The ICH E9 R1 addendum on estimands in clinical trials has made big waves in the clinical trial world in the last few years. It aims to provide a framework to think about and define more precisely what exactly the treatment effect(s) of interest is in a clinical trial, in light of what the addendum calls ‘intercurrent events’ (ICEs):
Events occurring after treatment initiation that affect either the interpretation or the existence of the
measurements associated with the clinical question of interest. It is necessary to address intercurrent
events when describing the clinical question of interest in order to precisely define the treatment effect
that is to be estimated.
A couple of weeks ago a really nice paper was published by Harrison and Brummel in the American Statistican which explored the five different ‘strategies’ described in the E9 addendum for handling ICEs in a simple example using potential outcomes. For each strategy they gave an example of an estimand defined using the strategy and a simple estimator for estimating the estimand from the data. In this post, I want to focus on the while on treatment strategy, as I think it’s one area where there is some debate as to what exactly the E9 addendum meant. I of course do not claim to have the definitive answer, but the following is my view.
independent Bernoulli 0/1 draws, where each draw has a probability 
of ‘success’. Does the model rely or assume that for each of these binary observations the success probability is the same? In the third paragraph of this 
=40/100=0.4.