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I am using PROC GLIMMIX to analyze repeated measures data about specific sexual events. The original data came from a weekly diary study of about 400 people. During each week they reported on behaviours from their most recent sexual encounter. We also have basline data on their demographics. 12 weeks of observation were collected and we had a high completion rate.
I would like to create a mixed effect model, but I am unsure exactly how this is done in SAS. I want to test the effect of event-specific factors as well as some person level demographics and would like to get odds ratios for each factor of interest. The outcome is whether or not drugs were used during the event and the explanatory factors will be things like age, gender, etc. as well as characteristics about the event (i.e., partner HIV status), whether the partner was a regular sexual partner, etc..
The code I'm working with follows this pattern:
PROC GLIMMIX DATA=work.dataset oddsratio;
CLASS VISIT_NUMBER PARTICIPANT_ID BINARY_EVENTLEVEL_OUTCOME BINARY_EVENTLEVEL_EXPLANATORY_FACTOR CATEGORICAL_PERSONLEVEL_EXPLANATORY_FACTOR;
MODEL BINARY_EVENTLEVEL_OUTCOME = BINARY_EVENTLEVEL_EXPLANATORY CATEGORICAL_PERSONLEVEL_EXPLANATORY_FACTOR /DIST=binary link=logit CL S ddfm=kr;
RANDOM ?????;
option 1 for ?????:
residual
/ subject=PARTICIPANT_ID
option 2 for ?????: INTERCEPT / subject=PARTICIPANT_ID
option 3 for ?????: VISIT_NUM / subject=PARTICIPANT_ID residual type=ar(1)
INTERCEPT / subject=VISIT_NUM(PARTICIPANT_ID)
option 4 for ?????: Other?
I am also unclear whether I should use ddfm=kr in my model statement or method=laplace in my proc statement -- both have been recommended elsewhere for this sort of repeated measures analysis.
I've come across several potential options for modelling this which often give similar results, but option 1 gives a statistically significant result for an event-level, while the others give non-significant results. The inclusion of the ddfm=kr makes the result of interest more significant. The method=laplace does not allow for option 1.
I may not be answering your question, but might be able to provide a couple of directions:
To start with the simplest part, your
MODEL
statement looks correct to me as you want to test
event-level factors
and
person-level demographics
which are thus considered as
fixed effects
.
Now, as far as the
random effects
are concerned:
the
RANDOM
statements you propose for
options (1) and (2)
:
(1)
RANDOM _residual_ / subject=PARTICIPANT_ID;
(2)
RANDOM intercept / subject=PARTICIPANT_ID;
are modeling two different parts of the random effects: the
R-side
and the
G-side
, respectively.
If you are already familiar with
PROC MIXED
, you may want to notice that your option (1) of using
RANDOM _residual_
in
PROC GLIMMIX
is equivalent to using the
REPEATED
statement in
PROC MIXED
that tells that you have repeated measures for
PARTICIPANT_ID
, which is clearly your case (Ref:
"Comparing the GLIMMIX and MIXED Procedures"
)
As for
option (3)
:
RANDOM VISIT_NUM / subject=PARTICIPANT_ID residual type=ar(1) INTERCEPT / subject=VISIT_NUM(PARTICIPANT_ID);
here you are modeling the time component of the repeated measures (
visit_num
) as a random effect, and this should be included when you believe that there would be a random variation of the response at each of the measurements times (i.e. at each event). At first glance, I don't believe this is relevant in your case, since you are taking this into account already by the fixed effects... but of course I may be wrong by not seeing your data.
Up to here is what I can contribute at this time.
As
next steps
for you to have a better understanding, I would suggest that you:
Read the Overview of the
PROC GLIMMIX
documentation, in particular the
mathematical model specification
and all 3 sections therein:
The Basic Model
G-Side and R-Side Random Effects and Covariance Structures
Relationship with Generalized Linear Models
If you are still unsure, ask your question at
communities.sas.com
which might be able to help you better.
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