Intention-to-treat with drop-out

We consider the problem of an ITT analysis in a randomized clinical
trial. Due to (study) drop-outs, standard methods are not
applicable and simple imputation methods like LOCF (last
observation carried forward) may lead to biased results. Since a
patient who drops out of the study often will also change or drop
the assigned treatment, an "ignorable" analysis in the sense of
Rubin (1976) assuming MAR (missing at random), as e.g. a propensity
weighted analysis or a likelihood based MAR-analysis is not valid.
This is due to the fact that information is missing about outcomes
as well as the covariate treatment after drop-out. That is, even if
the drop-out process itself is ignorable, we can not treat the
problem as ignorable because of the missing covariate information.
We follow the path given by Little and Yau (1996), who created
multiple imputations under various assumptions about the actual
treatment after drop-out, and conduct a simulation study on the
alpha-error and power of simple endpoint tests. This should also
shed light onto the problem whether the true treatment effect can
be sensibly bracketed by assumptions like zero dose or continuing
dose after drop-out.