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I'm new to longitudinal analyses, and I'm having trouble formulating a model that accurately reflects my study design. This study recruited subjects for two groups (dx vs. control), with measurements taken for each subject at three different timepoints. Age at baseline varied from subject to subject; from what I've read, this means the design is "unbalanced."

My data frame is organized such that if row n reads:

[subject_ID = x, baseline_age = y, Group = 1, Timepoint = 1, DV = k]

then row n+1 reads:

[subject_ID = x, baseline_age = y, Group = 1, Timepoint = 2, DV = m]

I'm interested in relationships between baseline_age, timepoint, and Group. If baseline_age weren't a factor, I think the R code would be as follows:

mod1 <- lmer(DV ~ timepoint + Group + timepoint:Group + (timepoint|subject_ID), 
             data = mydat) 

where (timepoint | subject_ID) reflects the fact that timepoint varies at the individual level. However, assuming the above is correct, my confusion arises when I try to model random effects with baseline_age entered into the equation. Since baseline_age and subject_ID are perfectly correlated, would it be possible to use baseline_age as proxy for subject_ID in lmer? Or should I model a second random effect? Specifically, I'm considering the following three-way interaction model:

mod2 <- lmer(DV ~ timepoint + Group + baseline_age + timepoint:Group + 
                  timepoint:baseline_age + Group:baseline_age + 
                  timepoint:Group:baseline_age + (timepoint | baseline_age), 
             data = my dat)
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  • $\begingroup$ Why are baseline_age and subject_ID perfectly correlated ? You said that Age at baseline varied from subject to subject.... $\endgroup$ Jul 21, 2016 at 3:01
  • $\begingroup$ They're not, I was thinking about things incorrectly. Rather, Age and Timepoint are highly correlated because T2-T1 and T3-T2 are equivalent across subjects. The crux of my confusion lies with (1) whether baseline_age needs to be modeled as a random effect and (2) if so, how to model it? $\endgroup$ Jul 21, 2016 at 14:39
  • $\begingroup$ But age at baseline does not vary with time. It is a subject-level variable and should be constant for each subject_ID , so it can't be collinear with timepoint. Please can you explain your data a little more ? If you could explain the data a little more it would help to formulate an answer. $\endgroup$ Jul 21, 2016 at 15:27
  • $\begingroup$ So sorry for the confusion. Age and baseline_age are two different variables -- baseline_age is a subject-level variable and is constant for each subject ID, whereas Age is collinear with timepoint, incrementing between Timepoint 1 and Timepoint 2 and again between Timepoint 2 and Timepoint 3. I didn't include Age in the model because I was worried about issues of multicollinearity and thought the effect of age could be captured equally well by baseline_age. $\endgroup$ Jul 21, 2016 at 15:50
  • $\begingroup$ Thank you so much for your thorough, thoughtful response -- really helped clarify my understanding of linear mixed effect modeling, not only for the specific model in question but also longitudinal models more generally. $\endgroup$ Jul 22, 2016 at 14:37

1 Answer 1

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Your second model does not make sense because observations are not grouped/clustered/nested within baseline_age.

The first model does make sense because observations are grouped/nested/clustered within subject_ID (because you have repeated measures). There is no further clustering as far as I can gather from the description.

So, a good initial model would be

mod2 <- lmer(DV ~ timepoint + Group + baseline_age + (1 | subject_ID), 
         data = mydat)

The coefficient for timepoint will provide the linear growth estimate(s) (whether it is coded as a factor or numeric matters, see below), the coefficient for Group will give the treatment effect, while controlling for differential baseline ages, while the random intercept for subject_ID will allow each subject's intercept (at the study inception point) to vary. You could include interactions of the fixed effects, at this stage, if this makes sense to your research question.

Subsequent to this, you might want to include one or more of the fixed effects variables as random coefficients (slopes) if the effects of these are thought to vary between subject, such as timepoint that you mention. Note that this is not the same as your statement in the question: "where (timepoint | subject_ID) reflects the fact that timepoint varies at the individual level". Obviously timepoint varies at the individual level, because you have repeated measures. The random intercept deals with this, but it assumes that the slope (coefficient) for it is the same for each subject. If you have reason to believe that the slopes should vary between subjects then you can include one or more on the left side of the | symbol). However, if timepoint is a factor then this will result in a separate random effect for each level, which will increase the computational burden an possibly cause numerical problems (that is, assuming you have enough observations to make such a model identifiable to begin with), as well as making the model interpretation more complex.

Also note that if timepoint is a factor then you will get a seperate fixed effect estimate for each level too, which may or may not be what you want (in my experience it is not usually what you want unless the number of levels is small). So, if it isn't already, you might also want to consider coding timepoint as a numeric variable, this will then give you a single fixed main effect. This will model linear growth in your outcome/response, giving each subject their own intercept. If you also add timepoint as a random slope, then you can allow each subject to have their own slope. If you want to cater for non-linear growth then you could add a quadratic variable for timepoint (centering it first to avoid collinearity).

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