Analyze Repeated Measures Data Using PROC GLIMMIX

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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 ?????; RUN;

  • 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.

1
sasdata-analysislongitudinal

1 Answers

0
votes

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;
    or
    (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:

  1. 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
  2. If you are still unsure, ask your question at communities.sas.com which might be able to help you better.

HTH