#StackBounty: #r #mixed-model Handling baseline differences with a retrospective study and mixed model

Bounty: 150

I am looking at the fixed effects of Var1, Var2, and Var3 on a dependent variable V with a random effect across participant ID using the lmer function in R.

  • Var1 has levels TimePoint1, TimePoint2, and TimePoint3
  • Var2 has levels TreatmentA and TreatmentB
  • Var3 has levels LocationX and LocationY

My mixed model has the following form:

V ~ Var1*Var2*Var3+(1|ID)

which showed a main effect of Var1, Var2, and Var3 with no significant interactions. I thus removed the interactions and repeated the model in the following way:

V ~ Var1+Var2+Var3+(1|ID)

which showed the same main effects.

However, here’s the catch. Factor Var2 refers to treatment type, of which the levels are TreatmentA and TreatmentB. It turns out that upon review of baseline characteristics, I found that the ages are significantly different by t-test between people in group Var2=TreatmentA and people in group Var2=TreatmentB, but only when Var1=LocationX (and not when Var2=LocationY)

Thus, I am thinking I need to include a covariate Age in my original mixed model. However, since Age is only significantly different across the levels of Var2 when Var1=LocationX, how should I structure my mixed model to handle this? I was thinking to just add an interaction term between Age and Var2.

V ~ Var1+Var2+Var3+Var1:Age+(1|ID)

Thoughts? I could also extract out the subset of my data when Var1=LocationX and then make a mixed model just for that data with a covariate of Age. I’m not really sure what the appropriate approach is.


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