Hi,   I have a large dataset with >1,000,000 observations for ~10,000 patients.   Each patient has a unique ID.     data have; input ID ICD10 $; datalines; 11111 E0800 11111 E0801 11111 E0803 22222 J45909 22222 J45908 33333 G4001 33333 G4002 33333 G4003 44444 E8883 44444 E8882 ; run;     I need to add an age variable to the main dataset with >1,000,000 observations.   I have a separate age dataset which has the age variable and corresponding patient ID.     data age; input ID age; datalines; 1 57 2 64 3 67 4 81 ; Run;     Before merging the two datasets, I have set the length and format for the ID variable on which I am merging,     data have; length ID $ 5; set have; format ID $8.; informat ID $10.; run; data age; length ID $ 5; set age; format ID $8.; informat ID $10.; run; /*merge have and age datasets*/ Proc sort data=have; by ID; run; Proc sort data=age; by ID; run; data merged; merge have (in=a) age(in=b); by ID; if a and b; run;     However, I am encountering issues where age does not appear in the merged dataset for all observations (even though the ID does have an age value available in the age dataset), for example:     Data merged; Input ID ICD10 $ age; Datalines; 11111 E0800 . 11111 E0801 57 11111 E0803 57 22222 J45909 64 22222 J45908 . 33333 G4001 67 33333 G4002 . 33333 G4003 . 44444 E8883 . 44444 E8882 81 ; run;       When I first discovered the issue, I also set the ID informat the same in both datasets (above code), in an attempt to see if this would resolve the issue (but it has not).   Any help would be greatly appreciated.   Thank you in advance,   ... View more

Hi,   I have a dataset (sample below) which reports the number of years worked by teachers in the same school.   Among teachers with a Bachelor's degree, I want to look at the independent effect of male teachers on the number of years worked.   The outcome (number of years worked) is continuous, and the independent variables (Bachelor's degree and male sex) are binary (1,2; yes/no).   data results; input ID bachelors male years_worked; datalines; 1 0 1 10 2 1 1 17 3 1 0 16 4 0 0 8 5 1 1 27 6 1 0 10 7 0 0 9 8 0 1 4 9 0 1 6 10 1 0 12 ; run;   I'm wondering if it's correct to use PROC GENMOD for this?     proc genmod data=results; class bachelors(ref='0'); model years_worked= bachelors male bachelors*male / dist=gamma; run;   Among teachers with a Bachelor's degree, I want to be able to interpret the effect of male sex on the number of years worked, and state if there is any difference.   I'm not sure if I have the GENMOD statement specified correctly, and how I should interpret this?   Also how can I see the difference in number of years worked among teachers with a Bachelor's degree who are male vs those who are not male?   I'd really appreciated any help, thank you ... View more