Comparing slopes for serial measurements in the presence of missing data

Posted by Frank Furter on Nov 05, 2016; 12:52pm
URL: http://spssx-discussion.165.s1.nabble.com/Comparing-slopes-for-serial-measurements-in-the-presence-of-missing-data-tp5733425.html

We are planning to investigate whether a new treatment alters the progression of a disease over time. We will have two groups of patients randomized to standard of care or the new treatment, and there will be monthly measurements of some continuous outcome over a period of 12 months. According to regulatory guidance, efficacy of a new treatment should preferrably be demonstrated by showing that the decline slopes for the outcome of interest diverge over time.

Since some patients will likely drop out before the scheduled end of the period of observation, and we need to include all randomized patients with any post-baseline data into the analysis, I was originally thinking of using MMRM for alanyzing the data. However, since MMRM treats time as categorical, this may not be the optimal method for comparing slopes.

What would be a more efficient method for comparing slopes while appropriately accounting for missing data - and (how) can this be done in SPSS?

Thanks, Andreas