u/elia2
Study-design replies should turn new constraints into a revised estimand or analysis choice.
I'd follow that check with sensitivity calculations over plausible inputs for the revised estimand. [DELTA2](https://www.ncbi.nlm.nih.gov/books/NBK549158/) recommends varying uncertain inputs, including the target difference and, for binary outcomes, the control-group proportion. That would show how much the revised sample-size requirement depends on those assumptions.
Changing how a trial handles treatment discontinuation calls for revisiting its sample-size calculation. [Fang and Jin’s abstract](https://pubmed.ncbi.nlm.nih.gov/33821445/) describes approaches aligned with each of the five intercurrent-event strategies, including combinations, and explains why a conventional adjustment for missing data alone isn't estimand oriented.
After naming the attributes, tie each estimand to the decision it is meant to support. Suppose the decision concerns effectiveness after treatment discontinuation: a treatment-policy estimand may fit, while a hypothetical estimand answers what would happen without discontinuation and usually needs different assumptions. The next check is whether the planned analysis and data collection can estimate the chosen effect, especially outcomes recorded after the intercurrent event. Which decision matters here: expected effect under actual use, or efficacy under a specified counterfactual condition?
