So, we are doing a linear mixed effects model for analyzing some results of our study. In short, we have performed two different meal tests (i.e., two groups), and measured the response in various biomarkers at baseline as well as 1, 2, 3, and 4 hours after the meal.
I had a meeting with a statistician who explained that we should use linear mixed models for this and as such, using the
nlme package in R the syntax looks like this:
model <-lme(biomarker~ as.factor(group)*visit, random = ~1|ID, data=data, method="ML") summary(model)
The output (abbreviated for readability):
Linear mixed-effects model fit by maximum likelihood Data: data AIC BIC logLik 137.593 149.0651 -62.79649 Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 1.462879 0.6039689 Fixed effects: biomarker ~ as.factor(group) * visit Value Std.Error DF t-value p-value (Intercept) 7.869766 0.7157143 38 10.995681 0.0000 as.factor(group)3 1.295118 1.0121729 8 1.279542 0.2366 visit -0.096024 0.0679003 38 -1.414191 0.1654 as.factor(group)3:visit -0.358905 0.0960255 38 -3.737606 0.0006 Correlation: (Intr) as.()3 visit as.factor(group)3 -0.707 visit -0.247 0.174 as.factor(group)3:visit 0.174 -0.247 -0.707 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -3.107751422 -0.303320567 0.004573801 0.377750437 1.967646127 Number of Observations: 50 Number of Groups: 10
- Exposure = one of two meal tests (group in the syntax)
- Outcome = Biomarker
- Time variable = Visit (5 in total for each participant, continuous)
My questions are:
Am I correct in interpreting this that there is an interaction between group and visit?
I am unclear as to how I should interpret the estimates here. Am I correct in saying that at time = 0, then the group difference (3 vs. 2) is 1.29? And further that this effect depends on the visit? What about the other timepoints?
Is it sufficient to report this model or should we also include a model without the interaction term that is just including group and visit?