MMRM structure in R with fixed and random effects

Question

I'm trying to recapitulate a statistical analysis based on a written description of an MMRM used to fit a patient data set.

The description is as follows:

Dependent variable: area (continuous, float)

Independent variables:

1. arm: 2 level factor
2. study_part: 2 level factor
3. m1: 2 level factor
4. m2: 2 level factor
5. m3: 2 level factor
6. visit: 3 level factor | random effect with unstructured correlation
7. Interaction terms between visit and all other factors | random effect

The above needs to be implemented in R, but I'm not as familiar with R syntax.

Currently I've arrived at:

mmrm <- lme(area ~ arm*visit + study_part*vist + m1*visit + m2*visit + m3*visit,
            data = data,
            method = "REML",
            na.action = na.omit,
            random = ~visit | pt_id #pt_id is to indicate random effect to be grouped by patient
            correlation = corSymm(form = ~visit | pt_id),
            weights = varIdent(form = ~1|vist),
            control = lmeControl(msMaxIter = 200, opt = "optim")) #optim seems to be the preffered method, though not the default

My question is 3-fold:

1. With the above structure are visit and the related interaction terms being correctly handled as random effects?

2. My understanding of R is that when stating a covariate of the form arm*visit that the packaged will include terms for arm, visit, and arm*visit, where arm will be a fixed effect and the remaining 2 terms will be random - is this correct?

3. If the above structure is not correct - any suggestions on how to more correctly structure would be helpful

Answer 1

If you would be ok to use fixed instead of random effects for the visit interaction terms, you could use the new mmrm package:

library(mmrm)
fit <- mmrm(
  area ~ arm*visit + study_part*visit + m1*visit + m2*visit + m3*visit + us(visit | pt_id), 
  data = data, 
  method = "Kenward-Roger"
)
summary(fit)

Since only have few levels for each of the factor variables that could be a reasonable approach.

Regarding your question 2, yes this would be correct and also apply in the mmrm package use case.