I am analyzing data from a randomized clinical trial, with 2 intervention groups (placebo and intervention) and repeated measurements over time. I am planning to use linear mixed effects modeling to analyze this longitudinal data and determine whether the intervention causes a change in response over time compared to the control.
More specifically, the outcome variable “six_min_wd” is the walking distance in a standardized walking test (6-minute walking test). I hypothesized that the walking distance will increase in the intervention group over time compared to the control group.
I’ve tested this hypothesis using the following syntax in SPSS:
MIXED six_min_wd BY treatment WITH visit /FIXED=treatment visit treatment*visit /METHOD=ML /PRINT=SOLUTION TESTCOV /RANDOM=INTERCEPT | SUBJECT(id) COVTYPE(UN) /REPEATED=visit | SUBJECT(id) COVTYPE(UN).
“Treatment” is a binary variable for the two intervention groups (0=control, 1=intervention) and “visit” a continuous variable for the three repeated measures (at baseline (0), week 1 (1) and week 8 (8)). A significant interaction term “treatment*visit” would tell me that the two intervention groups significantly differ over time. Are those assumptions correct?
Is the /RANDOM subcommand required in this context? From what I understand the /REPEATED subcommand should suffice?
Secondly, I know that my outcome variable (walking distance) is also affected by other variables, such as age (walking distance expected to decrease with age) or BMI (decrease expected with higher BMI). My approach to controlling for these covariates would be to include those variables as additional terms in the /FIXED subcommand:
MIXED six_min_wd BY treatment WITH visit bmi age /FIXED=treatment visit treatment*visit bmi age /METHOD=ML /PRINT=SOLUTION TESTCOV /RANDOM=INTERCEPT | SUBJECT(id) COVTYPE(UN) /REPEATED=visit | SUBJECT(id) COVTYPE(UN).
Is this the appropriate way to control for these variables?
I spent quite a lot of time reading about mixed effects models, but a review of the actual approach to my situation would be greatly appreciated, since I might miss something and be completely off with my planned analysis. Many thanks.