In a cohort study, patients have been enrolled for a new method of treatment of a condition. The measurement of interest, for example, tumour size is taken at baseline (BL) ie before treatment, the treatment is then given and the tumour size is then measured every 4 weeks for 40 weeks (ie we have a total of 11 measurements with one BL and the rest post treatment). The analysis of interest is change in tumour size from BL to week 40.
This was recommended by someone else to be done using a paired sample t-test between BL and week 40 and supplement with a p-value and confidence interval. However I feel that using a repeated measures mixed effects model with tumour size as dependant variable and time and possibly the BL value as independent variables (ancova), would be more suitable. This way we utilise all of the data rather than just the two time points.
Please can someone explain to me the benefit of using one method over the other? Can the t-test give a realistic view of the treatment impact whilst ignoring all the middle values?