Posted by
Bruce Weaver on
Mar 16, 2021; 1:43pm
URL: http://spssx-discussion.165.s1.nabble.com/Interpretation-of-interaction-with-time-varying-predictor-Growth-model-tp5740294p5740302.html
The original model...
MIXED Pain WITH Time Dep (etc.)
... treated Time as a quantitative variable and included the linear term
only. In other words, the fitted values are forced to fall on a straight
line. What Rich has suggested below is that a straight line probably
doesn't provide the best fit. Given the large sample size, and the
relatively small number of time points, I think you can afford to treat Time
as categorical (BY Time rather than (WITH Time) and include contrasts of
interest. IIRC, MIXED has no /CONTRAST sub-command, so you'll likely have
to use /TEST to and generate the codes for the contrasts you want. Helmert
contrasts (each level vs the mean of all subsequent levels) might make
sense, given Rich's expectation, and would have the virtue of being
orthogonal. Repeated (aka., adjacent) contrasts (1v2, 2v3, 3v4, 4v5) would
be another option, but they are not orthogonal.
HTH.
Rich Ulrich wrote
> I have problems with the statistical design, particularly because
> I've been involved with studies of Depression. And it seems that
> your Pain may have this same feature: It is HIGH at start, and
> decreases rapidly to Time 2, after which it stays the same or
> decreases a bit more.
>
> For my analyses, I looked at Time 1 vs. 2, then 2-5, in order to
> separate the huge changes from the lesser ones. If you have that
> sort of time profile, I recommend doing the same. It reduces the
> complications of interpretation.
>
> I don't know how your Depression is measured, but I know that
> many Dep scales do have a somatic component -- like, severe pain
> will interfere with ability to sleep, and bad sleep can count towards
> Depression. (Somatic "depression" items were horrible, but not
> the "sadness" ones, on a tuberculosis ward in the 1960s.)
>
> --
> Rich Ulrich
> ________________________________
> From: SPSSX(r) Discussion <
> SPSSX-L@.UGA
> > on behalf of Oliver <
> momartel.bwh.harvard@
> >
> Sent: Monday, March 15, 2021 2:40 PM
> To:
> SPSSX-L@.UGA
> <
> SPSSX-L@.UGA
> >
> Subject: Interpretation of interaction with time-varying predictor (Growth
> model)
>
>
> Hi everyone,
>
> I have a question regarding the interpretation of an interaction effect
> involving time-varying (i.e., Level 1) variables in a growth model. In
> this
> study, participants (n = 1000) provided ratings of pain (Outcome: 0-10)
> and
> depressive symptoms (Dep: 0-10) across 5 time points (i.e., baseline, 3m,
> 6m, 9m, 12m). Results indicated a significant (linear) effect of time (B
> =
> -.49, p = .000), indicating that pain decreased linearly over time. There
> was also a significant main effect of depression on pain (B = .23) as well
> as a significant (Time * Dep) interaction (*B = .05,* p = .000). The
> syntax
> is copied below.
>
> I am a bit uncertain about the interpretation of the interaction effect.
> The
> variables are not centered (uncentered). By examining the beta coefficient
> of the interaction, would results suggest that the effect of time on the
> outcome (i.e., linear decrease in pain over time) is more pronounced among
> those who have higher depressive (Dep) symptoms ?
>
> MIXED Pain WITH Time Dep
> /METHOD = REML
> /PRINT = SOLUTION TESTCOV
> /FIXED = Time Dep Time*Dep | SSTYP(3)
> /RANDOM = INTERCEPT | SUBJECT(ID) COVTYPE(UN)
> /REPEATED = Wave | SUBJECT(ID) COVTYPE(AR1).
>
> Thanks in advance for your assistance.
> O.
>
>
>
> --
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--
Bruce Weaver
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Bruce Weaver
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