I want to compare data from a treatment group vs a control group over time (baseline, 1 yr, 2 yr).
The measurements are repeated for all subjects across all time points (i.e., all participants complete the same set of questionnaires at all time points).
Some participants have missing data (some did not complete questionnaires at certain time points).
Control group has more subjects than treatment group (unequal sample sizes).
I am not sure whether I should use mixed models analyses to handle missing data because I am not sure whether mixed models can be used for unequal sample sizes. Or should I use Scheffe's method (non-parametric variant of anova) to handle the unequal sample sizes.
I am interested to investigate whether scores on a questionnaire (that measures severity of symptoms) differ between the treatment group (given medication) and the control group (not on medication) across the different time points.
I was thinking of using repeated measures mixed model analysis (specified as 'Linear Mixed Models analysis' in SPSS which involves selecting the appropriate covariance structure [e.g.compound symmetry and 1st order auto regressive]) because all subjects undergo the same questionnaire across all time points and also due to incomplete data from some participants.
The only issue is that I have 300+ participants in the control group and 30+ participants in the treatment group. (very unequal group sizes).
In this case, will the repeated measures mixed model analysis still be appropriate?
I agree with Peter that performing multiple t-tests will not be advisable in this case as it also increases family-wise error (hence increases tendency of committing a type 1 error).
Please kindly clarify. Thanks!