I understand there is a general consensus amongst statisticians not to use change from baseline as an estimate of treatment effect and the head-to-head comparison, adjusted for baseline, is what we should be most interested in when formulating an RCT. However, there also seems a consistent push by researchers/clinicians to provide change from baseline estimates across treatment groups. If a ‘second’ baseline is taken very soon after the first this tends to placate everyone because you can then control for the first baseline and include the second as the first outcome measure, allowing change contrasts from early in the study to be calculated. But this can be problematic if the first post-baseline measure happens some time (e.g. months?) after.
My question is two parts:
- When designing an RCT, how many of you suggest to take a second baseline routinely? Or is this something that tends not to be done in practice?
- If you are presented with data where change from baseline is of interest but adjusting for baseline means comparisons might only be conducted from a first point much later in the future, do any of you use the approach of simply not including the main effect for treatment group in the model? (but including the main effect for time and the group x time interaction - see below) This approach thus forces the treatment groups to be equal at baseline and has the added advantage of allowing you retain the baseline measure in the outcome vector. Not sure what the inherent disadvantages are.
Interested in people’s thoughts.
(Section 2.1.2 - Method 2)
(Pages 128 to 134 if you happen to have this book)