Hi SAS experts,
I'm trying to find out whether hemoglobin of the study subjects increased or decreased over time adjusting to agecat stratifying by race. So that I'll be able to tell the direction of trend by race controlling for the effect of agecat. There is no control group. Each subject has repeated measurements with no equal intervals. In other words, measurements taken whever they showed up at clinics. Number of repeated measurements also vary across subjects. For example, pat_id has 2 measurements while pat_id may have 6 measurements so forth so on.
My attempt for modeling this scenario using proc glimmix is shown below.
My questions are;
1. Does model in SAS code make sense given my research question?
2. Do you have any idea why final output doesn't provide P-value?
Thanks a lot in advance for taking your precious time for help.
The similar problem was solved in the forum below. However,my context is different. My endpoint is continuous not binary.
https://communities.sas.com/t5/Statistical-Procedures/Analyzing-a-single-arm-study-with-repeated-mea...
DATA HAVE;
INPUT PAT_ID TIME AGEcat RACE HB1;
CARDS;
1 1 5 2 13.5
1 2 4 2 12.8
2 1 4 2 11.5
2 2 5 2 11.9
3 1 2 1 11.9
3 2 3 1 12.8
4 1 5 1 13.5
4 2 3 1 11.8
4 3 4 1 11.1
5 1 5 1 11.1
5 2 5 1 12.5
6 1 5 1 11.3
6 2 4 1 12
7 1 4 1 12.5
7 2 5 1 12.5
8 1 4 1 11.5
8 2 5 1 12.7
9 1 5 1 12.8
9 2 4 1 13.3
10 1 5 1 11.8
10 2 3 1 11.6
11 1 5 1 14.2
11 2 4 1 14.2
12 1 5 1 12.1
12 2 4 1 12.4
13 1 4 1 11.8
13 2 3 1 10.7
14 1 4 1 11.6
14 2 3 1 11.8
15 1 4 1 12.8
15 2 3 1 12.5
16 1 4 1 13.7
16 2 4 1 13.4
17 1 5 1 13.3
17 2 4 1 10.8
18 1 4 1 13.1
18 2 5 1 11.7
19 1 3 1 12.8
19 2 4 1 12.3
20 1 3 1 12.5
20 2 4 1 13.2
21 1 4 1 13.3
21 2 5 1 12.9
21 3 3 1 12.1
22 1 3 1 12.9
22 2 5 1 11.2
22 3 2 1 11.9
23 1 3 1 12.8
23 2 4 1 11.8
24 1 3 1 12.8
24 2 5 1 12.2
25 1 3 1 11.2
25 2 5 1 11.4
26 1 4 1 11.6
26 2 4 1 11.4
27 1 5 1 11.8
27 2 4 1 12.2
28 1 4 1 11.1
28 2 5 1 11.6
28 3 3 1 12.1
29 1 3 1 12
29 2 5 1 11.5
30 1 4 1 12.9
30 2 5 1 13.5
31 1 4 9 10.3
31 2 4 9 12.7
32 1 4 3 10.8
32 2 5 3 10.8
33 1 5 1 12.3
33 2 4 1 12
34 1 5 1 12.3
34 2 5 1 12.2
35 1 4 1 12.4
35 2 5 1 12.4
36 1 4 1 13.2
36 2 5 1 10.1
37 1 4 1 12.4
37 2 3 1 14.4
38 1 4 1 10.5
38 2 3 1 12.4
39 1 5 1 13.8
39 2 5 1 12
40 1 5 1 12.9
40 2 3 1 13.1
41 1 4 1 12.7
41 2 4 1 12.3
42 1 2 3 12.2
42 2 4 3 11.9
42 3 4 3 11.1
43 1 2 1 12.8
43 2 3 1 13.3
44 1 5 1 11.6
44 2 4 1 11.8
45 1 5 3 14.2
45 2 4 3 12.6
46 1 4 1 11.6
46 2 5 1 13.6
47 1 5 1 12.7
47 2 3 1 11.1
;
PROC PRINT; RUN;
proc glimmix data=HAVE;
class TIME PAT_ID RACE;
model HB1 = TIME RACE TIME*RACE/solution;
random intercept / subject=PAT_ID;
random intercept / subject=PAT_ID(TIME) type=UN;
LSMEANS TIME RACE TIME*RACE/DIFF;
run;
