... View more