Example 5 for PROC GENMOD
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: GENMEX5 */
/* TITLE: Example 5 for PROC GENMOD */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: generalized linear models, GEEs */
/* PROCS: GENMOD */
/* DATA: */
/* */
/* SUPPORT: sasgjj */
/* REF: PROC GENMOD, EXAMPLE 5. */
/* Stokes, M.E., Davis, C.S., and Koch, G.G (2000), */
/* Categorical Data Analysis Using the SAS System, */
/* Cary NC: SAS Institute. */
/* MISC: */
/****************************************************************/
data resp;
input center id treatment $ sex $ age baseline visit1-visit4;
datalines;
1 1 P M 46 0 0 0 0 0
1 2 P M 28 0 0 0 0 0
1 3 A M 23 1 1 1 1 1
1 4 P M 44 1 1 1 1 0
1 5 P F 13 1 1 1 1 1
1 6 A M 34 0 0 0 0 0
1 7 P M 43 0 1 0 1 1
1 8 A M 28 0 0 0 0 0
1 9 A M 31 1 1 1 1 1
1 10 P M 37 1 0 1 1 0
1 11 A M 30 1 1 1 1 1
1 12 A M 14 0 1 1 1 0
1 13 P M 23 1 1 0 0 0
1 14 P M 30 0 0 0 0 0
1 15 P M 20 1 1 1 1 1
1 16 A M 22 0 0 0 0 1
1 17 P M 25 0 0 0 0 0
1 18 A F 47 0 0 1 1 1
1 19 P F 31 0 0 0 0 0
1 20 A M 20 1 1 0 1 0
1 21 A M 26 0 1 0 1 0
1 22 A M 46 1 1 1 1 1
1 23 A M 32 1 1 1 1 1
1 24 A M 48 0 1 0 0 0
1 25 P F 35 0 0 0 0 0
1 26 A M 26 0 0 0 0 0
1 27 P M 23 1 1 0 1 1
1 28 P F 36 0 1 1 0 0
1 29 P M 19 0 1 1 0 0
1 30 A M 28 0 0 0 0 0
1 31 P M 37 0 0 0 0 0
1 32 A M 23 0 1 1 1 1
1 33 A M 30 1 1 1 1 0
1 34 P M 15 0 0 1 1 0
1 35 A M 26 0 0 0 1 0
1 36 P F 45 0 0 0 0 0
1 37 A M 31 0 0 1 0 0
1 38 A M 50 0 0 0 0 0
1 39 P M 28 0 0 0 0 0
1 40 P M 26 0 0 0 0 0
1 41 P M 14 0 0 0 0 1
1 42 A M 31 0 0 1 0 0
1 43 P M 13 1 1 1 1 1
1 44 P M 27 0 0 0 0 0
1 45 P M 26 0 1 0 1 1
1 46 P M 49 0 0 0 0 0
1 47 P M 63 0 0 0 0 0
1 48 A M 57 1 1 1 1 1
1 49 P M 27 1 1 1 1 1
1 50 A M 22 0 0 1 1 1
1 51 A M 15 0 0 1 1 1
1 52 P M 43 0 0 0 1 0
1 53 A F 32 0 0 0 1 0
1 54 A M 11 1 1 1 1 0
1 55 P M 24 1 1 1 1 1
1 56 A M 25 0 1 1 0 1
2 1 P F 39 0 0 0 0 0
2 2 A M 25 0 0 1 1 1
2 3 A M 58 1 1 1 1 1
2 4 P F 51 1 1 0 1 1
2 5 P F 32 1 0 0 1 1
2 6 P M 45 1 1 0 0 0
2 7 P F 44 1 1 1 1 1
2 8 P F 48 0 0 0 0 0
2 9 A M 26 0 1 1 1 1
2 10 A M 14 0 1 1 1 1
2 11 P F 48 0 0 0 0 0
2 12 A M 13 1 1 1 1 1
2 13 P M 20 0 1 1 1 1
2 14 A M 37 1 1 0 0 1
2 15 A M 25 1 1 1 1 1
2 16 A M 20 0 0 0 0 0
2 17 P F 58 0 1 0 0 0
2 18 P M 38 1 1 0 0 0
2 19 A M 55 1 1 1 1 1
2 20 A M 24 1 1 1 1 1
2 21 P F 36 1 1 0 0 1
2 22 P M 36 0 1 1 1 1
2 23 A F 60 1 1 1 1 1
2 24 P M 15 1 0 0 1 1
2 25 A M 25 1 1 1 1 0
2 26 A M 35 1 1 1 1 1
2 27 A M 19 1 1 0 1 1
2 28 P F 31 1 1 1 1 1
2 29 A M 21 1 1 1 1 1
2 30 A F 37 0 1 1 1 1
2 31 P M 52 0 1 1 1 1
2 32 A M 55 0 0 1 1 0
2 33 P M 19 1 0 0 1 1
2 34 P M 20 1 0 1 1 1
2 35 P M 42 1 0 0 0 0
2 36 A M 41 1 1 1 1 1
2 37 A M 52 0 0 0 0 0
2 38 P F 47 0 1 1 0 1
2 39 P M 11 1 1 1 1 1
2 40 P M 14 0 0 0 1 0
2 41 P M 15 1 1 1 1 1
2 42 P M 66 1 1 1 1 1
2 43 A M 34 0 1 1 0 1
2 44 P M 43 0 0 0 0 0
2 45 P M 33 1 1 1 0 1
2 46 P M 48 1 1 0 0 0
2 47 A M 20 0 1 1 1 1
2 48 P F 39 1 0 1 0 0
2 49 A M 28 0 1 0 0 0
2 50 P F 38 0 0 0 0 0
2 51 A M 43 1 1 1 1 0
2 52 A F 39 0 1 1 1 1
2 53 A M 68 0 1 1 1 1
2 54 A F 63 1 1 1 1 1
2 55 A M 31 1 1 1 1 1
;
data resp;
set resp;
visit=1; outcome=visit1; output;
visit=2; outcome=visit2; output;
visit=3; outcome=visit3; output;
visit=4; outcome=visit4; output;
run;
proc print data=resp (obs=20);
run;
proc genmod data=resp;
class id treatment(ref="P") center(ref="1") sex(ref="M")
baseline(ref="0");
model outcome(event='1')=treatment center sex age baseline / dist=bin;
repeated subject=id(center) / corr=unstr corrw;
run;
