Hi all,   I obtained an error message "Within-imputation Estimate missing for effect PGS_MEGALGC_APP_APOE*TIME in _Imputation_= 1 in the inputPARMS= data set" when I run the following code:   PROC MIXED DATA = IMP_IMM_LONG METHOD = RMEL; CLASS DBID FAMILY TIME(REF="2") GENDER; MODEL CS_IMM_LRN = PGS_MEGALGC_APP_APOE PGS_MEGALGC_APP_APOE*TIME AGEATVISIT TIME GENDER EDYEARS_CODED_MAX20 PC1 PC2 PC3 PC4 PC5 / CHISQ S DDFM = BW; RANDOM INTERCEPT / TYPE = UN SUBJECT = FAMILY G; REPEATED TIME / TYPE = CS SUBJECT = DBID(FAMILY) R; BY _IMPUTATION_; ODS OUTPUT SOLUTIONF = APP_IMM_BETA; RUN; PROC MIANALYZE PARMS(CLASSVAR=FULL) = APP_IMM_BETA; CLASS TIME GENDER; MODELEFFECTS PGS_MEGALGC_APP_APOE PGS_MEGALGC_APP_APOE*TIME AGEATVISIT TIME GENDER EDYEARS_CODED_MAX20 PC1 PC2 PC3 PC4 PC5; RUN; This error appears after SAS executed the PROC MIANALYZE procedure.   The parms data set I used for the PROC MIANALIZE procedure looks like this:   What should I do to avoid this error message? Any help is appreaciated!       ... View more

Hi all,   I have a data set like this (see below). There are duplicate observations within some survey waves. Now I want to remove the duplicate observations with the highest "visit age" from the sample. What should I do?   The original data set: ID WAVE Visit age 1231 1 45 1231 2 46 1231 2 47 1232 1 55 1233 1 56 1234 1 34 1234 1 35 1234 2 38   The dataset that I want: ID WAVE Visit age 1231 1 45 1231 2 46 1232 1 55 1233 1 56 1234 1 34 1234 2 38   Thank you very much! ... View more

Hi all,   I have a character ID variable like this   001 002 003 L8982 L8983 L8984   I want to add a leading zero only to those IDs whose first number is also number. In other words, I want   0001 0002 0003 L8982 L8983 L8984   Could anyone write a sample code for me to help me solve this problem? Any help is appreciated.     ... View more

Hi, I am a new SAS user. I am currently trying to replicate a paper using a mixed effect model to analyze the effect of genetic risk on cognitive functions using a longitudinal data set. In the article I would like to replicate, the authors state    " Genetic associations with cognitive function or AD-related biomarkers were tested using linear mixed-effects regression models implemented in the SAS MIXED procedure to account for the within-family and within-subject (due to repeated measures) correlations while allowing for missing data[28]. Each mixed-effects model included random intercepts for both family and participant. A random effect for time (age centered) was included when its inclusion led to a better model fit. "   I know how to account for within-subject correlation and give random intercept to each participant using the proc mixed procedure. Still, I don't know how to account for within-subject and within-family correlation at the same time while allowing the model to include random intercepts for both family and participant.    I am currently trying the following code, but I don't know if this code is consistent with what has been described by the method description I showed above. Also, running this code is too time-consuming, and I don't know if there are some other efficient methods I can use to save time. In this code, DBID is the unique individual ID; FAMILY is the unique family ID, VISNO is numeric time, TIME is character time. Thank you in advance if you can help me address this problem.   PROC MIXED DATA = PHENOTYPE_PGS_STD; CLASS DBID FAMILY TIME; MODEL Z_STAY_RCL = PGS_APP_DOLD AGEATVISIT GENDER VISNO / CHISQ S DDFM = BW; RANDOM INTERCEPT VISNO FAMILY / TYPE = UN SUBJECT = FAMILY G; REPEATED TIME / TYPE = UN SUBJECT = DBID R; RUN; ... View more